From aed7e2dd0f752a9216acf949c2018c2585eb0fa1 Mon Sep 17 00:00:00 2001 From: Khanan Grauer Date: Mon, 10 Mar 2025 21:49:44 -0400 Subject: [PATCH 1/3] Studying the data --- README.md | 119 +- analysis.ipynb | 43 - dme_analysis.ipynb | 2205 ++++++++++++++++++++++++- dme_analysis/__init__.py | 8 + dme_analysis/utils/__init__.py | 17 + dme_analysis/utils/data_dictionary.py | 153 ++ dme_analysis/utils/data_import.py | 155 ++ dme_data_analysis.py | 195 +-- dme_notebook_example.ipynb | 1 - fraud_detector.py | 311 ++++ 10 files changed, 2919 insertions(+), 288 deletions(-) delete mode 100644 analysis.ipynb create mode 100644 dme_analysis/__init__.py create mode 100644 dme_analysis/utils/__init__.py create mode 100644 dme_analysis/utils/data_dictionary.py create mode 100644 dme_analysis/utils/data_import.py delete mode 100644 dme_notebook_example.ipynb create mode 100644 fraud_detector.py diff --git a/README.md b/README.md index 5c212eb..548a149 100644 --- a/README.md +++ b/README.md @@ -1,83 +1,90 @@ -# Data Exploration Project +# Medicare DME Data Analysis -This project provides a structured environment for data exploration and analysis using Jupyter notebooks. +This repository contains scripts for analyzing Medicare Durable Medical Equipment (DME) data from 2017-2022. -## Getting Started +## Directory Structure -### Prerequisites +``` +. +├── dme_analysis/ # Main package +│ ├── __init__.py # Package initialization +│ ├── utils/ # Utility modules +│ │ ├── __init__.py # Subpackage initialization +│ │ ├── data_dictionary.py # Data dictionary and column categorization +│ │ └── data_import.py # Data import functions +│ └── ... # Analysis modules +├── dme_data_analysis.py # Main analysis script +├── fraud_detector.py # Fraud detection script +└── ... # Data files and other scripts +``` -- Python 3.8 or higher -- pip (Python package installer) +## Usage -### Setup Instructions +### Importing Data -1. **Clone or download this repository** +You can import the DME data using the utility functions: -2. **Create a virtual environment (recommended)** +```python +from dme_analysis.utils import import_data_for_years - ```bash - # On macOS/Linux - python3 -m venv venv - source venv/bin/activate +# Import data for years 2017-2022 +df_by_year = import_data_for_years(range(2017, 2023)) +``` - # On Windows - python -m venv venv - venv\Scripts\activate - ``` +### Data Dictionary -3. **Install the required packages** +The data dictionary contains descriptions for all columns in the dataset: - ```bash - pip install -r requirements.txt - ``` +```python +from dme_analysis.utils import DATA_DICTIONARY -4. **Launch Jupyter Notebook** +# Get the description of a column +print(DATA_DICTIONARY['DME_Tot_Suplr_Benes']) +``` - ```bash - jupyter notebook - ``` +### Analyzing the Data -5. **Open the data_exploration.ipynb notebook** - - A browser window should open automatically. If not, copy the URL displayed in the terminal and paste it into your web browser. - - Navigate to and click on `data_exploration.ipynb` to open the notebook. +The main analysis script can be run directly: -## Project Structure +```bash +python dme_data_analysis.py +``` -- `data_exploration.ipynb`: Starter Jupyter notebook for data analysis -- `requirements.txt`: Contains all the Python dependencies -- `data/`: Directory where you can store your datasets (create this as needed) +Or imported in a Jupyter notebook: -## Adding Your Data +```python +import dme_data_analysis as dme +%matplotlib inline -You can add your data files to the project: +# Run the analysis +df_by_year, visualizations = dme.main() -1. Create a `data` directory (if not already present): +# Display visualizations +visualizations['spending_trends'] +``` - ```bash - mkdir data - ``` +### Fraud Detection -2. Place your data files (CSV, Excel, etc.) in the `data` directory. +The fraud detection script can be used to identify potential fraud patterns: -3. In the notebook, load your data: - ```python - df = pd.read_csv('data/your_file.csv') - ``` +```python +import fraud_detector as fd +%matplotlib inline -## Common Tasks +# Run the fraud detection analysis +df_by_year, visualizations, data = fd.main() -- **Data Loading**: Use pandas to read different file formats (CSV, Excel, JSON, etc.) -- **Data Cleaning**: Handle missing values, duplicates, outliers -- **Exploratory Analysis**: Descriptive statistics, correlation analysis -- **Data Visualization**: Create charts and plots using matplotlib and seaborn -- **Feature Engineering**: Create new features or transform existing ones -- **Model Building**: Build and evaluate machine learning models using scikit-learn +# Display fraud indicators +visualizations['high_growth_suppliers'] +``` -## Useful Extensions +## Column Descriptions -Consider installing these additional Jupyter extensions for enhanced productivity: +The DME dataset contains the following types of columns: -```bash -pip install jupyterlab # JupyterLab interface -pip install nbextensions # Notebook extensions -``` +1. Supplier Information (e.g., `Suplr_NPI`, `Suplr_Prvdr_Last_Name_Org`) +2. DME-specific fields (e.g., `DME_Tot_Suplr_Benes`, `DME_Suplr_Mdcr_Pymt_Amt`) +3. Prosthetic and Orthotic fields (e.g., `POS_Tot_Suplr_Benes`, `POS_Suplr_Mdcr_Pymt_Amt`) +4. Drug and Nutritional fields (e.g., `Drug_Tot_Suplr_Benes`, `Drug_Suplr_Mdcr_Pymt_Amt`) +5. Beneficiary Demographics (e.g., `Bene_Avg_Age`, `Bene_Feml_Cnt`) +6. Health Conditions (e.g., `Bene_CC_PH_Hypertension_V2_Pct`, `Bene_CC_BH_Mood_V2_Pct`) diff --git a/analysis.ipynb b/analysis.ipynb deleted file mode 100644 index 9638289..0000000 --- a/analysis.ipynb +++ /dev/null @@ -1,43 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "cad336dc-cdc4-4dc0-8a6a-e4df4cacea45", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello World\n" - ] - } - ], - "source": [ - "print(\"Hello World\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dme_analysis.ipynb b/dme_analysis.ipynb index 9638289..1649cd2 100644 --- a/dme_analysis.ipynb +++ b/dme_analysis.ipynb @@ -2,26 +2,2221 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "id": "cad336dc-cdc4-4dc0-8a6a-e4df4cacea45", + "execution_count": 2, + "id": "76a4490c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hello World\n" + "DME Fraud Detection Analysis\n", + "===========================\n", + "\n", + "Importing data for 2017...\n", + "✓ Data for 2017 imported successfully. Shape: (75343, 94)\n", + "Importing data for 2018...\n", + "✓ Data for 2018 imported successfully. Shape: (75805, 94)\n", + "Importing data for 2019...\n", + "✓ Data for 2019 imported successfully. Shape: (72775, 94)\n", + "Importing data for 2020...\n", + "✓ Data for 2020 imported successfully. Shape: (69398, 94)\n", + "Importing data for 2021...\n", + "✓ Data for 2021 imported successfully. Shape: (68227, 94)\n", + "Importing data for 2022...\n", + "✓ Data for 2022 imported successfully. Shape: (66406, 94)\n", + "\n", + "6 year(s) of data imported.\n", + "\n", + "Sample column names from 2017 data:\n", + " 1. Bene_Age_65_74_Cnt\n", + " 2. Bene_Age_75_84_Cnt\n", + " 3. Bene_Age_GT_84_Cnt\n", + " 4. Bene_Age_LT_65_Cnt\n", + " 5. Bene_Avg_Age\n", + " 6. Bene_Avg_Risk_Scre\n", + " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", + " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", + " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", + " 10. Bene_CC_BH_Anxiety_V1_Pct\n", + " 11. Bene_CC_BH_Bipolar_V1_Pct\n", + " 12. Bene_CC_BH_Depress_V1_Pct\n", + " 13. Bene_CC_BH_Mood_V2_Pct\n", + " 14. Bene_CC_BH_PD_V1_Pct\n", + " 15. Bene_CC_BH_PTSD_V1_Pct\n", + " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", + " 17. Bene_CC_BH_Tobacco_V1_Pct\n", + " 18. Bene_CC_PH_Afib_V2_Pct\n", + " 19. Bene_CC_PH_Arthritis_V2_Pct\n", + " 20. Bene_CC_PH_Asthma_V2_Pct\n", + " ... and 74 more columns\n", + "\n", + "1. High Growth Rate Analysis\n", + "--------------------------\n", + "\n", + "Identifying suppliers with highest year-over-year growth rates...\n", + "Warning: No supplier name column found. Using placeholder names.\n", + "\n", + "Available columns in the dataset:\n", + " 1. Bene_Age_65_74_Cnt\n", + " 2. Bene_Age_75_84_Cnt\n", + " 3. Bene_Age_GT_84_Cnt\n", + " 4. Bene_Age_LT_65_Cnt\n", + " 5. Bene_Avg_Age\n", + " 6. Bene_Avg_Risk_Scre\n", + " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", + " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", + " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", + " 10. Bene_CC_BH_Anxiety_V1_Pct\n", + " 11. Bene_CC_BH_Bipolar_V1_Pct\n", + " 12. Bene_CC_BH_Depress_V1_Pct\n", + " 13. Bene_CC_BH_Mood_V2_Pct\n", + " 14. Bene_CC_BH_PD_V1_Pct\n", + " 15. Bene_CC_BH_PTSD_V1_Pct\n", + " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", + " 17. Bene_CC_BH_Tobacco_V1_Pct\n", + " 18. Bene_CC_PH_Afib_V2_Pct\n", + " 19. Bene_CC_PH_Arthritis_V2_Pct\n", + " 20. Bene_CC_PH_Asthma_V2_Pct\n", + " 21. Bene_CC_PH_CKD_V2_Pct\n", + " 22. Bene_CC_PH_COPD_V2_Pct\n", + " 23. Bene_CC_PH_Cancer6_V2_Pct\n", + " 24. Bene_CC_PH_Diabetes_V2_Pct\n", + " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", + " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", + " 27. Bene_CC_PH_Hypertension_V2_Pct\n", + " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", + " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", + " 30. Bene_CC_PH_Parkinson_V2_Pct\n", + " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", + " 32. Bene_Dual_Cnt\n", + " 33. Bene_Feml_Cnt\n", + " 34. Bene_Male_Cnt\n", + " 35. Bene_Ndual_Cnt\n", + " 36. Bene_Race_Api_Cnt\n", + " 37. Bene_Race_Black_Cnt\n", + " 38. Bene_Race_Hspnc_Cnt\n", + " 39. Bene_Race_Natind_Cnt\n", + " 40. Bene_Race_Othr_Cnt\n", + " 41. Bene_Race_Wht_Cnt\n", + " 42. DME_Sprsn_Ind\n", + " 43. DME_Suplr_Mdcr_Alowd_Amt\n", + " 44. DME_Suplr_Mdcr_Pymt_Amt\n", + " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 46. DME_Suplr_Sbmtd_Chrgs\n", + " 47. DME_Tot_Suplr_Benes\n", + " 48. DME_Tot_Suplr_Clms\n", + " 49. DME_Tot_Suplr_HCPCS_Cds\n", + " 50. DME_Tot_Suplr_Srvcs\n", + " 51. Drug_Sprsn_Ind\n", + " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", + " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", + " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 55. Drug_Suplr_Sbmtd_Chrgs\n", + " 56. Drug_Tot_Suplr_Benes\n", + " 57. Drug_Tot_Suplr_Clms\n", + " 58. Drug_Tot_Suplr_HCPCS_Cds\n", + " 59. Drug_Tot_Suplr_Srvcs\n", + " 60. POS_Sprsn_Ind\n", + " 61. POS_Suplr_Mdcr_Alowd_Amt\n", + " 62. POS_Suplr_Mdcr_Pymt_Amt\n", + " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 64. POS_Suplr_Sbmtd_Chrgs\n", + " 65. POS_Tot_Suplr_Benes\n", + " 66. POS_Tot_Suplr_Clms\n", + " 67. POS_Tot_Suplr_HCPCS_Cds\n", + " 68. POS_Tot_Suplr_Srvcs\n", + " 69. Suplr_Mdcr_Alowd_Amt\n", + " 70. Suplr_Mdcr_Pymt_Amt\n", + " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 72. Suplr_NPI\n", + " 73. Suplr_Prvdr_City\n", + " 74. Suplr_Prvdr_Cntry\n", + " 75. Suplr_Prvdr_Crdntls\n", + " 76. Suplr_Prvdr_Ent_Cd\n", + " 77. Suplr_Prvdr_First_Name\n", + " 78. Suplr_Prvdr_Gndr\n", + " 79. Suplr_Prvdr_Last_Name_Org\n", + " 80. Suplr_Prvdr_MI\n", + " 81. Suplr_Prvdr_RUCA\n", + " 82. Suplr_Prvdr_RUCA_Desc\n", + " 83. Suplr_Prvdr_Spclty_Desc\n", + " 84. Suplr_Prvdr_Spclty_Srce\n", + " 85. Suplr_Prvdr_St1\n", + " 86. Suplr_Prvdr_St2\n", + " 87. Suplr_Prvdr_State_Abrvtn\n", + " 88. Suplr_Prvdr_State_FIPS\n", + " 89. Suplr_Prvdr_Zip5\n", + " 90. Suplr_Sbmtd_Chrgs\n", + " 91. Tot_Suplr_Benes\n", + " 92. Tot_Suplr_Clms\n", + " 93. Tot_Suplr_HCPCS_Cds\n", + " 94. Tot_Suplr_Srvcs\n", + "\n", + "Please adjust the script to use the correct column names for your dataset.\n", + "Top 15 suppliers with highest growth rates:\n", + " Supplier NPI Supplier Name Supplier State Year Period Start Year Value End Year Value Growth Rate (%) Absolute Growth\n", + " 1437771474.0 Supplier 1437771474.0 TX 2021-2022 $1,019.39 $9,748,100.46 956168.01% $9,747,081.07\n", + " 1851421663.0 Supplier 1851421663.0 NC 2018-2019 $645.96 $908,231.92 140501.88% $907,585.96\n", + " 1235190232.0 Supplier 1235190232.0 KY 2021-2022 $10,705.50 $9,922,745.68 92588.30% $9,912,040.18\n", + " 1558553040.0 Supplier 1558553040.0 TX 2017-2018 $226.62 $191,169.55 84256.87% $190,942.93\n", + " 1235754094.0 Supplier 1235754094.0 TN 2021-2022 $7,661.90 $6,030,413.89 78606.51% $6,022,751.99\n", + " 1962081679.0 Supplier 1962081679.0 FL 2021-2022 $1,716.12 $1,084,887.33 63117.45% $1,083,171.21\n", + " 1881972040.0 Supplier 1881972040.0 FL 2018-2019 $713.12 $442,124.30 61898.58% $441,411.18\n", + " 1063967768.0 Supplier 1063967768.0 MS 2021-2022 $26,062.96 $15,797,494.45 60512.82% $15,771,431.49\n", + " 1740458694.0 Supplier 1740458694.0 OH 2017-2018 $3.48 $2,034.40 58359.77% $2,030.92\n", + " 1861847824.0 Supplier 1861847824.0 PA 2021-2022 $737.30 $409,565.31 55449.34% $408,828.01\n", + " 1295801421.0 Supplier 1295801421.0 CA 2017-2018 $1,620.57 $812,890.78 50060.79% $811,270.21\n", + " 1316438849.0 Supplier 1316438849.0 OH 2018-2019 $317.81 $158,292.95 49707.42% $157,975.14\n", + " 1952948002.0 Supplier 1952948002.0 KY 2021-2022 $108,739.54 $50,139,168.76 46009.42% $50,030,429.22\n", + " 1891275590.0 Supplier 1891275590.0 CT 2018-2019 $8,142.60 $3,539,476.72 43368.63% $3,531,334.12\n", + " 1043627060.0 Supplier 1043627060.0 CA 2017-2018 $2,063.37 $784,360.94 37913.59% $782,297.57\n", + "\n", + "2. Geographic Fraud Hotspots\n", + "-------------------------\n", + "\n", + "States with highest number of suspicious suppliers:\n", + "State Suspicious Suppliers Average Growth Rate (%) Total Growth ($)\n", + " FL 8 27795.87% $19,559,249.39\n", + " TX 6 187306.97% $27,882,045.41\n", + " WA 4 17343.04% $1,585,521.59\n", + " OH 4 34423.24% $318,671.52\n", + " CA 4 30767.56% $1,782,205.08\n", + " MI 2 20004.69% $406,913.44\n", + " TN 2 46639.87% $6,108,188.37\n", + " PA 2 36701.58% $549,356.09\n", + " IN 2 24899.44% $368,638.47\n", + " KY 2 69298.86% $59,942,469.40\n", + "\n", + "3. Outlier Claim Amount Analysis\n", + "-----------------------------\n", + "\n", + "Identifying suppliers with abnormally high average claim amounts in 2022...\n", + "Warning: No supplier name column found. Using placeholder names.\n", + "\n", + "Available columns in the dataset:\n", + " 1. Bene_Age_65_74_Cnt\n", + " 2. Bene_Age_75_84_Cnt\n", + " 3. Bene_Age_GT_84_Cnt\n", + " 4. Bene_Age_LT_65_Cnt\n", + " 5. Bene_Avg_Age\n", + " 6. Bene_Avg_Risk_Scre\n", + " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", + " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", + " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", + " 10. Bene_CC_BH_Anxiety_V1_Pct\n", + " 11. Bene_CC_BH_Bipolar_V1_Pct\n", + " 12. Bene_CC_BH_Depress_V1_Pct\n", + " 13. Bene_CC_BH_Mood_V2_Pct\n", + " 14. Bene_CC_BH_PD_V1_Pct\n", + " 15. Bene_CC_BH_PTSD_V1_Pct\n", + " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", + " 17. Bene_CC_BH_Tobacco_V1_Pct\n", + " 18. Bene_CC_PH_Afib_V2_Pct\n", + " 19. Bene_CC_PH_Arthritis_V2_Pct\n", + " 20. Bene_CC_PH_Asthma_V2_Pct\n", + " 21. Bene_CC_PH_CKD_V2_Pct\n", + " 22. Bene_CC_PH_COPD_V2_Pct\n", + " 23. Bene_CC_PH_Cancer6_V2_Pct\n", + " 24. Bene_CC_PH_Diabetes_V2_Pct\n", + " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", + " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", + " 27. Bene_CC_PH_Hypertension_V2_Pct\n", + " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", + " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", + " 30. Bene_CC_PH_Parkinson_V2_Pct\n", + " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", + " 32. Bene_Dual_Cnt\n", + " 33. Bene_Feml_Cnt\n", + " 34. Bene_Male_Cnt\n", + " 35. Bene_Ndual_Cnt\n", + " 36. Bene_Race_Api_Cnt\n", + " 37. Bene_Race_Black_Cnt\n", + " 38. Bene_Race_Hspnc_Cnt\n", + " 39. Bene_Race_Natind_Cnt\n", + " 40. Bene_Race_Othr_Cnt\n", + " 41. Bene_Race_Wht_Cnt\n", + " 42. DME_Sprsn_Ind\n", + " 43. DME_Suplr_Mdcr_Alowd_Amt\n", + " 44. DME_Suplr_Mdcr_Pymt_Amt\n", + " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 46. DME_Suplr_Sbmtd_Chrgs\n", + " 47. DME_Tot_Suplr_Benes\n", + " 48. DME_Tot_Suplr_Clms\n", + " 49. DME_Tot_Suplr_HCPCS_Cds\n", + " 50. DME_Tot_Suplr_Srvcs\n", + " 51. Drug_Sprsn_Ind\n", + " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", + " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", + " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 55. Drug_Suplr_Sbmtd_Chrgs\n", + " 56. Drug_Tot_Suplr_Benes\n", + " 57. Drug_Tot_Suplr_Clms\n", + " 58. Drug_Tot_Suplr_HCPCS_Cds\n", + " 59. Drug_Tot_Suplr_Srvcs\n", + " 60. POS_Sprsn_Ind\n", + " 61. POS_Suplr_Mdcr_Alowd_Amt\n", + " 62. POS_Suplr_Mdcr_Pymt_Amt\n", + " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 64. POS_Suplr_Sbmtd_Chrgs\n", + " 65. POS_Tot_Suplr_Benes\n", + " 66. POS_Tot_Suplr_Clms\n", + " 67. POS_Tot_Suplr_HCPCS_Cds\n", + " 68. POS_Tot_Suplr_Srvcs\n", + " 69. Suplr_Mdcr_Alowd_Amt\n", + " 70. Suplr_Mdcr_Pymt_Amt\n", + " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 72. Suplr_NPI\n", + " 73. Suplr_Prvdr_City\n", + " 74. Suplr_Prvdr_Cntry\n", + " 75. Suplr_Prvdr_Crdntls\n", + " 76. Suplr_Prvdr_Ent_Cd\n", + " 77. Suplr_Prvdr_First_Name\n", + " 78. Suplr_Prvdr_Gndr\n", + " 79. Suplr_Prvdr_Last_Name_Org\n", + " 80. Suplr_Prvdr_MI\n", + " 81. Suplr_Prvdr_RUCA\n", + " 82. Suplr_Prvdr_RUCA_Desc\n", + " 83. Suplr_Prvdr_Spclty_Desc\n", + " 84. Suplr_Prvdr_Spclty_Srce\n", + " 85. Suplr_Prvdr_St1\n", + " 86. Suplr_Prvdr_St2\n", + " 87. Suplr_Prvdr_State_Abrvtn\n", + " 88. Suplr_Prvdr_State_FIPS\n", + " 89. Suplr_Prvdr_Zip5\n", + " 90. Suplr_Sbmtd_Chrgs\n", + " 91. Tot_Suplr_Benes\n", + " 92. Tot_Suplr_Clms\n", + " 93. Tot_Suplr_HCPCS_Cds\n", + " 94. Tot_Suplr_Srvcs\n", + "\n", + "Please adjust the script to use the correct column names for your dataset.\n", + "Error: No average charge metrics found in data for year 2022.\n", + "No suppliers with outlier claim amounts detected in 2022.\n", + "\n", + "4. Unusual Beneficiary-to-Claim Ratio Analysis\n", + "-----------------------------------------\n", + "\n", + "Identifying suppliers with unusual claims per beneficiary in 2022...\n", + "Warning: No supplier name column found. Using placeholder names.\n", + "\n", + "Available columns in the dataset:\n", + " 1. Bene_Age_65_74_Cnt\n", + " 2. Bene_Age_75_84_Cnt\n", + " 3. Bene_Age_GT_84_Cnt\n", + " 4. Bene_Age_LT_65_Cnt\n", + " 5. Bene_Avg_Age\n", + " 6. Bene_Avg_Risk_Scre\n", + " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", + " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", + " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", + " 10. Bene_CC_BH_Anxiety_V1_Pct\n", + " 11. Bene_CC_BH_Bipolar_V1_Pct\n", + " 12. Bene_CC_BH_Depress_V1_Pct\n", + " 13. Bene_CC_BH_Mood_V2_Pct\n", + " 14. Bene_CC_BH_PD_V1_Pct\n", + " 15. Bene_CC_BH_PTSD_V1_Pct\n", + " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", + " 17. Bene_CC_BH_Tobacco_V1_Pct\n", + " 18. Bene_CC_PH_Afib_V2_Pct\n", + " 19. Bene_CC_PH_Arthritis_V2_Pct\n", + " 20. Bene_CC_PH_Asthma_V2_Pct\n", + " 21. Bene_CC_PH_CKD_V2_Pct\n", + " 22. Bene_CC_PH_COPD_V2_Pct\n", + " 23. Bene_CC_PH_Cancer6_V2_Pct\n", + " 24. Bene_CC_PH_Diabetes_V2_Pct\n", + " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", + " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", + " 27. Bene_CC_PH_Hypertension_V2_Pct\n", + " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", + " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", + " 30. Bene_CC_PH_Parkinson_V2_Pct\n", + " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", + " 32. Bene_Dual_Cnt\n", + " 33. Bene_Feml_Cnt\n", + " 34. Bene_Male_Cnt\n", + " 35. Bene_Ndual_Cnt\n", + " 36. Bene_Race_Api_Cnt\n", + " 37. Bene_Race_Black_Cnt\n", + " 38. Bene_Race_Hspnc_Cnt\n", + " 39. Bene_Race_Natind_Cnt\n", + " 40. Bene_Race_Othr_Cnt\n", + " 41. Bene_Race_Wht_Cnt\n", + " 42. DME_Sprsn_Ind\n", + " 43. DME_Suplr_Mdcr_Alowd_Amt\n", + " 44. DME_Suplr_Mdcr_Pymt_Amt\n", + " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 46. DME_Suplr_Sbmtd_Chrgs\n", + " 47. DME_Tot_Suplr_Benes\n", + " 48. DME_Tot_Suplr_Clms\n", + " 49. DME_Tot_Suplr_HCPCS_Cds\n", + " 50. DME_Tot_Suplr_Srvcs\n", + " 51. Drug_Sprsn_Ind\n", + " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", + " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", + " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 55. Drug_Suplr_Sbmtd_Chrgs\n", + " 56. Drug_Tot_Suplr_Benes\n", + " 57. Drug_Tot_Suplr_Clms\n", + " 58. Drug_Tot_Suplr_HCPCS_Cds\n", + " 59. Drug_Tot_Suplr_Srvcs\n", + " 60. POS_Sprsn_Ind\n", + " 61. POS_Suplr_Mdcr_Alowd_Amt\n", + " 62. POS_Suplr_Mdcr_Pymt_Amt\n", + " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 64. POS_Suplr_Sbmtd_Chrgs\n", + " 65. POS_Tot_Suplr_Benes\n", + " 66. POS_Tot_Suplr_Clms\n", + " 67. POS_Tot_Suplr_HCPCS_Cds\n", + " 68. POS_Tot_Suplr_Srvcs\n", + " 69. Suplr_Mdcr_Alowd_Amt\n", + " 70. Suplr_Mdcr_Pymt_Amt\n", + " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 72. Suplr_NPI\n", + " 73. Suplr_Prvdr_City\n", + " 74. Suplr_Prvdr_Cntry\n", + " 75. Suplr_Prvdr_Crdntls\n", + " 76. Suplr_Prvdr_Ent_Cd\n", + " 77. Suplr_Prvdr_First_Name\n", + " 78. Suplr_Prvdr_Gndr\n", + " 79. Suplr_Prvdr_Last_Name_Org\n", + " 80. Suplr_Prvdr_MI\n", + " 81. Suplr_Prvdr_RUCA\n", + " 82. Suplr_Prvdr_RUCA_Desc\n", + " 83. Suplr_Prvdr_Spclty_Desc\n", + " 84. Suplr_Prvdr_Spclty_Srce\n", + " 85. Suplr_Prvdr_St1\n", + " 86. Suplr_Prvdr_St2\n", + " 87. Suplr_Prvdr_State_Abrvtn\n", + " 88. Suplr_Prvdr_State_FIPS\n", + " 89. Suplr_Prvdr_Zip5\n", + " 90. Suplr_Sbmtd_Chrgs\n", + " 91. Tot_Suplr_Benes\n", + " 92. Tot_Suplr_Clms\n", + " 93. Tot_Suplr_HCPCS_Cds\n", + " 94. Tot_Suplr_Srvcs\n", + "\n", + "Please adjust the script to use the correct column names for your dataset.\n", + "Top 10 suppliers with unusual claims per beneficiary in 2022:\n", + " Suplr_NPI Suplr_Prvdr_Org_Name Suplr_Prvdr_State_Abrvtn DME_Tot_Suplr_Benes DME_Tot_Suplr_Clms Claims_Per_Beneficiary Claims_Per_Beneficiary_zscore\n", + "1902023013 Supplier 1902023013 TX 18.0 828.0 46.00 29.92\n", + "1477647337 Supplier 1477647337 NJ 13.0 464.0 35.69 22.67\n", + "1912199381 Supplier 1912199381 TX 59.0 1771.0 30.02 18.68\n", + "1043656614 Supplier 1043656614 FL 12.0 324.0 27.00 16.56\n", + "1316924061 Supplier 1316924061 MI 13.0 333.0 25.62 15.58\n", + "1942216577 Supplier 1942216577 CA 13.0 313.0 24.08 14.50\n", + "1437649456 Supplier 1437649456 NJ 24.0 538.0 22.42 13.33\n", + "1891736286 Supplier 1891736286 CA 57.0 1260.0 22.11 13.11\n", + "1952312456 Supplier 1952312456 IL 18.0 373.0 20.72 12.14\n", + "1053412643 Supplier 1053412643 NJ 13.0 268.0 20.62 12.07\n", + "\n", + "5. Combined Fraud Indicators\n", + "-------------------------\n", + "\n", + "Identifying suppliers with multiple fraud indicators...\n", + "Warning: One or more fraud indicator dataframes are empty. Cannot perform combined analysis.\n", + "No suppliers with multiple fraud indicators identified.\n", + "\n", + "\n", + "6. Generating Fraud Detection Visualizations\n", + "------------------------------------------\n", + "\n", + "\n", + "Column names available in the most recent year's data:\n", + " 1. Bene_Age_65_74_Cnt\n", + " 2. Bene_Age_75_84_Cnt\n", + " 3. Bene_Age_GT_84_Cnt\n", + " 4. Bene_Age_LT_65_Cnt\n", + " 5. Bene_Avg_Age\n", + " 6. Bene_Avg_Risk_Scre\n", + " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", + " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", + " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", + " 10. Bene_CC_BH_Anxiety_V1_Pct\n", + " 11. Bene_CC_BH_Bipolar_V1_Pct\n", + " 12. Bene_CC_BH_Depress_V1_Pct\n", + " 13. Bene_CC_BH_Mood_V2_Pct\n", + " 14. Bene_CC_BH_PD_V1_Pct\n", + " 15. Bene_CC_BH_PTSD_V1_Pct\n", + " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", + " 17. Bene_CC_BH_Tobacco_V1_Pct\n", + " 18. Bene_CC_PH_Afib_V2_Pct\n", + " 19. Bene_CC_PH_Arthritis_V2_Pct\n", + " 20. Bene_CC_PH_Asthma_V2_Pct\n", + " 21. Bene_CC_PH_CKD_V2_Pct\n", + " 22. Bene_CC_PH_COPD_V2_Pct\n", + " 23. Bene_CC_PH_Cancer6_V2_Pct\n", + " 24. Bene_CC_PH_Diabetes_V2_Pct\n", + " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", + " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", + " 27. Bene_CC_PH_Hypertension_V2_Pct\n", + " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", + " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", + " 30. Bene_CC_PH_Parkinson_V2_Pct\n", + " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", + " 32. Bene_Dual_Cnt\n", + " 33. Bene_Feml_Cnt\n", + " 34. Bene_Male_Cnt\n", + " 35. Bene_Ndual_Cnt\n", + " 36. Bene_Race_Api_Cnt\n", + " 37. Bene_Race_Black_Cnt\n", + " 38. Bene_Race_Hspnc_Cnt\n", + " 39. Bene_Race_Natind_Cnt\n", + " 40. Bene_Race_Othr_Cnt\n", + " 41. Bene_Race_Wht_Cnt\n", + " 42. DME_Sprsn_Ind\n", + " 43. DME_Suplr_Mdcr_Alowd_Amt\n", + " 44. DME_Suplr_Mdcr_Pymt_Amt\n", + " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 46. DME_Suplr_Sbmtd_Chrgs\n", + " 47. DME_Tot_Suplr_Benes\n", + " 48. DME_Tot_Suplr_Clms\n", + " 49. DME_Tot_Suplr_HCPCS_Cds\n", + " 50. DME_Tot_Suplr_Srvcs\n", + " 51. Drug_Sprsn_Ind\n", + " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", + " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", + " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 55. Drug_Suplr_Sbmtd_Chrgs\n", + " 56. Drug_Tot_Suplr_Benes\n", + " 57. Drug_Tot_Suplr_Clms\n", + " 58. Drug_Tot_Suplr_HCPCS_Cds\n", + " 59. Drug_Tot_Suplr_Srvcs\n", + " 60. POS_Sprsn_Ind\n", + " 61. POS_Suplr_Mdcr_Alowd_Amt\n", + " 62. POS_Suplr_Mdcr_Pymt_Amt\n", + " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 64. POS_Suplr_Sbmtd_Chrgs\n", + " 65. POS_Tot_Suplr_Benes\n", + " 66. POS_Tot_Suplr_Clms\n", + " 67. POS_Tot_Suplr_HCPCS_Cds\n", + " 68. POS_Tot_Suplr_Srvcs\n", + " 69. Suplr_Mdcr_Alowd_Amt\n", + " 70. Suplr_Mdcr_Pymt_Amt\n", + " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 72. Suplr_NPI\n", + " 73. Suplr_Prvdr_City\n", + " 74. Suplr_Prvdr_Cntry\n", + " 75. Suplr_Prvdr_Crdntls\n", + " 76. Suplr_Prvdr_Ent_Cd\n", + " 77. Suplr_Prvdr_First_Name\n", + " 78. Suplr_Prvdr_Gndr\n", + " 79. Suplr_Prvdr_Last_Name_Org\n", + " 80. Suplr_Prvdr_MI\n", + " 81. Suplr_Prvdr_RUCA\n", + " 82. Suplr_Prvdr_RUCA_Desc\n", + " 83. Suplr_Prvdr_Spclty_Desc\n", + " 84. Suplr_Prvdr_Spclty_Srce\n", + " 85. Suplr_Prvdr_St1\n", + " 86. Suplr_Prvdr_St2\n", + " 87. Suplr_Prvdr_State_Abrvtn\n", + " 88. Suplr_Prvdr_State_FIPS\n", + " 89. Suplr_Prvdr_Zip5\n", + " 90. Suplr_Sbmtd_Chrgs\n", + " 91. Tot_Suplr_Benes\n", + " 92. Tot_Suplr_Clms\n", + " 93. Tot_Suplr_HCPCS_Cds\n", + " 94. Tot_Suplr_Srvcs\n", + "\n", + "Detecting high growth suppliers...\n", + "Identifying suppliers with highest year-over-year growth rates...\n", + "Warning: No supplier name column found. Using placeholder names.\n", + "\n", + "Available columns in the dataset:\n", + " 1. Bene_Age_65_74_Cnt\n", + " 2. Bene_Age_75_84_Cnt\n", + " 3. Bene_Age_GT_84_Cnt\n", + " 4. Bene_Age_LT_65_Cnt\n", + " 5. Bene_Avg_Age\n", + " 6. Bene_Avg_Risk_Scre\n", + " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", + " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", + " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", + " 10. Bene_CC_BH_Anxiety_V1_Pct\n", + " 11. Bene_CC_BH_Bipolar_V1_Pct\n", + " 12. Bene_CC_BH_Depress_V1_Pct\n", + " 13. Bene_CC_BH_Mood_V2_Pct\n", + " 14. Bene_CC_BH_PD_V1_Pct\n", + " 15. Bene_CC_BH_PTSD_V1_Pct\n", + " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", + " 17. Bene_CC_BH_Tobacco_V1_Pct\n", + " 18. Bene_CC_PH_Afib_V2_Pct\n", + " 19. Bene_CC_PH_Arthritis_V2_Pct\n", + " 20. Bene_CC_PH_Asthma_V2_Pct\n", + " 21. Bene_CC_PH_CKD_V2_Pct\n", + " 22. Bene_CC_PH_COPD_V2_Pct\n", + " 23. Bene_CC_PH_Cancer6_V2_Pct\n", + " 24. Bene_CC_PH_Diabetes_V2_Pct\n", + " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", + " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", + " 27. Bene_CC_PH_Hypertension_V2_Pct\n", + " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", + " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", + " 30. Bene_CC_PH_Parkinson_V2_Pct\n", + " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", + " 32. Bene_Dual_Cnt\n", + " 33. Bene_Feml_Cnt\n", + " 34. Bene_Male_Cnt\n", + " 35. Bene_Ndual_Cnt\n", + " 36. Bene_Race_Api_Cnt\n", + " 37. Bene_Race_Black_Cnt\n", + " 38. Bene_Race_Hspnc_Cnt\n", + " 39. Bene_Race_Natind_Cnt\n", + " 40. Bene_Race_Othr_Cnt\n", + " 41. Bene_Race_Wht_Cnt\n", + " 42. DME_Sprsn_Ind\n", + " 43. DME_Suplr_Mdcr_Alowd_Amt\n", + " 44. DME_Suplr_Mdcr_Pymt_Amt\n", + " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 46. DME_Suplr_Sbmtd_Chrgs\n", + " 47. DME_Tot_Suplr_Benes\n", + " 48. DME_Tot_Suplr_Clms\n", + " 49. DME_Tot_Suplr_HCPCS_Cds\n", + " 50. DME_Tot_Suplr_Srvcs\n", + " 51. Drug_Sprsn_Ind\n", + " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", + " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", + " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 55. Drug_Suplr_Sbmtd_Chrgs\n", + " 56. Drug_Tot_Suplr_Benes\n", + " 57. Drug_Tot_Suplr_Clms\n", + " 58. Drug_Tot_Suplr_HCPCS_Cds\n", + " 59. Drug_Tot_Suplr_Srvcs\n", + " 60. POS_Sprsn_Ind\n", + " 61. POS_Suplr_Mdcr_Alowd_Amt\n", + " 62. POS_Suplr_Mdcr_Pymt_Amt\n", + " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 64. POS_Suplr_Sbmtd_Chrgs\n", + " 65. POS_Tot_Suplr_Benes\n", + " 66. POS_Tot_Suplr_Clms\n", + " 67. POS_Tot_Suplr_HCPCS_Cds\n", + " 68. POS_Tot_Suplr_Srvcs\n", + " 69. Suplr_Mdcr_Alowd_Amt\n", + " 70. Suplr_Mdcr_Pymt_Amt\n", + " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 72. Suplr_NPI\n", + " 73. Suplr_Prvdr_City\n", + " 74. Suplr_Prvdr_Cntry\n", + " 75. Suplr_Prvdr_Crdntls\n", + " 76. Suplr_Prvdr_Ent_Cd\n", + " 77. Suplr_Prvdr_First_Name\n", + " 78. Suplr_Prvdr_Gndr\n", + " 79. Suplr_Prvdr_Last_Name_Org\n", + " 80. Suplr_Prvdr_MI\n", + " 81. Suplr_Prvdr_RUCA\n", + " 82. Suplr_Prvdr_RUCA_Desc\n", + " 83. Suplr_Prvdr_Spclty_Desc\n", + " 84. Suplr_Prvdr_Spclty_Srce\n", + " 85. Suplr_Prvdr_St1\n", + " 86. Suplr_Prvdr_St2\n", + " 87. Suplr_Prvdr_State_Abrvtn\n", + " 88. Suplr_Prvdr_State_FIPS\n", + " 89. Suplr_Prvdr_Zip5\n", + " 90. Suplr_Sbmtd_Chrgs\n", + " 91. Tot_Suplr_Benes\n", + " 92. Tot_Suplr_Clms\n", + " 93. Tot_Suplr_HCPCS_Cds\n", + " 94. Tot_Suplr_Srvcs\n", + "\n", + "Please adjust the script to use the correct column names for your dataset.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_18399/2260217778.py:489: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " bars = sns.barplot(\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_18399/2260217778.py:581: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_18399/2260217778.py:596: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ High growth suppliers visualization created successfully.\n", + "\n", + "Detecting geographic fraud hotspots...\n", + "✓ Geographic hotspots visualization created successfully.\n", + "\n", + "Detecting outlier claim amounts...\n", + "Identifying suppliers with abnormally high average claim amounts in 2022...\n", + "Warning: No supplier name column found. Using placeholder names.\n", + "\n", + "Available columns in the dataset:\n", + " 1. Bene_Age_65_74_Cnt\n", + " 2. Bene_Age_75_84_Cnt\n", + " 3. Bene_Age_GT_84_Cnt\n", + " 4. Bene_Age_LT_65_Cnt\n", + " 5. Bene_Avg_Age\n", + " 6. Bene_Avg_Risk_Scre\n", + " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", + " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", + " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", + " 10. Bene_CC_BH_Anxiety_V1_Pct\n", + " 11. Bene_CC_BH_Bipolar_V1_Pct\n", + " 12. Bene_CC_BH_Depress_V1_Pct\n", + " 13. Bene_CC_BH_Mood_V2_Pct\n", + " 14. Bene_CC_BH_PD_V1_Pct\n", + " 15. Bene_CC_BH_PTSD_V1_Pct\n", + " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", + " 17. Bene_CC_BH_Tobacco_V1_Pct\n", + " 18. Bene_CC_PH_Afib_V2_Pct\n", + " 19. Bene_CC_PH_Arthritis_V2_Pct\n", + " 20. Bene_CC_PH_Asthma_V2_Pct\n", + " 21. Bene_CC_PH_CKD_V2_Pct\n", + " 22. Bene_CC_PH_COPD_V2_Pct\n", + " 23. Bene_CC_PH_Cancer6_V2_Pct\n", + " 24. Bene_CC_PH_Diabetes_V2_Pct\n", + " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", + " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", + " 27. Bene_CC_PH_Hypertension_V2_Pct\n", + " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", + " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", + " 30. Bene_CC_PH_Parkinson_V2_Pct\n", + " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", + " 32. Bene_Dual_Cnt\n", + " 33. Bene_Feml_Cnt\n", + " 34. Bene_Male_Cnt\n", + " 35. Bene_Ndual_Cnt\n", + " 36. Bene_Race_Api_Cnt\n", + " 37. Bene_Race_Black_Cnt\n", + " 38. Bene_Race_Hspnc_Cnt\n", + " 39. Bene_Race_Natind_Cnt\n", + " 40. Bene_Race_Othr_Cnt\n", + " 41. Bene_Race_Wht_Cnt\n", + " 42. DME_Sprsn_Ind\n", + " 43. DME_Suplr_Mdcr_Alowd_Amt\n", + " 44. DME_Suplr_Mdcr_Pymt_Amt\n", + " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 46. DME_Suplr_Sbmtd_Chrgs\n", + " 47. DME_Tot_Suplr_Benes\n", + " 48. DME_Tot_Suplr_Clms\n", + " 49. DME_Tot_Suplr_HCPCS_Cds\n", + " 50. DME_Tot_Suplr_Srvcs\n", + " 51. Drug_Sprsn_Ind\n", + " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", + " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", + " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 55. Drug_Suplr_Sbmtd_Chrgs\n", + " 56. Drug_Tot_Suplr_Benes\n", + " 57. Drug_Tot_Suplr_Clms\n", + " 58. Drug_Tot_Suplr_HCPCS_Cds\n", + " 59. Drug_Tot_Suplr_Srvcs\n", + " 60. POS_Sprsn_Ind\n", + " 61. POS_Suplr_Mdcr_Alowd_Amt\n", + " 62. POS_Suplr_Mdcr_Pymt_Amt\n", + " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 64. POS_Suplr_Sbmtd_Chrgs\n", + " 65. POS_Tot_Suplr_Benes\n", + " 66. POS_Tot_Suplr_Clms\n", + " 67. POS_Tot_Suplr_HCPCS_Cds\n", + " 68. POS_Tot_Suplr_Srvcs\n", + " 69. Suplr_Mdcr_Alowd_Amt\n", + " 70. Suplr_Mdcr_Pymt_Amt\n", + " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 72. Suplr_NPI\n", + " 73. Suplr_Prvdr_City\n", + " 74. Suplr_Prvdr_Cntry\n", + " 75. Suplr_Prvdr_Crdntls\n", + " 76. Suplr_Prvdr_Ent_Cd\n", + " 77. Suplr_Prvdr_First_Name\n", + " 78. Suplr_Prvdr_Gndr\n", + " 79. Suplr_Prvdr_Last_Name_Org\n", + " 80. Suplr_Prvdr_MI\n", + " 81. Suplr_Prvdr_RUCA\n", + " 82. Suplr_Prvdr_RUCA_Desc\n", + " 83. Suplr_Prvdr_Spclty_Desc\n", + " 84. Suplr_Prvdr_Spclty_Srce\n", + " 85. Suplr_Prvdr_St1\n", + " 86. Suplr_Prvdr_St2\n", + " 87. Suplr_Prvdr_State_Abrvtn\n", + " 88. Suplr_Prvdr_State_FIPS\n", + " 89. Suplr_Prvdr_Zip5\n", + " 90. Suplr_Sbmtd_Chrgs\n", + " 91. Tot_Suplr_Benes\n", + " 92. Tot_Suplr_Clms\n", + " 93. Tot_Suplr_HCPCS_Cds\n", + " 94. Tot_Suplr_Srvcs\n", + "\n", + "Please adjust the script to use the correct column names for your dataset.\n", + "Error: No average charge metrics found in data for year 2022.\n", + "⚠ No outlier claim amounts identified.\n", + "\n", + "Detecting unusual beneficiary-to-claim ratios...\n", + "Identifying suppliers with unusual claims per beneficiary in 2022...\n", + "Warning: No supplier name column found. Using placeholder names.\n", + "\n", + "Available columns in the dataset:\n", + " 1. Bene_Age_65_74_Cnt\n", + " 2. Bene_Age_75_84_Cnt\n", + " 3. Bene_Age_GT_84_Cnt\n", + " 4. Bene_Age_LT_65_Cnt\n", + " 5. Bene_Avg_Age\n", + " 6. Bene_Avg_Risk_Scre\n", + " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", + " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", + " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", + " 10. Bene_CC_BH_Anxiety_V1_Pct\n", + " 11. Bene_CC_BH_Bipolar_V1_Pct\n", + " 12. Bene_CC_BH_Depress_V1_Pct\n", + " 13. Bene_CC_BH_Mood_V2_Pct\n", + " 14. Bene_CC_BH_PD_V1_Pct\n", + " 15. Bene_CC_BH_PTSD_V1_Pct\n", + " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", + " 17. Bene_CC_BH_Tobacco_V1_Pct\n", + " 18. Bene_CC_PH_Afib_V2_Pct\n", + " 19. Bene_CC_PH_Arthritis_V2_Pct\n", + " 20. Bene_CC_PH_Asthma_V2_Pct\n", + " 21. Bene_CC_PH_CKD_V2_Pct\n", + " 22. Bene_CC_PH_COPD_V2_Pct\n", + " 23. Bene_CC_PH_Cancer6_V2_Pct\n", + " 24. Bene_CC_PH_Diabetes_V2_Pct\n", + " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", + " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", + " 27. Bene_CC_PH_Hypertension_V2_Pct\n", + " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", + " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", + " 30. Bene_CC_PH_Parkinson_V2_Pct\n", + " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", + " 32. Bene_Dual_Cnt\n", + " 33. Bene_Feml_Cnt\n", + " 34. Bene_Male_Cnt\n", + " 35. Bene_Ndual_Cnt\n", + " 36. Bene_Race_Api_Cnt\n", + " 37. Bene_Race_Black_Cnt\n", + " 38. Bene_Race_Hspnc_Cnt\n", + " 39. Bene_Race_Natind_Cnt\n", + " 40. Bene_Race_Othr_Cnt\n", + " 41. Bene_Race_Wht_Cnt\n", + " 42. DME_Sprsn_Ind\n", + " 43. DME_Suplr_Mdcr_Alowd_Amt\n", + " 44. DME_Suplr_Mdcr_Pymt_Amt\n", + " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 46. DME_Suplr_Sbmtd_Chrgs\n", + " 47. DME_Tot_Suplr_Benes\n", + " 48. DME_Tot_Suplr_Clms\n", + " 49. DME_Tot_Suplr_HCPCS_Cds\n", + " 50. DME_Tot_Suplr_Srvcs\n", + " 51. Drug_Sprsn_Ind\n", + " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", + " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", + " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 55. Drug_Suplr_Sbmtd_Chrgs\n", + " 56. Drug_Tot_Suplr_Benes\n", + " 57. Drug_Tot_Suplr_Clms\n", + " 58. Drug_Tot_Suplr_HCPCS_Cds\n", + " 59. Drug_Tot_Suplr_Srvcs\n", + " 60. POS_Sprsn_Ind\n", + " 61. POS_Suplr_Mdcr_Alowd_Amt\n", + " 62. POS_Suplr_Mdcr_Pymt_Amt\n", + " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 64. POS_Suplr_Sbmtd_Chrgs\n", + " 65. POS_Tot_Suplr_Benes\n", + " 66. POS_Tot_Suplr_Clms\n", + " 67. POS_Tot_Suplr_HCPCS_Cds\n", + " 68. POS_Tot_Suplr_Srvcs\n", + " 69. Suplr_Mdcr_Alowd_Amt\n", + " 70. Suplr_Mdcr_Pymt_Amt\n", + " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", + " 72. Suplr_NPI\n", + " 73. Suplr_Prvdr_City\n", + " 74. Suplr_Prvdr_Cntry\n", + " 75. Suplr_Prvdr_Crdntls\n", + " 76. Suplr_Prvdr_Ent_Cd\n", + " 77. Suplr_Prvdr_First_Name\n", + " 78. Suplr_Prvdr_Gndr\n", + " 79. Suplr_Prvdr_Last_Name_Org\n", + " 80. Suplr_Prvdr_MI\n", + " 81. Suplr_Prvdr_RUCA\n", + " 82. Suplr_Prvdr_RUCA_Desc\n", + " 83. Suplr_Prvdr_Spclty_Desc\n", + " 84. Suplr_Prvdr_Spclty_Srce\n", + " 85. Suplr_Prvdr_St1\n", + " 86. Suplr_Prvdr_St2\n", + " 87. Suplr_Prvdr_State_Abrvtn\n", + " 88. Suplr_Prvdr_State_FIPS\n", + " 89. Suplr_Prvdr_Zip5\n", + " 90. Suplr_Sbmtd_Chrgs\n", + " 91. Tot_Suplr_Benes\n", + " 92. Tot_Suplr_Clms\n", + " 93. Tot_Suplr_HCPCS_Cds\n", + " 94. Tot_Suplr_Srvcs\n", + "\n", + "Please adjust the script to use the correct column names for your dataset.\n", + "✓ Unusual beneficiary-to-claim ratios visualization created successfully.\n", + "\n", + "Identifying suppliers with multiple fraud indicators...\n", + "Identifying suppliers with multiple fraud indicators...\n", + "Warning: One or more fraud indicator dataframes are empty. Cannot perform combined analysis.\n", + "⚠ No suppliers with multiple fraud indicators identified.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_18399/2260217778.py:705: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " bars = sns.barplot(\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "print(\"Hello World\")" + "#!/usr/bin/env python3\n", + "# -*- coding: utf-8 -*-\n", + "\n", + "\"\"\"\n", + "DME Fraud Detection Script\n", + "This script analyzes Medicare DME supplier data to identify potential fraud indicators,\n", + "with a focus on suspicious growth patterns similar to credit card fraud detection techniques.\n", + "\"\"\"\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from collections import defaultdict\n", + "import os\n", + "import sys\n", + "\n", + "# Set visualization style\n", + "plt.style.use('seaborn-v0_8-whitegrid')\n", + "sns.set_palette('viridis')\n", + "\n", + "\n", + "def import_dme_data(file_path):\n", + " \"\"\"\n", + " Import and preprocess DME data from a CSV file.\n", + "\n", + " Parameters:\n", + " -----------\n", + " file_path : str\n", + " Path to the CSV file containing DME data\n", + "\n", + " Returns:\n", + " --------\n", + " df : DataFrame\n", + " Processed DataFrame containing DME data\n", + " \"\"\"\n", + " print(f\"Importing data from {file_path}...\")\n", + "\n", + " try:\n", + " # Import data with appropriate dtypes to handle monetary values correctly\n", + " df = pd.read_csv(file_path, low_memory=False)\n", + "\n", + " # Convert monetary columns to numeric\n", + " money_columns = [\n", + " col for col in df.columns if 'Pymt' in col or 'Amt' in col]\n", + " for col in money_columns:\n", + " if col in df.columns:\n", + " df[col] = pd.to_numeric(df[col], errors='coerce')\n", + "\n", + " print(f\"Successfully imported data with shape: {df.shape}\")\n", + " return df\n", + "\n", + " except Exception as e:\n", + " print(f\"Error importing data: {str(e)}\")\n", + " return None\n", + "\n", + "\n", + "def get_column_mapping(df):\n", + " \"\"\"\n", + " Get a mapping of expected column names to actual column names in the DataFrame.\n", + " This helps handle variations in column names across different datasets.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : DataFrame\n", + " DataFrame to inspect for column names\n", + "\n", + " Returns:\n", + " --------\n", + " column_map : dict\n", + " Dictionary mapping expected column names to actual column names\n", + " \"\"\"\n", + " column_map = {}\n", + "\n", + " # Map for supplier organization name\n", + " if 'Suplr_Prvdr_Org_Name' in df.columns:\n", + " column_map['supplier_name'] = 'Suplr_Prvdr_Org_Name'\n", + " elif 'Suplr_Name' in df.columns:\n", + " column_map['supplier_name'] = 'Suplr_Name'\n", + " elif 'Supplier_Name' in df.columns:\n", + " column_map['supplier_name'] = 'Supplier_Name'\n", + " elif 'Provider_Org_Name' in df.columns:\n", + " column_map['supplier_name'] = 'Provider_Org_Name'\n", + " else:\n", + " # If no suitable column exists, create a placeholder\n", + " print(\"Warning: No supplier name column found. Using placeholder names.\")\n", + " column_map['supplier_name'] = None\n", + "\n", + " # Map for supplier state\n", + " if 'Suplr_Prvdr_State_Abrvtn' in df.columns:\n", + " column_map['supplier_state'] = 'Suplr_Prvdr_State_Abrvtn'\n", + " elif 'Suplr_State' in df.columns:\n", + " column_map['supplier_state'] = 'Suplr_State'\n", + " elif 'State' in df.columns:\n", + " column_map['supplier_state'] = 'State'\n", + " else:\n", + " print(\"Warning: No supplier state column found. Using placeholder.\")\n", + " column_map['supplier_state'] = None\n", + "\n", + " # Map for supplier NPI\n", + " if 'Suplr_NPI' in df.columns:\n", + " column_map['supplier_npi'] = 'Suplr_NPI'\n", + " elif 'NPI' in df.columns:\n", + " column_map['supplier_npi'] = 'NPI'\n", + " elif 'Provider_NPI' in df.columns:\n", + " column_map['supplier_npi'] = 'Provider_NPI'\n", + " else:\n", + " print(\"Warning: No NPI column found. Using index as placeholder.\")\n", + " column_map['supplier_npi'] = None\n", + "\n", + " # Print available columns if key columns are missing\n", + " if None in column_map.values():\n", + " print(\"\\nAvailable columns in the dataset:\")\n", + " for i, col in enumerate(sorted(df.columns)):\n", + " print(f\" {i+1}. {col}\")\n", + " print(\n", + " \"\\nPlease adjust the script to use the correct column names for your dataset.\")\n", + "\n", + " return column_map\n", + "\n", + "\n", + "def detect_high_growth_suppliers(df_by_year, metric='DME_Suplr_Mdcr_Pymt_Amt', top_n=50):\n", + " \"\"\"\n", + " Identify suppliers with abnormally high growth rates year over year.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df_by_year : dict\n", + " Dictionary containing DataFrames by year\n", + " metric : str\n", + " The metric to analyze for growth (default: Medicare payments)\n", + " top_n : int\n", + " Number of top growth suppliers to identify\n", + "\n", + " Returns:\n", + " --------\n", + " growth_df : DataFrame\n", + " DataFrame containing suppliers with their growth rates\n", + " \"\"\"\n", + " print(f\"Identifying suppliers with highest year-over-year growth rates...\")\n", + "\n", + " # Check if metric exists in all dataframes\n", + " for year, df in df_by_year.items():\n", + " if metric not in df.columns:\n", + " available_metrics = [\n", + " col for col in df.columns if 'Pymt' in col or 'Amt' in col]\n", + " if not available_metrics:\n", + " print(\n", + " f\"Error: No payment metrics found in data for year {year}.\")\n", + " return pd.DataFrame()\n", + "\n", + " # Use the first available payment metric\n", + " metric = available_metrics[0]\n", + " print(f\"Using alternate metric: {metric}\")\n", + " break\n", + "\n", + " # Get all available years\n", + " years = sorted(df_by_year.keys())\n", + "\n", + " if len(years) < 2:\n", + " print(\"Error: Need at least two years of data to calculate growth rates\")\n", + " return pd.DataFrame()\n", + "\n", + " # Get column mappings from the most recent year's data\n", + " recent_year = max(years)\n", + " column_map = get_column_mapping(df_by_year[recent_year])\n", + "\n", + " # Create a dictionary to store supplier data across years\n", + " supplier_data = {}\n", + " supplier_info = {}\n", + "\n", + " # Process each supplier's data for each year\n", + " for year in years:\n", + " df = df_by_year[year]\n", + "\n", + " # Get NPI column name\n", + " npi_col = column_map['supplier_npi']\n", + " if npi_col is None:\n", + " # Create a synthetic NPI using index\n", + " df['synthetic_npi'] = 'NPI' + df.index.astype(str)\n", + " npi_col = 'synthetic_npi'\n", + "\n", + " # Group by supplier NPI and sum the metric\n", + " supplier_metric = df.groupby(npi_col)[metric].sum().reset_index()\n", + "\n", + " # Store in dictionary\n", + " for _, row in supplier_metric.iterrows():\n", + " npi = row[npi_col]\n", + " value = row[metric]\n", + "\n", + " if npi not in supplier_data:\n", + " supplier_data[npi] = {}\n", + "\n", + " # Store supplier info for later use\n", + " supplier_row = df[df[npi_col] == npi].iloc[0] if len(\n", + " df[df[npi_col] == npi]) > 0 else None\n", + " if supplier_row is not None:\n", + " supplier_info[npi] = {\n", + " 'name': supplier_row[column_map['supplier_name']] if column_map['supplier_name'] is not None else f\"Supplier {npi}\",\n", + " 'state': supplier_row[column_map['supplier_state']] if column_map['supplier_state'] is not None else 'Unknown'\n", + " }\n", + " else:\n", + " supplier_info[npi] = {\n", + " 'name': f\"Supplier {npi}\",\n", + " 'state': 'Unknown'\n", + " }\n", + "\n", + " supplier_data[npi][year] = value\n", + "\n", + " # Calculate year-over-year growth rates\n", + " growth_data = []\n", + "\n", + " for npi, year_values in supplier_data.items():\n", + " # Need at least two years of data for this supplier\n", + " if len(year_values) < 2:\n", + " continue\n", + "\n", + " for i in range(len(years) - 1):\n", + " current_year = years[i]\n", + " next_year = years[i + 1]\n", + "\n", + " # Skip if supplier doesn't have data for both years\n", + " if current_year not in year_values or next_year not in year_values:\n", + " continue\n", + "\n", + " current_value = year_values[current_year]\n", + " next_value = year_values[next_year]\n", + "\n", + " # Skip if current value is zero (would result in infinity growth)\n", + " if current_value == 0:\n", + " continue\n", + "\n", + " # Calculate growth rate\n", + " growth_rate = ((next_value - current_value) / current_value) * 100\n", + "\n", + " growth_data.append({\n", + " 'Supplier NPI': npi,\n", + " 'Supplier Name': supplier_info[npi]['name'],\n", + " 'Supplier State': supplier_info[npi]['state'],\n", + " 'Year Period': f\"{current_year}-{next_year}\",\n", + " 'Start Year Value': current_value,\n", + " 'End Year Value': next_value,\n", + " 'Growth Rate (%)': growth_rate,\n", + " 'Absolute Growth': next_value - current_value\n", + " })\n", + "\n", + " # Convert to DataFrame\n", + " growth_df = pd.DataFrame(growth_data)\n", + "\n", + " # Sort by growth rate (descending)\n", + " growth_df = growth_df.sort_values('Growth Rate (%)', ascending=False)\n", + "\n", + " return growth_df.head(top_n)\n", + "\n", + "\n", + "def detect_outlier_claim_amounts(df, year, metric='DME_Avg_Sbmtd_Chrg', threshold=2.0):\n", + " \"\"\"\n", + " Identify suppliers with abnormally high average claim amounts.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : DataFrame\n", + " DataFrame for a specific year\n", + " year : int\n", + " The year being analyzed\n", + " metric : str\n", + " The metric to analyze for outliers (default: average submitted charge)\n", + " threshold : float\n", + " Z-score threshold for flagging outliers (default: 2.0)\n", + "\n", + " Returns:\n", + " --------\n", + " outlier_df : DataFrame\n", + " DataFrame containing suppliers with outlier claim amounts\n", + " \"\"\"\n", + " print(\n", + " f\"Identifying suppliers with abnormally high average claim amounts in {year}...\")\n", + "\n", + " # Get column mappings\n", + " column_map = get_column_mapping(df)\n", + "\n", + " # Verify the metric exists\n", + " if metric not in df.columns:\n", + " available_metrics = [col for col in df.columns if 'Avg' in col and (\n", + " 'Chrg' in col or 'Amt' in col)]\n", + " if not available_metrics:\n", + " print(\n", + " f\"Error: No average charge metrics found in data for year {year}.\")\n", + " return pd.DataFrame()\n", + "\n", + " # Use the first available charge metric\n", + " metric = available_metrics[0]\n", + " print(f\"Using alternate metric: {metric}\")\n", + "\n", + " # Create a copy of the DataFrame to avoid modifying original\n", + " df_copy = df.copy()\n", + "\n", + " # Calculate z-scores for the metric\n", + " df_copy[f'{metric}_zscore'] = (\n", + " df_copy[metric] - df_copy[metric].mean()) / df_copy[metric].std()\n", + "\n", + " # Filter for outliers\n", + " outlier_df = df_copy[df_copy[f'{metric}_zscore'] > threshold].copy()\n", + "\n", + " # Sort by z-score (descending)\n", + " outlier_df = outlier_df.sort_values(f'{metric}_zscore', ascending=False)\n", + "\n", + " # Get NPI column name\n", + " npi_col = column_map['supplier_npi']\n", + " if npi_col is None:\n", + " # Create a synthetic NPI using index\n", + " outlier_df['synthetic_npi'] = 'NPI' + outlier_df.index.astype(str)\n", + " npi_col = 'synthetic_npi'\n", + "\n", + " # Get name column\n", + " name_col = column_map['supplier_name']\n", + " if name_col is None:\n", + " # Create a synthetic name\n", + " outlier_df['synthetic_name'] = outlier_df[npi_col].apply(\n", + " lambda x: f\"Supplier {x}\")\n", + " name_col = 'synthetic_name'\n", + "\n", + " # Get state column\n", + " state_col = column_map['supplier_state']\n", + " if state_col is None:\n", + " # Create a synthetic state\n", + " outlier_df['synthetic_state'] = 'Unknown'\n", + " state_col = 'synthetic_state'\n", + "\n", + " # Get beneficiary and claims columns if they exist\n", + " bene_col = 'DME_Tot_Suplr_Benes' if 'DME_Tot_Suplr_Benes' in df.columns else None\n", + " claims_col = 'DME_Tot_Suplr_Clms' if 'DME_Tot_Suplr_Clms' in df.columns else None\n", + "\n", + " # Select relevant columns\n", + " columns = [npi_col, name_col, state_col, metric, f'{metric}_zscore']\n", + " if bene_col:\n", + " columns.append(bene_col)\n", + " if claims_col:\n", + " columns.append(claims_col)\n", + "\n", + " # Rename columns to standard names\n", + " result_df = outlier_df[columns].copy()\n", + " result_df.columns = [\n", + " 'Suplr_NPI',\n", + " 'Suplr_Prvdr_Org_Name',\n", + " 'Suplr_Prvdr_State_Abrvtn',\n", + " metric,\n", + " f'{metric}_zscore'\n", + " ] + (['DME_Tot_Suplr_Benes'] if bene_col else []) + (['DME_Tot_Suplr_Clms'] if claims_col else [])\n", + "\n", + " return result_df\n", + "\n", + "\n", + "def detect_unusual_beneficiary_to_claim_ratio(df, year, threshold=2.0):\n", + " \"\"\"\n", + " Identify suppliers with abnormally high claim per beneficiary ratios.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : DataFrame\n", + " DataFrame for a specific year\n", + " year : int\n", + " The year being analyzed\n", + " threshold : float\n", + " Z-score threshold for flagging outliers (default: 2.0)\n", + "\n", + " Returns:\n", + " --------\n", + " ratio_df : DataFrame\n", + " DataFrame containing suppliers with unusual claim-to-beneficiary ratios\n", + " \"\"\"\n", + " print(\n", + " f\"Identifying suppliers with unusual claims per beneficiary in {year}...\")\n", + "\n", + " # Get column mappings\n", + " column_map = get_column_mapping(df)\n", + "\n", + " # Check for beneficiary and claims columns\n", + " bene_col = None\n", + " claims_col = None\n", + "\n", + " # Look for beneficiary column\n", + " for potential_col in ['DME_Tot_Suplr_Benes', 'Tot_Benes', 'Beneficiaries', 'Total_Beneficiaries']:\n", + " if potential_col in df.columns:\n", + " bene_col = potential_col\n", + " break\n", + "\n", + " # Look for claims column\n", + " for potential_col in ['DME_Tot_Suplr_Clms', 'Tot_Clms', 'Claims', 'Total_Claims']:\n", + " if potential_col in df.columns:\n", + " claims_col = potential_col\n", + " break\n", + "\n", + " if bene_col is None or claims_col is None:\n", + " print(\n", + " f\"Error: Unable to find beneficiary or claims columns in data for year {year}.\")\n", + " print(\"Available columns: \", \", \".join(sorted(df.columns)))\n", + " return pd.DataFrame()\n", + "\n", + " # Create a copy of the DataFrame to avoid modifying original\n", + " df_copy = df.copy()\n", + "\n", + " # Calculate claims per beneficiary\n", + " df_copy['Claims_Per_Beneficiary'] = df_copy[claims_col] / df_copy[bene_col]\n", + "\n", + " # Calculate z-scores\n", + " df_copy['Claims_Per_Beneficiary_zscore'] = (\n", + " df_copy['Claims_Per_Beneficiary'] - df_copy['Claims_Per_Beneficiary'].mean()) / df_copy['Claims_Per_Beneficiary'].std()\n", + "\n", + " # Filter for outliers\n", + " ratio_df = df_copy[df_copy['Claims_Per_Beneficiary_zscore']\n", + " > threshold].copy()\n", + "\n", + " # Sort by z-score (descending)\n", + " ratio_df = ratio_df.sort_values(\n", + " 'Claims_Per_Beneficiary_zscore', ascending=False)\n", + "\n", + " # Get NPI column name\n", + " npi_col = column_map['supplier_npi']\n", + " if npi_col is None:\n", + " # Create a synthetic NPI using index\n", + " ratio_df['synthetic_npi'] = 'NPI' + ratio_df.index.astype(str)\n", + " npi_col = 'synthetic_npi'\n", + "\n", + " # Get name column\n", + " name_col = column_map['supplier_name']\n", + " if name_col is None:\n", + " # Create a synthetic name\n", + " ratio_df['synthetic_name'] = ratio_df[npi_col].apply(\n", + " lambda x: f\"Supplier {x}\")\n", + " name_col = 'synthetic_name'\n", + "\n", + " # Get state column\n", + " state_col = column_map['supplier_state']\n", + " if state_col is None:\n", + " # Create a synthetic state\n", + " ratio_df['synthetic_state'] = 'Unknown'\n", + " state_col = 'synthetic_state'\n", + "\n", + " # Select relevant columns\n", + " columns = [\n", + " npi_col,\n", + " name_col,\n", + " state_col,\n", + " bene_col,\n", + " claims_col,\n", + " 'Claims_Per_Beneficiary',\n", + " 'Claims_Per_Beneficiary_zscore'\n", + " ]\n", + "\n", + " # Rename columns to standard names\n", + " result_df = ratio_df[columns].copy()\n", + " result_df.columns = [\n", + " 'Suplr_NPI',\n", + " 'Suplr_Prvdr_Org_Name',\n", + " 'Suplr_Prvdr_State_Abrvtn',\n", + " 'DME_Tot_Suplr_Benes',\n", + " 'DME_Tot_Suplr_Clms',\n", + " 'Claims_Per_Beneficiary',\n", + " 'Claims_Per_Beneficiary_zscore'\n", + " ]\n", + "\n", + " return result_df\n", + "\n", + "\n", + "def plot_high_growth_suppliers(growth_df, top_n=20):\n", + " \"\"\"\n", + " Create a visualization of suppliers with highest growth rates.\n", + "\n", + " Parameters:\n", + " -----------\n", + " growth_df : DataFrame\n", + " DataFrame from detect_high_growth_suppliers function\n", + " top_n : int\n", + " Number of top suppliers to visualize\n", + "\n", + " Returns:\n", + " --------\n", + " fig : Figure\n", + " Matplotlib figure object containing the visualization\n", + " \"\"\"\n", + " # Take top N suppliers\n", + " plot_df = growth_df.head(top_n)\n", + "\n", + " # Create figure\n", + " fig, ax = plt.subplots(figsize=(14, 10))\n", + "\n", + " # Plot horizontal bar chart\n", + " bars = sns.barplot(\n", + " x='Growth Rate (%)',\n", + " y='Supplier Name',\n", + " data=plot_df,\n", + " palette='viridis',\n", + " ax=ax\n", + " )\n", + "\n", + " # Add value labels\n", + " for i, bar in enumerate(bars.patches):\n", + " value = plot_df.iloc[i]['Growth Rate (%)']\n", + " ax.text(\n", + " bar.get_width() + 10,\n", + " bar.get_y() + bar.get_height()/2,\n", + " f\"{value:,.1f}%\",\n", + " ha='left',\n", + " va='center',\n", + " fontweight='bold'\n", + " )\n", + "\n", + " # Add a second x-axis for absolute growth\n", + " ax2 = ax.twiny()\n", + " ax2.set_xlabel('Absolute Growth ($)', color='red')\n", + " ax2.tick_params(axis='x', colors='red')\n", + "\n", + " # Plot absolute growth as scatter points\n", + " for i, (_, row) in enumerate(plot_df.iterrows()):\n", + " ax2.scatter(row['Absolute Growth'], i, color='red', s=100, alpha=0.7)\n", + "\n", + " # Format the x-axis for absolute growth with dollar amounts\n", + " ax2.xaxis.set_major_formatter(\n", + " plt.FuncFormatter(lambda x, pos: f'${x:,.0f}'))\n", + "\n", + " # Set labels and title\n", + " ax.set_xlabel('Growth Rate (%)', fontsize=14)\n", + " ax.set_ylabel('Supplier', fontsize=14)\n", + " ax.set_title(f'Top {top_n} Suppliers by Growth Rate',\n", + " fontsize=16, fontweight='bold')\n", + "\n", + " # Add year period information\n", + " if not plot_df.empty:\n", + " year_periods = plot_df['Year Period'].unique()\n", + " period_str = ', '.join(year_periods)\n", + " ax.text(\n", + " 0.5, 1.05, f\"Year Period(s): {period_str}\", transform=ax.transAxes, ha='center')\n", + "\n", + " # Add grid\n", + " ax.grid(axis='x', linestyle='--', alpha=0.7)\n", + "\n", + " plt.tight_layout()\n", + " return fig\n", + "\n", + "\n", + "def plot_geographic_fraud_hotspots(df_by_year, growth_df, threshold=200):\n", + " \"\"\"\n", + " Create a visualization of geographical hotspots for high-growth suppliers.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df_by_year : dict\n", + " Dictionary containing DataFrames by year\n", + " growth_df : DataFrame\n", + " DataFrame from detect_high_growth_suppliers function\n", + " threshold : float\n", + " Growth rate threshold for inclusion in hotspots\n", + "\n", + " Returns:\n", + " --------\n", + " fig : Figure\n", + " Matplotlib figure object containing the visualization\n", + " \"\"\"\n", + " # Filter for extremely high growth rates\n", + " high_growth_df = growth_df[growth_df['Growth Rate (%)'] > threshold].copy()\n", + "\n", + " # Group by state and count unique suppliers\n", + " state_counts = high_growth_df.groupby('Supplier State').agg({\n", + " 'Supplier NPI': 'nunique',\n", + " 'Growth Rate (%)': 'mean',\n", + " 'Absolute Growth': 'sum'\n", + " }).reset_index()\n", + "\n", + " state_counts.columns = ['State', 'Suspicious Suppliers',\n", + " 'Average Growth Rate (%)', 'Total Growth ($)']\n", + "\n", + " # Sort by count (descending)\n", + " state_counts = state_counts.sort_values(\n", + " 'Suspicious Suppliers', ascending=False)\n", + "\n", + " # Create figure with two subplots\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 10))\n", + "\n", + " # Plot supplier counts\n", + " sns.barplot(\n", + " x='Suspicious Suppliers',\n", + " y='State',\n", + " data=state_counts.head(15),\n", + " palette='Reds_r',\n", + " ax=ax1\n", + " )\n", + "\n", + " ax1.set_xlabel('Number of Suspicious Suppliers', fontsize=14)\n", + " ax1.set_ylabel('State', fontsize=14)\n", + " ax1.set_title('States with Highest Number of Suspicious Suppliers',\n", + " fontsize=16, fontweight='bold')\n", + " ax1.grid(axis='x', linestyle='--', alpha=0.7)\n", + "\n", + " # Plot total growth by state\n", + " sns.barplot(\n", + " x='Total Growth ($)',\n", + " y='State',\n", + " data=state_counts.head(15),\n", + " palette='Blues_r',\n", + " ax=ax2\n", + " )\n", + "\n", + " # Format with dollar amounts\n", + " ax2.xaxis.set_major_formatter(\n", + " plt.FuncFormatter(lambda x, pos: f'${x:,.0f}'))\n", + "\n", + " ax2.set_xlabel('Total Suspicious Growth ($)', fontsize=14)\n", + " ax2.set_ylabel('', fontsize=14) # No y-label for second plot\n", + " ax2.set_title('States with Highest Suspicious Dollar Growth',\n", + " fontsize=16, fontweight='bold')\n", + " ax2.grid(axis='x', linestyle='--', alpha=0.7)\n", + "\n", + " plt.tight_layout()\n", + " return fig\n", + "\n", + "\n", + "def plot_outlier_claim_patterns(outlier_df, metric='DME_Avg_Sbmtd_Chrg', top_n=20):\n", + " \"\"\"\n", + " Create a visualization of suppliers with outlier claim patterns.\n", + "\n", + " Parameters:\n", + " -----------\n", + " outlier_df : DataFrame\n", + " DataFrame from detect_outlier_claim_amounts function\n", + " metric : str\n", + " The metric being analyzed\n", + " top_n : int\n", + " Number of top suppliers to visualize\n", + "\n", + " Returns:\n", + " --------\n", + " fig : Figure\n", + " Matplotlib figure object containing the visualization\n", + " \"\"\"\n", + " # Take top N suppliers\n", + " plot_df = outlier_df.head(top_n)\n", + "\n", + " # Create figure\n", + " fig, ax = plt.subplots(figsize=(14, 10))\n", + "\n", + " # Plot horizontal bar chart\n", + " bars = sns.barplot(\n", + " x=metric,\n", + " y='Suplr_Prvdr_Org_Name',\n", + " data=plot_df,\n", + " palette='plasma',\n", + " ax=ax\n", + " )\n", + "\n", + " # Add z-score labels\n", + " for i, bar in enumerate(bars.patches):\n", + " zscore = plot_df.iloc[i][f'{metric}_zscore']\n", + " ax.text(\n", + " bar.get_width() + 5,\n", + " bar.get_y() + bar.get_height()/2,\n", + " f\"z={zscore:.1f}\",\n", + " ha='left',\n", + " va='center',\n", + " fontweight='bold'\n", + " )\n", + "\n", + " # Format the x-axis with dollar amounts if it's a monetary metric\n", + " if 'Pymt' in metric or 'Chrg' in metric or 'Amt' in metric:\n", + " ax.xaxis.set_major_formatter(\n", + " plt.FuncFormatter(lambda x, pos: f'${x:,.0f}'))\n", + "\n", + " # Set labels and title\n", + " metric_name = metric.replace('_', ' ')\n", + " ax.set_xlabel(metric_name, fontsize=14)\n", + " ax.set_ylabel('Supplier', fontsize=14)\n", + " ax.set_title(f'Top {top_n} Suppliers with Unusually High {metric_name}',\n", + " fontsize=16, fontweight='bold')\n", + "\n", + " # Add grid\n", + " ax.grid(axis='x', linestyle='--', alpha=0.7)\n", + "\n", + " plt.tight_layout()\n", + " return fig\n", + "\n", + "\n", + "def plot_beneficiary_claim_ratio_outliers(ratio_df, top_n=20):\n", + " \"\"\"\n", + " Create a visualization of suppliers with unusual beneficiary-to-claim ratios.\n", + "\n", + " Parameters:\n", + " -----------\n", + " ratio_df : DataFrame\n", + " DataFrame from detect_unusual_beneficiary_to_claim_ratio function\n", + " top_n : int\n", + " Number of top suppliers to visualize\n", + "\n", + " Returns:\n", + " --------\n", + " fig : Figure\n", + " Matplotlib figure object containing the visualization\n", + " \"\"\"\n", + " # Take top N suppliers\n", + " plot_df = ratio_df.head(top_n)\n", + "\n", + " # Create figure\n", + " fig, ax = plt.subplots(figsize=(14, 10))\n", + "\n", + " # Plot horizontal bar chart\n", + " bars = sns.barplot(\n", + " x='Claims_Per_Beneficiary',\n", + " y='Suplr_Prvdr_Org_Name',\n", + " data=plot_df,\n", + " palette='magma',\n", + " ax=ax\n", + " )\n", + "\n", + " # Add z-score labels\n", + " for i, bar in enumerate(bars.patches):\n", + " zscore = plot_df.iloc[i]['Claims_Per_Beneficiary_zscore']\n", + " ax.text(\n", + " bar.get_width() + 0.5,\n", + " bar.get_y() + bar.get_height()/2,\n", + " f\"z={zscore:.1f}\",\n", + " ha='left',\n", + " va='center',\n", + " fontweight='bold'\n", + " )\n", + "\n", + " # Set labels and title\n", + " ax.set_xlabel('Claims Per Beneficiary', fontsize=14)\n", + " ax.set_ylabel('Supplier', fontsize=14)\n", + " ax.set_title(f'Top {top_n} Suppliers with Unusually High Claims Per Beneficiary',\n", + " fontsize=16, fontweight='bold')\n", + "\n", + " # Add grid\n", + " ax.grid(axis='x', linestyle='--', alpha=0.7)\n", + "\n", + " plt.tight_layout()\n", + " return fig\n", + "\n", + "\n", + "def plot_combined_fraud_indicators(growth_df, outlier_df, ratio_df, top_n=20):\n", + " \"\"\"\n", + " Create a visualization of suppliers that appear in multiple fraud indicator lists.\n", + "\n", + " Parameters:\n", + " -----------\n", + " growth_df : DataFrame\n", + " DataFrame from detect_high_growth_suppliers function\n", + " outlier_df : DataFrame\n", + " DataFrame from detect_outlier_claim_amounts function\n", + " ratio_df : DataFrame\n", + " DataFrame from detect_unusual_beneficiary_to_claim_ratio function\n", + " top_n : int\n", + " Number of top suppliers to visualize\n", + "\n", + " Returns:\n", + " --------\n", + " fig : Figure\n", + " Matplotlib figure object containing the visualization\n", + " \"\"\"\n", + " print(\"Identifying suppliers with multiple fraud indicators...\")\n", + "\n", + " # Check if any of the dataframes are empty\n", + " if growth_df.empty or outlier_df.empty or ratio_df.empty:\n", + " print(\"Warning: One or more fraud indicator dataframes are empty. Cannot perform combined analysis.\")\n", + " # Return an empty plot\n", + " fig, ax = plt.subplots(figsize=(15, 12))\n", + " ax.text(0.5, 0.5, \"Insufficient data for combined fraud indicator analysis\",\n", + " ha='center', va='center', fontsize=14)\n", + " return fig, pd.DataFrame()\n", + "\n", + " # Get unique supplier NPIs from each DataFrame\n", + " growth_npis = set(growth_df['Supplier NPI'])\n", + " outlier_npis = set(outlier_df['Suplr_NPI'])\n", + " ratio_npis = set(ratio_df['Suplr_NPI'])\n", + "\n", + " # Find suppliers that appear in multiple lists\n", + " suspicious_suppliers = []\n", + "\n", + " # Check all 3 indicators\n", + " all_three = growth_npis & outlier_npis & ratio_npis\n", + " for npi in all_three:\n", + " try:\n", + " growth_row = growth_df[growth_df['Supplier NPI'] == npi].iloc[0]\n", + " outlier_row = outlier_df[outlier_df['Suplr_NPI'] == npi].iloc[0]\n", + " ratio_row = ratio_df[ratio_df['Suplr_NPI'] == npi].iloc[0]\n", + "\n", + " suspicious_suppliers.append({\n", + " 'NPI': npi,\n", + " 'Supplier Name': growth_row['Supplier Name'],\n", + " 'State': growth_row['Supplier State'],\n", + " 'Growth Rate (%)': growth_row['Growth Rate (%)'],\n", + " 'Claims Per Beneficiary': ratio_row['Claims_Per_Beneficiary'],\n", + " # Use index to get the charge column\n", + " 'Avg Charge': outlier_row[outlier_row.columns[3]],\n", + " 'Indicators': 3,\n", + " 'Flags': 'High Growth, High Charges, High Claims/Beneficiary'\n", + " })\n", + " except (IndexError, KeyError) as e:\n", + " print(\n", + " f\"Error processing supplier {npi} with all three indicators: {str(e)}\")\n", + " continue\n", + "\n", + " # Check growth + outlier\n", + " growth_outlier = (growth_npis & outlier_npis) - all_three\n", + " for npi in growth_outlier:\n", + " try:\n", + " growth_row = growth_df[growth_df['Supplier NPI'] == npi].iloc[0]\n", + " outlier_row = outlier_df[outlier_df['Suplr_NPI'] == npi].iloc[0]\n", + "\n", + " suspicious_suppliers.append({\n", + " 'NPI': npi,\n", + " 'Supplier Name': growth_row['Supplier Name'],\n", + " 'State': growth_row['Supplier State'],\n", + " 'Growth Rate (%)': growth_row['Growth Rate (%)'],\n", + " 'Claims Per Beneficiary': 0,\n", + " # Use index to get the charge column\n", + " 'Avg Charge': outlier_row[outlier_row.columns[3]],\n", + " 'Indicators': 2,\n", + " 'Flags': 'High Growth, High Charges'\n", + " })\n", + " except (IndexError, KeyError) as e:\n", + " print(\n", + " f\"Error processing supplier {npi} with growth+outlier indicators: {str(e)}\")\n", + " continue\n", + "\n", + " # Check growth + ratio\n", + " growth_ratio = (growth_npis & ratio_npis) - all_three\n", + " for npi in growth_ratio:\n", + " try:\n", + " growth_row = growth_df[growth_df['Supplier NPI'] == npi].iloc[0]\n", + " ratio_row = ratio_df[ratio_df['Suplr_NPI'] == npi].iloc[0]\n", + "\n", + " suspicious_suppliers.append({\n", + " 'NPI': npi,\n", + " 'Supplier Name': growth_row['Supplier Name'],\n", + " 'State': growth_row['Supplier State'],\n", + " 'Growth Rate (%)': growth_row['Growth Rate (%)'],\n", + " 'Claims Per Beneficiary': ratio_row['Claims_Per_Beneficiary'],\n", + " 'Avg Charge': 0,\n", + " 'Indicators': 2,\n", + " 'Flags': 'High Growth, High Claims/Beneficiary'\n", + " })\n", + " except (IndexError, KeyError) as e:\n", + " print(\n", + " f\"Error processing supplier {npi} with growth+ratio indicators: {str(e)}\")\n", + " continue\n", + "\n", + " # Check outlier + ratio\n", + " outlier_ratio = (outlier_npis & ratio_npis) - all_three\n", + " for npi in outlier_ratio:\n", + " try:\n", + " outlier_row = outlier_df[outlier_df['Suplr_NPI'] == npi].iloc[0]\n", + " ratio_row = ratio_df[ratio_df['Suplr_NPI'] == npi].iloc[0]\n", + "\n", + " suspicious_suppliers.append({\n", + " 'NPI': npi,\n", + " 'Supplier Name': outlier_row['Suplr_Prvdr_Org_Name'],\n", + " 'State': outlier_row['Suplr_Prvdr_State_Abrvtn'],\n", + " 'Growth Rate (%)': 0,\n", + " 'Claims Per Beneficiary': ratio_row['Claims_Per_Beneficiary'],\n", + " # Use index to get the charge column\n", + " 'Avg Charge': outlier_row[outlier_row.columns[3]],\n", + " 'Indicators': 2,\n", + " 'Flags': 'High Charges, High Claims/Beneficiary'\n", + " })\n", + " except (IndexError, KeyError) as e:\n", + " print(\n", + " f\"Error processing supplier {npi} with outlier+ratio indicators: {str(e)}\")\n", + " continue\n", + "\n", + " # Convert to DataFrame\n", + " combined_df = pd.DataFrame(suspicious_suppliers)\n", + "\n", + " # If no suppliers found with multiple indicators, return empty\n", + " if combined_df.empty:\n", + " print(\"No suppliers found with multiple fraud indicators.\")\n", + " fig, ax = plt.subplots(figsize=(15, 12))\n", + " ax.text(0.5, 0.5, \"No suppliers identified with multiple fraud indicators\",\n", + " ha='center', va='center', fontsize=14)\n", + " return fig, combined_df\n", + "\n", + " # Sort by number of indicators (descending), then by growth rate\n", + " combined_df = combined_df.sort_values(\n", + " ['Indicators', 'Growth Rate (%)'], ascending=[False, False])\n", + "\n", + " # Take top N suppliers\n", + " plot_df = combined_df.head(top_n)\n", + "\n", + " # Create figure\n", + " fig, ax = plt.subplots(figsize=(15, 12))\n", + "\n", + " # Plot horizontal bar chart, colored by number of indicators\n", + " scatter = ax.scatter(\n", + " plot_df['Growth Rate (%)'],\n", + " range(len(plot_df)),\n", + " c=plot_df['Indicators'],\n", + " cmap='RdYlBu_r',\n", + " s=300,\n", + " alpha=0.7\n", + " )\n", + "\n", + " # Set y-tick labels to supplier names\n", + " ax.set_yticks(range(len(plot_df)))\n", + " ax.set_yticklabels(plot_df['Supplier Name'])\n", + "\n", + " # Create a colorbar\n", + " cbar = plt.colorbar(scatter)\n", + " cbar.set_label('Number of Fraud Indicators', rotation=270, labelpad=20)\n", + "\n", + " # Add text labels with flag information\n", + " for i, (_, row) in enumerate(plot_df.iterrows()):\n", + " ax.text(\n", + " row['Growth Rate (%)'] + 5,\n", + " i,\n", + " row['Flags'],\n", + " va='center',\n", + " fontsize=9\n", + " )\n", + "\n", + " # Set labels and title\n", + " ax.set_xlabel('Growth Rate (%)', fontsize=14)\n", + " ax.set_ylabel('Supplier', fontsize=14)\n", + " ax.set_title('Suppliers with Multiple Fraud Indicators',\n", + " fontsize=16, fontweight='bold')\n", + "\n", + " # Add grid\n", + " ax.grid(axis='x', linestyle='--', alpha=0.7)\n", + "\n", + " plt.tight_layout()\n", + " return fig, combined_df\n", + "\n", + "\n", + "def create_fraud_detection_visualizations(df_by_year):\n", + " \"\"\"\n", + " Create all fraud detection visualizations for a Jupyter notebook.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df_by_year : dict\n", + " Dictionary with yearly dataframes\n", + "\n", + " Returns:\n", + " --------\n", + " visualizations : dict\n", + " Dictionary with all visualizations\n", + " data : dict\n", + " Dictionary with all data DataFrames\n", + " \"\"\"\n", + " # Initialize results dictionaries\n", + " visualizations = {}\n", + " data = {}\n", + "\n", + " if not df_by_year:\n", + " print(\"Error: No data provided. Cannot create visualizations.\")\n", + " return {}, {}\n", + "\n", + " # Most recent year\n", + " recent_year = max(df_by_year.keys())\n", + "\n", + " # Display column names for debugging\n", + " print(\"\\nColumn names available in the most recent year's data:\")\n", + " for i, col in enumerate(sorted(df_by_year[recent_year].columns)):\n", + " print(f\" {i+1}. {col}\")\n", + "\n", + " # 1. Detect high growth suppliers\n", + " print(\"\\nDetecting high growth suppliers...\")\n", + " growth_df = detect_high_growth_suppliers(df_by_year, top_n=50)\n", + " data['high_growth_suppliers'] = growth_df\n", + "\n", + " if not growth_df.empty:\n", + " # Create high growth visualization\n", + " growth_fig = plot_high_growth_suppliers(growth_df, top_n=20)\n", + " visualizations['high_growth_suppliers'] = growth_fig\n", + " print(\"✓ High growth suppliers visualization created successfully.\")\n", + " else:\n", + " # Create empty plot\n", + " fig, ax = plt.subplots(figsize=(14, 10))\n", + " ax.text(0.5, 0.5, \"No high growth suppliers identified\",\n", + " ha='center', va='center', fontsize=14)\n", + " visualizations['high_growth_suppliers'] = fig\n", + " print(\"⚠ No high growth suppliers identified.\")\n", + "\n", + " # 2. Detect geographic fraud hotspots\n", + " print(\"\\nDetecting geographic fraud hotspots...\")\n", + " if not growth_df.empty:\n", + " hotspots_fig = plot_geographic_fraud_hotspots(df_by_year, growth_df)\n", + " visualizations['geographic_hotspots'] = hotspots_fig\n", + " print(\"✓ Geographic hotspots visualization created successfully.\")\n", + " else:\n", + " # Create empty plot\n", + " fig, ax = plt.subplots(figsize=(14, 10))\n", + " ax.text(0.5, 0.5, \"No geographic hotspots identified (no high growth suppliers)\",\n", + " ha='center', va='center', fontsize=14)\n", + " visualizations['geographic_hotspots'] = fig\n", + " print(\"⚠ No geographic hotspots identified (no high growth suppliers).\")\n", + "\n", + " # 3. Detect outlier claim amounts\n", + " print(\"\\nDetecting outlier claim amounts...\")\n", + " outlier_df = detect_outlier_claim_amounts(\n", + " df_by_year[recent_year], recent_year)\n", + " data['outlier_claims'] = outlier_df\n", + "\n", + " if not outlier_df.empty:\n", + " outlier_fig = plot_outlier_claim_patterns(outlier_df)\n", + " visualizations['outlier_claims'] = outlier_fig\n", + " print(\"✓ Outlier claim patterns visualization created successfully.\")\n", + " else:\n", + " # Create empty plot\n", + " fig, ax = plt.subplots(figsize=(14, 10))\n", + " ax.text(0.5, 0.5, \"No outlier claim amounts identified\",\n", + " ha='center', va='center', fontsize=14)\n", + " visualizations['outlier_claims'] = fig\n", + " print(\"⚠ No outlier claim amounts identified.\")\n", + "\n", + " # 4. Detect unusual beneficiary-to-claim ratios\n", + " print(\"\\nDetecting unusual beneficiary-to-claim ratios...\")\n", + " ratio_df = detect_unusual_beneficiary_to_claim_ratio(\n", + " df_by_year[recent_year], recent_year)\n", + " data['unusual_ratios'] = ratio_df\n", + "\n", + " if not ratio_df.empty:\n", + " ratio_fig = plot_beneficiary_claim_ratio_outliers(ratio_df)\n", + " visualizations['unusual_ratios'] = ratio_fig\n", + " print(\"✓ Unusual beneficiary-to-claim ratios visualization created successfully.\")\n", + " else:\n", + " # Create empty plot\n", + " fig, ax = plt.subplots(figsize=(14, 10))\n", + " ax.text(0.5, 0.5, \"No unusual beneficiary-to-claim ratios identified\",\n", + " ha='center', va='center', fontsize=14)\n", + " visualizations['unusual_ratios'] = fig\n", + " print(\"⚠ No unusual beneficiary-to-claim ratios identified.\")\n", + "\n", + " # 5. Combined fraud indicators\n", + " print(\"\\nIdentifying suppliers with multiple fraud indicators...\")\n", + " combined_fig, combined_df = plot_combined_fraud_indicators(\n", + " growth_df, outlier_df, ratio_df)\n", + " visualizations['combined_indicators'] = combined_fig\n", + " data['combined_indicators'] = combined_df\n", + "\n", + " if not combined_df.empty:\n", + " print(\"✓ Combined fraud indicators visualization created successfully.\")\n", + " else:\n", + " print(\"⚠ No suppliers with multiple fraud indicators identified.\")\n", + "\n", + " return visualizations, data\n", + "\n", + "\n", + "def main():\n", + " \"\"\"Main function to import and analyze DME data for fraud detection.\"\"\"\n", + " print(\"DME Fraud Detection Analysis\")\n", + " print(\"===========================\\n\")\n", + "\n", + " # Dictionary to store dataframes by year\n", + " df_by_year = {}\n", + "\n", + " # Import data for years 2017-2022\n", + " for year in range(2017, 2023):\n", + " csv_path = f\"data/{year}/mup_dme_ry24_p05_v10_dy{str(year)[-2:]}_supr.csv\"\n", + " if os.path.exists(csv_path):\n", + " print(f\"Importing data for {year}...\")\n", + " try:\n", + " df = pd.read_csv(csv_path, low_memory=False)\n", + "\n", + " # Convert monetary columns to numeric\n", + " money_columns = [\n", + " col for col in df.columns if 'Pymt' in col or 'Amt' in col]\n", + " for col in money_columns:\n", + " if col in df.columns:\n", + " df[col] = pd.to_numeric(df[col], errors='coerce')\n", + "\n", + " df_by_year[year] = df\n", + " print(\n", + " f\"✓ Data for {year} imported successfully. Shape: {df.shape}\")\n", + " except Exception as e:\n", + " print(f\"Error importing data for {year}: {str(e)}\")\n", + " else:\n", + " print(f\"Warning: No data file found for {year}\")\n", + "\n", + " if not df_by_year:\n", + " print(\"\\nError: No data files were successfully imported. Cannot proceed with analysis.\")\n", + " return {}, {}, {}\n", + "\n", + " print(f\"\\n{len(df_by_year)} year(s) of data imported.\")\n", + "\n", + " # Print column names from the first year to help diagnose column issues\n", + " first_year = min(df_by_year.keys())\n", + " print(f\"\\nSample column names from {first_year} data:\")\n", + " # Show first 20 columns\n", + " for i, col in enumerate(sorted(df_by_year[first_year].columns)[:20]):\n", + " print(f\" {i+1}. {col}\")\n", + "\n", + " if len(df_by_year[first_year].columns) > 20:\n", + " print(\n", + " f\" ... and {len(df_by_year[first_year].columns) - 20} more columns\")\n", + "\n", + " # ----- FRAUD DETECTION ANALYSIS -----\n", + " print(\"\\n1. High Growth Rate Analysis\")\n", + " print(\"--------------------------\\n\")\n", + "\n", + " # Detect suppliers with abnormally high growth rates\n", + " growth_df = detect_high_growth_suppliers(df_by_year, top_n=50)\n", + "\n", + " if growth_df.empty:\n", + " print(\"No suppliers with high growth rates detected. Cannot proceed with this part of the analysis.\")\n", + " else:\n", + " # Print summary of top 15 high-growth suppliers\n", + " print(\"Top 15 suppliers with highest growth rates:\")\n", + "\n", + " # Format the output for display\n", + " formatted_growth_df = growth_df.head(15).copy()\n", + " formatted_growth_df['Growth Rate (%)'] = formatted_growth_df['Growth Rate (%)'].apply(\n", + " lambda x: f\"{x:.2f}%\")\n", + " formatted_growth_df['Start Year Value'] = formatted_growth_df['Start Year Value'].apply(\n", + " lambda x: f\"${x:,.2f}\")\n", + " formatted_growth_df['End Year Value'] = formatted_growth_df['End Year Value'].apply(\n", + " lambda x: f\"${x:,.2f}\")\n", + " formatted_growth_df['Absolute Growth'] = formatted_growth_df['Absolute Growth'].apply(\n", + " lambda x: f\"${x:,.2f}\")\n", + "\n", + " print(formatted_growth_df.to_string(index=False))\n", + "\n", + " # ----- GEOGRAPHIC ANALYSIS -----\n", + " print(\"\\n2. Geographic Fraud Hotspots\")\n", + " print(\"-------------------------\\n\")\n", + "\n", + " # Group high growth suppliers by state\n", + " high_growth_states = growth_df.groupby('Supplier State').agg({\n", + " 'Supplier NPI': 'nunique',\n", + " 'Growth Rate (%)': 'mean',\n", + " 'Absolute Growth': 'sum'\n", + " }).reset_index()\n", + "\n", + " high_growth_states.columns = [\n", + " 'State', 'Suspicious Suppliers', 'Average Growth Rate (%)', 'Total Growth ($)']\n", + " high_growth_states = high_growth_states.sort_values(\n", + " 'Suspicious Suppliers', ascending=False)\n", + "\n", + " print(\"States with highest number of suspicious suppliers:\")\n", + "\n", + " # Format for display\n", + " formatted_states = high_growth_states.head(10).copy()\n", + " formatted_states['Average Growth Rate (%)'] = formatted_states['Average Growth Rate (%)'].apply(\n", + " lambda x: f\"{x:.2f}%\")\n", + " formatted_states['Total Growth ($)'] = formatted_states['Total Growth ($)'].apply(\n", + " lambda x: f\"${x:,.2f}\")\n", + "\n", + " print(formatted_states.to_string(index=False))\n", + "\n", + " # ----- OUTLIER CLAIM ANALYSIS -----\n", + " print(\"\\n3. Outlier Claim Amount Analysis\")\n", + " print(\"-----------------------------\\n\")\n", + "\n", + " # Most recent year\n", + " recent_year = max(df_by_year.keys())\n", + "\n", + " # Detect suppliers with outlier claim amounts\n", + " outlier_df = detect_outlier_claim_amounts(\n", + " df_by_year[recent_year], recent_year)\n", + "\n", + " if outlier_df.empty:\n", + " print(\n", + " f\"No suppliers with outlier claim amounts detected in {recent_year}.\")\n", + " else:\n", + " print(f\"Top 10 suppliers with outlier claim amounts in {recent_year}:\")\n", + "\n", + " # Format for display\n", + " try:\n", + " formatted_outliers = outlier_df.head(10).copy()\n", + "\n", + " # Use the metric column name (4th column)\n", + " metric_col = outlier_df.columns[3]\n", + " zscore_col = outlier_df.columns[4]\n", + "\n", + " # Format monetary values with dollar signs\n", + " if 'Pymt' in metric_col or 'Chrg' in metric_col or 'Amt' in metric_col:\n", + " formatted_outliers[metric_col] = formatted_outliers[metric_col].apply(\n", + " lambda x: f\"${x:,.2f}\")\n", + "\n", + " formatted_outliers[zscore_col] = formatted_outliers[zscore_col].apply(\n", + " lambda x: f\"{x:.2f}\")\n", + "\n", + " print(formatted_outliers.to_string(index=False))\n", + " except Exception as e:\n", + " print(f\"Error formatting outlier data: {str(e)}\")\n", + " print(\"Raw data:\")\n", + " print(outlier_df.head(10))\n", + "\n", + " # ----- BENEFICIARY-CLAIM RATIO ANALYSIS -----\n", + " print(\"\\n4. Unusual Beneficiary-to-Claim Ratio Analysis\")\n", + " print(\"-----------------------------------------\\n\")\n", + "\n", + " # Detect suppliers with unusual beneficiary-to-claim ratios\n", + " ratio_df = detect_unusual_beneficiary_to_claim_ratio(\n", + " df_by_year[recent_year], recent_year)\n", + "\n", + " if ratio_df.empty:\n", + " print(\n", + " f\"No suppliers with unusual claims per beneficiary detected in {recent_year}.\")\n", + " else:\n", + " print(\n", + " f\"Top 10 suppliers with unusual claims per beneficiary in {recent_year}:\")\n", + "\n", + " # Format for display\n", + " try:\n", + " formatted_ratios = ratio_df.head(10).copy()\n", + " formatted_ratios['Claims_Per_Beneficiary'] = formatted_ratios['Claims_Per_Beneficiary'].apply(\n", + " lambda x: f\"{x:.2f}\")\n", + " formatted_ratios['Claims_Per_Beneficiary_zscore'] = formatted_ratios['Claims_Per_Beneficiary_zscore'].apply(\n", + " lambda x: f\"{x:.2f}\")\n", + "\n", + " print(formatted_ratios.to_string(index=False))\n", + " except Exception as e:\n", + " print(f\"Error formatting ratio data: {str(e)}\")\n", + " print(\"Raw data:\")\n", + " print(ratio_df.head(10))\n", + "\n", + " # ----- COMBINED FRAUD INDICATORS -----\n", + " print(\"\\n5. Combined Fraud Indicators\")\n", + " print(\"-------------------------\\n\")\n", + "\n", + " # Identify suppliers that appear in multiple fraud indicator lists\n", + " combined_fig, combined_df = plot_combined_fraud_indicators(\n", + " growth_df, outlier_df, ratio_df)\n", + "\n", + " if combined_df.empty:\n", + " print(\"No suppliers with multiple fraud indicators identified.\")\n", + " else:\n", + " print(\"Top 10 suppliers with multiple fraud indicators:\")\n", + "\n", + " # Format for display\n", + " try:\n", + " formatted_combined = combined_df.head(10).copy()\n", + " formatted_combined['Growth Rate (%)'] = formatted_combined['Growth Rate (%)'].apply(\n", + " lambda x: f\"{x:.2f}%\" if x > 0 else \"N/A\")\n", + " formatted_combined['Claims Per Beneficiary'] = formatted_combined['Claims Per Beneficiary'].apply(\n", + " lambda x: f\"{x:.2f}\" if x > 0 else \"N/A\")\n", + " formatted_combined['Avg Charge'] = formatted_combined['Avg Charge'].apply(\n", + " lambda x: f\"${x:,.2f}\" if x > 0 else \"N/A\")\n", + "\n", + " print(formatted_combined.to_string(index=False))\n", + " except Exception as e:\n", + " print(f\"Error formatting combined data: {str(e)}\")\n", + " print(\"Raw data:\")\n", + " print(combined_df.head(10))\n", + "\n", + " # ----- VISUALIZATIONS -----\n", + " print(\"\\n\\n6. Generating Fraud Detection Visualizations\")\n", + " print(\"------------------------------------------\\n\")\n", + "\n", + " # Setting plot style\n", + " sns.set_style('whitegrid')\n", + " plt.rcParams['figure.figsize'] = [14, 9]\n", + "\n", + " # Generate all visualizations\n", + " visualizations, data = create_fraud_detection_visualizations(df_by_year)\n", + "\n", + " # Save visualizations to files if not in a notebook environment\n", + " try:\n", + " # Check if we're in a notebook environment\n", + " if 'ipykernel' not in sys.modules:\n", + " print(\"\\nSaving visualizations to files...\")\n", + " os.makedirs('fraud_visualizations', exist_ok=True)\n", + " for name, fig in visualizations.items():\n", + " fig.savefig(\n", + " f'fraud_visualizations/{name}.png', dpi=300, bbox_inches='tight')\n", + " print(f\"Saved: fraud_visualizations/{name}.png\")\n", + " except Exception as e:\n", + " print(f\"Error saving visualizations: {str(e)}\")\n", + " print(\"Note: Visualizations will be displayed if run in a Jupyter notebook\")\n", + "\n", + " # When run in Jupyter, the figures will be displayed inline\n", + " return df_by_year, visualizations, data\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "PY311LLM", "language": "python", "name": "python3" }, diff --git a/dme_analysis/__init__.py b/dme_analysis/__init__.py new file mode 100644 index 0000000..28843f9 --- /dev/null +++ b/dme_analysis/__init__.py @@ -0,0 +1,8 @@ +""" +DME Analysis +=========== + +This package contains tools for analyzing Medicare DME data. +""" + +__version__ = "0.1.0" diff --git a/dme_analysis/utils/__init__.py b/dme_analysis/utils/__init__.py new file mode 100644 index 0000000..62b2f88 --- /dev/null +++ b/dme_analysis/utils/__init__.py @@ -0,0 +1,17 @@ +""" +DME Analysis Utilities +===================== + +This package contains utilities for DME data analysis. +""" + +from .data_dictionary import DATA_DICTIONARY, get_column_category +from .data_import import import_dme_data, get_column_mapping, import_data_for_years + +__all__ = [ + 'DATA_DICTIONARY', + 'get_column_category', + 'import_dme_data', + 'get_column_mapping', + 'import_data_for_years' +] diff --git a/dme_analysis/utils/data_dictionary.py b/dme_analysis/utils/data_dictionary.py new file mode 100644 index 0000000..a601e37 --- /dev/null +++ b/dme_analysis/utils/data_dictionary.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +DME Data Dictionary +This module contains the data dictionary for the DME dataset. +""" + +# Data dictionary mapping variable names to their descriptions +DATA_DICTIONARY = { + # Supplier Information + 'Suplr_NPI': "National Provider Identifier for the DME supplier", + 'Suplr_Prvdr_Last_Name_Org': "Organization name of the DME supplier", + 'Suplr_Prvdr_First_Name': "First name of the DME supplier (if individual)", + 'Suplr_Prvdr_MI': "Middle initial of the DME supplier (if individual)", + 'Suplr_Prvdr_Crdntls': "Credentials of the DME supplier", + 'Suplr_Prvdr_Gndr': "Gender of the DME supplier (if individual)", + 'Suplr_Prvdr_Ent_Cd': "Entity code of the DME supplier", + 'Suplr_Prvdr_St1': "Street address line 1 of the DME supplier", + 'Suplr_Prvdr_St2': "Street address line 2 of the DME supplier", + 'Suplr_Prvdr_City': "City where the DME supplier is located", + 'Suplr_Prvdr_State_Abrvtn': "State abbreviation where the DME supplier is located", + 'Suplr_Prvdr_State_FIPS': "FIPS code for the state where the DME supplier is located", + 'Suplr_Prvdr_Zip5': "5-digit ZIP code where the DME supplier is located", + 'Suplr_Prvdr_Cntry': "Country where the DME supplier is located", + 'Suplr_Prvdr_RUCA': "Rural-Urban Commuting Area code for the DME supplier", + 'Suplr_Prvdr_RUCA_Desc': "Description of the Rural-Urban Commuting Area for the DME supplier", + 'Suplr_Prvdr_Spclty_Desc': "Specialty description of the DME supplier", + 'Suplr_Prvdr_Spclty_Srce': "Source of the specialty information", + + # DME-specific fields + 'DME_Sprsn_Ind': "Indicator for suppression of DME data (Y/N)", + 'DME_Tot_Suplr_Benes': "Total number of beneficiaries served by the supplier for DME", + 'DME_Tot_Suplr_Clms': "Total number of claims submitted by the supplier for DME", + 'DME_Tot_Suplr_Srvcs': "Total number of services provided by the supplier for DME", + 'DME_Tot_Suplr_HCPCS_Cds': "Total number of unique HCPCS codes billed by the supplier for DME", + 'DME_Suplr_Sbmtd_Chrgs': "Total submitted charges by the supplier for DME", + 'DME_Suplr_Mdcr_Alowd_Amt': "Total Medicare allowed amount for the supplier for DME", + 'DME_Suplr_Mdcr_Pymt_Amt': "Total Medicare payment amount to the supplier for DME", + 'DME_Suplr_Mdcr_Stdzd_Pymt_Amt': "Total Medicare standardized payment amount to the supplier for DME", + + # Prosthetic and Orthotic fields + 'POS_Sprsn_Ind': "Indicator for suppression of POS data (Y/N)", + 'POS_Tot_Suplr_Benes': "Total number of beneficiaries served by the supplier for POS", + 'POS_Tot_Suplr_Clms': "Total number of claims submitted by the supplier for POS", + 'POS_Tot_Suplr_Srvcs': "Total number of services provided by the supplier for POS", + 'POS_Tot_Suplr_HCPCS_Cds': "Total number of unique HCPCS codes billed by the supplier for POS", + 'POS_Suplr_Sbmtd_Chrgs': "Total submitted charges by the supplier for POS", + 'POS_Suplr_Mdcr_Alowd_Amt': "Total Medicare allowed amount for the supplier for POS", + 'POS_Suplr_Mdcr_Pymt_Amt': "Total Medicare payment amount to the supplier for POS", + 'POS_Suplr_Mdcr_Stdzd_Pymt_Amt': "Total Medicare standardized payment amount to the supplier for POS", + + # Drug and Nutritional fields + 'Drug_Sprsn_Ind': "Indicator for suppression of Drug data (Y/N)", + 'Drug_Tot_Suplr_Benes': "Total number of beneficiaries served by the supplier for Drug", + 'Drug_Tot_Suplr_Clms': "Total number of claims submitted by the supplier for Drug", + 'Drug_Tot_Suplr_Srvcs': "Total number of services provided by the supplier for Drug", + 'Drug_Tot_Suplr_HCPCS_Cds': "Total number of unique HCPCS codes billed by the supplier for Drug", + 'Drug_Suplr_Sbmtd_Chrgs': "Total submitted charges by the supplier for Drug", + 'Drug_Suplr_Mdcr_Alowd_Amt': "Total Medicare allowed amount for the supplier for Drug", + 'Drug_Suplr_Mdcr_Pymt_Amt': "Total Medicare payment amount to the supplier for Drug", + 'Drug_Suplr_Mdcr_Stdzd_Pymt_Amt': "Total Medicare standardized payment amount to the supplier for Drug", + + # Overall supplier fields + 'Tot_Suplr_Benes': "Total number of beneficiaries served by the supplier overall", + 'Tot_Suplr_Clms': "Total number of claims submitted by the supplier overall", + 'Tot_Suplr_Srvcs': "Total number of services provided by the supplier overall", + 'Tot_Suplr_HCPCS_Cds': "Total number of unique HCPCS codes billed by the supplier overall", + 'Suplr_Sbmtd_Chrgs': "Total submitted charges by the supplier overall", + 'Suplr_Mdcr_Alowd_Amt': "Total Medicare allowed amount for the supplier overall", + 'Suplr_Mdcr_Pymt_Amt': "Total Medicare payment amount to the supplier overall", + 'Suplr_Mdcr_Stdzd_Pymt_Amt': "Total Medicare standardized payment amount to the supplier overall", + + # Beneficiary Demographics + 'Bene_Avg_Age': "Average age of beneficiaries served by this supplier", + 'Bene_Age_LT_65_Cnt': "Count of beneficiaries under 65 years of age", + 'Bene_Age_65_74_Cnt': "Count of beneficiaries 65-74 years of age", + 'Bene_Age_75_84_Cnt': "Count of beneficiaries 75-84 years of age", + 'Bene_Age_GT_84_Cnt': "Count of beneficiaries greater than 84 years of age", + 'Bene_Male_Cnt': "Count of male beneficiaries", + 'Bene_Feml_Cnt': "Count of female beneficiaries", + 'Bene_Race_Wht_Cnt': "Count of white beneficiaries", + 'Bene_Race_Black_Cnt': "Count of Black or African American beneficiaries", + 'Bene_Race_Api_Cnt': "Count of Asian/Pacific Islander beneficiaries", + 'Bene_Race_Hspnc_Cnt': "Count of Hispanic beneficiaries", + 'Bene_Race_Natind_Cnt': "Count of Native American/Alaska Native beneficiaries", + 'Bene_Race_Othr_Cnt': "Count of beneficiaries of other races", + 'Bene_Dual_Cnt': "Count of dual-eligible beneficiaries (Medicare and Medicaid)", + 'Bene_Ndual_Cnt': "Count of non-dual-eligible beneficiaries", + 'Bene_Avg_Risk_Scre': "Average risk score of beneficiaries", + + # Health Conditions + 'Bene_CC_PH_Hypertension_V2_Pct': "Percentage of beneficiaries with hypertension", + 'Bene_CC_PH_Hyperlipidemia_V2_Pct': "Percentage of beneficiaries with hyperlipidemia", + 'Bene_CC_PH_Diabetes_V2_Pct': "Percentage of beneficiaries with diabetes", + 'Bene_CC_PH_Arthritis_V2_Pct': "Percentage of beneficiaries with arthritis", + 'Bene_CC_PH_IschemicHeart_V2_Pct': "Percentage of beneficiaries with ischemic heart disease", + 'Bene_CC_PH_COPD_V2_Pct': "Percentage of beneficiaries with COPD", + 'Bene_CC_PH_CKD_V2_Pct': "Percentage of beneficiaries with chronic kidney disease", + 'Bene_CC_PH_Cancer6_V2_Pct': "Percentage of beneficiaries with cancer", + 'Bene_CC_PH_Asthma_V2_Pct': "Percentage of beneficiaries with asthma", + 'Bene_CC_PH_Afib_V2_Pct': "Percentage of beneficiaries with atrial fibrillation", + 'Bene_CC_PH_HF_NonIHD_V2_Pct': "Percentage of beneficiaries with heart failure", + 'Bene_CC_PH_Stroke_TIA_V2_Pct': "Percentage of beneficiaries with stroke/TIA", + 'Bene_CC_PH_Osteoporosis_V2_Pct': "Percentage of beneficiaries with osteoporosis", + 'Bene_CC_PH_Parkinson_V2_Pct': "Percentage of beneficiaries with Parkinson's disease", + 'Bene_CC_BH_Mood_V2_Pct': "Percentage of beneficiaries with mood disorders", + 'Bene_CC_BH_Depress_V1_Pct': "Percentage of beneficiaries with depression", + 'Bene_CC_BH_Anxiety_V1_Pct': "Percentage of beneficiaries with anxiety", + 'Bene_CC_BH_Tobacco_V1_Pct': "Percentage of beneficiaries with tobacco use disorder", + 'Bene_CC_BH_Alz_NonAlzdem_V2_Pct': "Percentage of beneficiaries with Alzheimer's/dementia", + 'Bene_CC_BH_Schizo_OthPsy_V1_Pct': "Percentage of beneficiaries with schizophrenia or other psychotic disorders", + 'Bene_CC_BH_Alcohol_Drug_V1_Pct': "Percentage of beneficiaries with alcohol/drug use disorders", + 'Bene_CC_BH_ADHD_OthCD_V1_Pct': "Percentage of beneficiaries with ADHD", + 'Bene_CC_BH_Bipolar_V1_Pct': "Percentage of beneficiaries with bipolar disorder", + 'Bene_CC_BH_PD_V1_Pct': "Percentage of beneficiaries with personality disorders", + 'Bene_CC_BH_PTSD_V1_Pct': "Percentage of beneficiaries with PTSD" +} + + +def get_column_category(column_name): + """ + Determine the category of a column based on its name. + + Parameters: + ----------- + column_name : str + The name of the column + + Returns: + -------- + category : str + The category of the column + """ + if column_name.startswith('Suplr_Prvdr_'): + return 'Supplier Information' + elif column_name.startswith('Suplr_') and not column_name.startswith('Suplr_Prvdr_'): + return 'Overall Supplier Metrics' + elif column_name.startswith('DME_'): + return 'Durable Medical Equipment' + elif column_name.startswith('POS_'): + return 'Prosthetics and Orthotics' + elif column_name.startswith('Drug_'): + return 'Drug and Nutritional Products' + elif column_name.startswith('Bene_'): + if any(x in column_name for x in ['_CC_', 'Risk']): + return 'Health Conditions' + else: + return 'Beneficiary Demographics' + elif column_name.startswith('Tot_'): + return 'Overall Provider Metrics' + else: + return 'Other' diff --git a/dme_analysis/utils/data_import.py b/dme_analysis/utils/data_import.py new file mode 100644 index 0000000..46f48fb --- /dev/null +++ b/dme_analysis/utils/data_import.py @@ -0,0 +1,155 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +DME Data Import Utilities +This module contains functions for importing DME data. +""" + +import pandas as pd +import numpy as np +import os +from collections import defaultdict + + +def import_dme_data(file_path): + """ + Import and preprocess DME data from a CSV file. + + Parameters: + ----------- + file_path : str + Path to the CSV file containing DME data + + Returns: + -------- + df : DataFrame + Processed DataFrame containing DME data + """ + print(f"Importing data from {file_path}...") + + try: + # Import data with appropriate dtypes to handle monetary values correctly + df = pd.read_csv(file_path, low_memory=False) + + # Convert monetary columns to numeric + money_columns = [col for col in df.columns if any( + x in col for x in ['Pymt', 'Amt', 'Chrgs'])] + for col in money_columns: + if col in df.columns: + df[col] = pd.to_numeric(df[col], errors='coerce') + + print(f"Successfully imported data with shape: {df.shape}") + return df + + except Exception as e: + print(f"Error importing data: {str(e)}") + return None + + +def get_column_mapping(df): + """ + Get a mapping of expected column names to actual column names in the DataFrame. + This helps handle variations in column names across different datasets. + + Parameters: + ----------- + df : DataFrame + DataFrame to inspect for column names + + Returns: + -------- + column_map : dict + Dictionary mapping expected column names to actual column names + """ + column_map = {} + + # Map for supplier organization name + if 'Suplr_Prvdr_Last_Name_Org' in df.columns: + column_map['supplier_name'] = 'Suplr_Prvdr_Last_Name_Org' + elif 'Suplr_Prvdr_Org_Name' in df.columns: + column_map['supplier_name'] = 'Suplr_Prvdr_Org_Name' + elif 'Suplr_Name' in df.columns: + column_map['supplier_name'] = 'Suplr_Name' + elif 'Supplier_Name' in df.columns: + column_map['supplier_name'] = 'Supplier_Name' + else: + # If no suitable column exists, create a placeholder + print("Warning: No supplier name column found. Using placeholder names.") + column_map['supplier_name'] = None + + # Map for supplier state + if 'Suplr_Prvdr_State_Abrvtn' in df.columns: + column_map['supplier_state'] = 'Suplr_Prvdr_State_Abrvtn' + elif 'Suplr_State' in df.columns: + column_map['supplier_state'] = 'Suplr_State' + elif 'State' in df.columns: + column_map['supplier_state'] = 'State' + else: + print("Warning: No supplier state column found. Using placeholder.") + column_map['supplier_state'] = None + + # Map for supplier NPI + if 'Suplr_NPI' in df.columns: + column_map['supplier_npi'] = 'Suplr_NPI' + elif 'NPI' in df.columns: + column_map['supplier_npi'] = 'NPI' + elif 'Provider_NPI' in df.columns: + column_map['supplier_npi'] = 'Provider_NPI' + else: + print("Warning: No NPI column found. Using index as placeholder.") + column_map['supplier_npi'] = None + + # Check if key columns are missing + if None in column_map.values(): + print("\nAvailable columns in the dataset:") + # Show first 20 columns + for i, col in enumerate(sorted(df.columns)[:20]): + print(f" {i+1}. {col}") + + if len(df.columns) > 20: + print(f" ... and {len(df.columns) - 20} more columns") + + return column_map + + +def import_data_for_years(years_range, base_path="data"): + """ + Import data for multiple years. + + Parameters: + ----------- + years_range : range or list + Range or list of years to import (e.g., range(2017, 2023)) + base_path : str + Base path where data files are stored + + Returns: + -------- + df_by_year : dict + Dictionary with years as keys and DataFrames as values + """ + df_by_year = {} + + for year in years_range: + # Try different file name patterns + file_patterns = [ + f"{base_path}/{year}/mup_dme_ry24_p05_v10_dy{str(year)[-2:]}_supr.csv", + f"{base_path}/dme_data_{year}.csv", + f"{base_path}/{year}/dme_data_{year}.csv", + f"dme_data_{year}.csv" + ] + + file_found = False + for file_path in file_patterns: + if os.path.exists(file_path): + df = import_dme_data(file_path) + if df is not None: + df_by_year[year] = df + file_found = True + break + + if not file_found: + print(f"Warning: No data file found for {year}") + + return df_by_year diff --git a/dme_data_analysis.py b/dme_data_analysis.py index c2b29aa..c17bce4 100644 --- a/dme_data_analysis.py +++ b/dme_data_analysis.py @@ -15,176 +15,12 @@ import seaborn as sns import sys - -def import_dme_data(file_path): - """ - Import and preprocess DME data from a CSV file. - - Parameters: - ----------- - file_path : str - Path to the CSV file containing DME data - - Returns: - -------- - df : DataFrame - Processed DataFrame containing DME data - """ - print(f"Importing data from {file_path}...") - - try: - # Import data with appropriate dtypes to handle monetary values correctly - df = pd.read_csv(file_path, low_memory=False) - - # Convert monetary columns to numeric - money_columns = [ - col for col in df.columns if 'Pymt' in col or 'Amt' in col] - for col in money_columns: - if col in df.columns: - df[col] = pd.to_numeric(df[col], errors='coerce') - - print(f"Successfully imported data with shape: {df.shape}") - return df - - except Exception as e: - print(f"Error importing data: {str(e)}") - return None - - -# Data dictionary mapping variable names to their descriptions -DATA_DICTIONARY = { - # Supplier Information - "Suplr_NPI": "Supplier NPI - NPI for the Supplier on the DMEPOS claim", - "Suplr_Prvdr_Last_Name_Org": "Supplier Last Name/Organization Name - When registered as individual, the Supplier's last name. When registered as organization, this is the organization name", - "Suplr_Prvdr_First_Name": "Supplier First Name - When registered as individual, the Supplier's first name", - "Suplr_Prvdr_MI": "Supplier Middle Initial - When registered as individual, the Supplier's middle initial", - "Suplr_Prvdr_Crdntls": "Supplier Credentials - When registered as individual, these are the Supplier's credentials", - "Suplr_Prvdr_Gndr": "Supplier Gender - When registered as individual, this is the Supplier's gender", - "Suplr_Prvdr_Ent_Cd": "Supplier Entity Code - 'I' identifies Suppliers registered as individuals, 'O' identifies Suppliers registered as organizations", - "Suplr_Prvdr_St1": "Supplier Street 1 - First line of the Supplier's street address", - "Suplr_Prvdr_St2": "Supplier Street 2 - Second line of the Supplier's street address", - "Suplr_Prvdr_City": "Supplier City - The city where the Supplier is located", - "Suplr_Prvdr_State_Abrvtn": "Supplier State - State postal abbreviation where the Supplier is located", - "Suplr_Prvdr_State_FIPS": "Supplier State FIPS Code - FIPS code for Supplier's state", - "Suplr_Prvdr_Zip5": "Supplier ZIP - The Supplier's ZIP code", - "Suplr_Prvdr_RUCA": "Supplier RUCA - Rural-Urban Commuting Area Code for the Supplier ZIP code", - "Suplr_Prvdr_RUCA_Desc": "Supplier RUCA Description - Description of Rural-Urban Commuting Area (RUCA) Code", - "Suplr_Prvdr_Cntry": "Supplier Country - Country where the Supplier is located", - "Suplr_Prvdr_Spclty_Desc": "Supplier Provider Specialty Description - Derived from Medicare provider/supplier specialty code", - "Suplr_Prvdr_Spclty_Srce": "Supplier Provider Specialty Source - Source of the Supplier Specialty (claims-specialty or NPPES-specialty)", - - # Total Supplier Claims/Services - "Tot_Suplr_HCPCS_Cds": "Number of Supplier HCPCS - Total unique DMEPOS product/service HCPCS codes", - "Tot_Suplr_Benes": "Number of Supplier Beneficiaries - Total unique beneficiaries (<11 are suppressed)", - "Tot_Suplr_Clms": "Number of Supplier Claims - Total DMEPOS claims submitted", - "Tot_Suplr_Srvcs": "Number of Supplier Services - Total DMEPOS products/services rendered", - "Suplr_Sbmtd_Chrgs": "Supplier Submitted Charges - Total charges submitted for DMEPOS products/services", - "Suplr_Mdcr_Alowd_Amt": "Supplier Medicare Allowed Amount - Total Medicare allowed amount", - "Suplr_Mdcr_Pymt_Amt": "Supplier Medicare Payment Amount - Amount Medicare paid after deductible/coinsurance", - "Suplr_Mdcr_Stdzd_Pymt_Amt": "Supplier Medicare Standard Payment Amount - Standardized Medicare payments", - - # DME-specific Fields - "DME_Sprsn_Ind": "Durable Medical Equipment Suppression Indicator - '*'=suppressed (1-10 claims), '#'=counter-suppressed", - "DME_Tot_Suplr_HCPCS_Cds": "Number of DME HCPCS - Total unique DME HCPCS codes", - "DME_Tot_Suplr_Benes": "Number of DME Beneficiaries - Total unique beneficiaries with DME claims (<11 are suppressed)", - "DME_Tot_Suplr_Clms": "Number of DME Claims - Total DME claims submitted", - "DME_Tot_Suplr_Srvcs": "Number of DME Services - Total DME products/services rendered", - "DME_Suplr_Sbmtd_Chrgs": "DME Submitted Charges - Total charges submitted for DME products/services", - "DME_Suplr_Mdcr_Alowd_Amt": "DME Medicare Allowed Amount - Total Medicare allowed amount for DME", - "DME_Suplr_Mdcr_Pymt_Amt": "DME Medicare Payment Amount - Amount Medicare paid for DME after deductible/coinsurance", - "DME_Suplr_Mdcr_Stdzd_Pymt_Amt": "DME Medicare Standard Payment Amount - Standardized Medicare payments for DME", - - # Prosthetic and Orthotic Fields - "POS_Sprsn_Ind": "Prosthetic and Orthotic Suppression Indicator - '*'=suppressed (1-10 claims), '#'=counter-suppressed", - "POS_Tot_Suplr_HCPCS_Cds": "Number of Prosthetic/Orthotic HCPCS - Total unique prosthetic/orthotic HCPCS codes", - "POS_Tot_Suplr_Benes": "Number of Prosthetic/Orthotic Beneficiaries - Total unique beneficiaries", - "POS_Tot_Suplr_Clms": "Number of Prosthetic/Orthotic Claims - Total prosthetic/orthotic claims submitted", - "POS_Tot_Suplr_Srvcs": "Number of Prosthetic/Orthotic Services - Total prosthetic/orthotic products/services", - "POS_Suplr_Sbmtd_Chrgs": "Prosthetic/Orthotic Submitted Charges - Total charges submitted for prosthetic/orthotic", - "POS_Suplr_Mdcr_Alowd_Amt": "Prosthetic/Orthotic Medicare Allowed Amount - Total Medicare allowed amount", - "POS_Suplr_Mdcr_Pymt_Amt": "Prosthetic/Orthotic Medicare Payment Amount - Amount Medicare paid after deductible/coinsurance", - "POS_Suplr_Mdcr_Stdzd_Pymt_Amt": "Prosthetic/Orthotic Medicare Standard Payment Amount - Standardized Medicare payments", - - # Drug and Nutritional Fields - "Drug_Sprsn_Ind": "Drug and Nutritional Suppression Indicator - '*'=suppressed (1-10 claims), '#'=counter-suppressed", - "Drug_Tot_Suplr_HCPCS_Cds": "Number of Drug/Nutritional HCPCS - Total unique drug/nutritional HCPCS codes", - "Drug_Tot_Suplr_Benes": "Number of Drug/Nutritional Beneficiaries - Total unique beneficiaries", - "Drug_Tot_Suplr_Clms": "Number of Drug/Nutritional Claims - Total drug/nutritional claims submitted", - "Drug_Tot_Suplr_Srvcs": "Number of Drug/Nutritional Services - Total drug/nutritional products/services", - "Drug_Suplr_Sbmtd_Chrgs": "Drug/Nutritional Submitted Charges - Total charges submitted for drug/nutritional", - "Drug_Suplr_Mdcr_Alowd_Amt": "Drug/Nutritional Medicare Allowed Amount - Total Medicare allowed amount", - "Drug_Suplr_Mdcr_Pymt_Amt": "Drug/Nutritional Medicare Payment Amount - Amount Medicare paid after deductible/coinsurance", - "Drug_Suplr_Mdcr_Stdzd_Pymt_Amt": "Drug/Nutritional Medicare Standard Payment Amount - Standardized Medicare payments", - - # Beneficiary Demographics - "Bene_Avg_Age": "Average Age of Beneficiaries - Average age at end of calendar year or time of death", - "Bene_Age_LT_65_Cnt": "Number of Beneficiaries <65 - Count of beneficiaries under 65 years old", - "Bene_Age_65_74_Cnt": "Number of Beneficiaries 65-74 - Count of beneficiaries between 65-74 years old", - "Bene_Age_75_84_Cnt": "Number of Beneficiaries 75-84 - Count of beneficiaries between 75-84 years old", - "Bene_Age_GT_84_Cnt": "Number of Beneficiaries >84 - Count of beneficiaries over 84 years old", - "Bene_Feml_Cnt": "Number of Female Beneficiaries - Count of female beneficiaries", - "Bene_Male_Cnt": "Number of Male Beneficiaries - Count of male beneficiaries", - "Bene_Race_Wht_Cnt": "Number of White Beneficiaries - Count of non-Hispanic white beneficiaries", - "Bene_Race_Black_Cnt": "Number of Black Beneficiaries - Count of non-Hispanic Black/African American beneficiaries", - "Bene_Race_Api_Cnt": "Number of Asian/PI Beneficiaries - Count of Asian Pacific Islander beneficiaries", - "Bene_Race_Hspnc_Cnt": "Number of Hispanic Beneficiaries - Count of Hispanic beneficiaries", - "Bene_Race_Natind_Cnt": "Number of Native American/Alaska Native Beneficiaries - Count of American Indian/Alaska Native beneficiaries", - "Bene_Race_Othr_Cnt": "Number of Other Race Beneficiaries - Count of beneficiaries with race not elsewhere classified", - "Bene_Ndual_Cnt": "Number of Medicare & Medicaid Beneficiaries - Count of dual-eligible beneficiaries", - "Bene_Dual_Cnt": "Number of Medicare-Only Beneficiaries - Count of Medicare-only beneficiaries", - - # Beneficiary Health Conditions (Mental/Behavioral Health) - "Bene_CC_BH_ADHD_OthCD_V1_Pct": "Percent with ADHD and Other Conduct Disorders", - "Bene_CC_BH_Alcohol_Drug_V1_Pct": "Percent with Alcohol and Drug Use Disorders", - "Bene_CC_BH_Tobacco_V1_Pct": "Percent with Tobacco Use Disorders", - "Bene_CC_BH_Alz_NonAlzdem_V2_Pct": "Percent with Alzheimer's and Non-Alzheimer's Dementia", - "Bene_CC_BH_Anxiety_V1_Pct": "Percent with Anxiety Disorders", - "Bene_CC_BH_Bipolar_V1_Pct": "Percent with Bipolar Disorder", - "Bene_CC_BH_Mood_V2_Pct": "Percent with Depression, Bipolar or Other Mood Disorders", - "Bene_CC_BH_Depress_V1_Pct": "Percent with Major Depressive Affective Disorder", - "Bene_CC_BH_PD_V1_Pct": "Percent with Personality Disorders", - "Bene_CC_BH_PTSD_V1_Pct": "Percent with Post-Traumatic Stress Disorder", - "Bene_CC_BH_Schizo_OthPsy_V1_Pct": "Percent with Schizophrenia and Other Psychotic Disorders", - - # Beneficiary Health Conditions (Physical Health) - "Bene_CC_PH_Asthma_V2_Pct": "Percent with Asthma", - "Bene_CC_PH_Afib_V2_Pct": "Percent with Atrial Fibrillation and Flutter", - "Bene_CC_PH_Cancer6_V2_Pct": "Percent with Cancer (combined 6 cancer indicators)", - "Bene_CC_PH_CKD_V2_Pct": "Percent with Chronic Kidney Disease", - "Bene_CC_PH_COPD_V2_Pct": "Percent with Chronic Obstructive Pulmonary Disease", - "Bene_CC_PH_Diabetes_V2_Pct": "Percent with Diabetes", - "Bene_CC_PH_HF_NonIHD_V2_Pct": "Percent with Heart Failure and Non-Ischemic Heart Disease", - "Bene_CC_PH_Hyperlipidemia_V2_Pct": "Percent with Hyperlipidemia", - "Bene_CC_PH_Hypertension_V2_Pct": "Percent with Hypertension", - "Bene_CC_PH_IschemicHeart_V2_Pct": "Percent with Ischemic Heart Disease", - "Bene_CC_PH_Osteoporosis_V2_Pct": "Percent with Osteoporosis", - "Bene_CC_PH_Parkinson_V2_Pct": "Percent with Parkinson's Disease", - "Bene_CC_PH_Arthritis_V2_Pct": "Percent with Rheumatoid Arthritis/Osteoarthritis", - "Bene_CC_PH_Stroke_TIA_V2_Pct": "Percent with Stroke/Transient Ischemic Attack", - - # Risk Score - "Bene_Avg_Risk_Scre": "Average HCC Risk Score of Beneficiaries" -} - - -def get_column_category(column_name): - """Return the category for a given column name based on prefix.""" - if column_name.startswith('Suplr_'): - return "Supplier Information" - elif column_name.startswith('DME_'): - return "Durable Medical Equipment" - elif column_name.startswith('POS_'): - return "Prosthetics and Orthotics" - elif column_name.startswith('Drug_'): - return "Drug and Nutritional Products" - elif column_name.startswith('Bene_CC_BH_'): - return "Beneficiary Behavioral Health Conditions" - elif column_name.startswith('Bene_CC_PH_'): - return "Beneficiary Physical Health Conditions" - elif column_name.startswith('Bene_'): - return "Beneficiary Demographics" - else: - return "Other" +# Import from the new module structure +from dme_analysis.utils import ( + DATA_DICTIONARY, + get_column_category, + import_data_for_years +) def get_top_suppliers(df, top_n=10): @@ -687,19 +523,12 @@ def main(): print("DME Data Analysis") print("================\n") - # Dictionary to store dataframes by year - df_by_year = {} - - # Import data for years 2017-2022 - for year in range(2017, 2023): - csv_path = f"data/{year}/mup_dme_ry24_p05_v10_dy{str(year)[-2:]}_supr.csv" - if os.path.exists(csv_path): - print(f"Importing data for {year}...") - df_by_year[year] = pd.read_csv(csv_path, low_memory=False) - print( - f"✓ Data for {year} imported successfully. Shape: {df_by_year[year].shape}") - else: - print(f"Warning: No data file found for {year}") + # Import data for years 2017-2022 using the utility function + df_by_year = import_data_for_years(range(2017, 2023)) + + if not df_by_year: + print("Error: No data files were found. Cannot proceed with analysis.") + return {}, {} print("\nAll available data files have been imported.") diff --git a/dme_notebook_example.ipynb b/dme_notebook_example.ipynb deleted file mode 100644 index 0519ecb..0000000 --- a/dme_notebook_example.ipynb +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/fraud_detector.py b/fraud_detector.py new file mode 100644 index 0000000..160ce92 --- /dev/null +++ b/fraud_detector.py @@ -0,0 +1,311 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +DME Fraud Detection Script +This script analyzes Medicare DME supplier data to identify potential fraud indicators, +with a focus on suspicious growth patterns similar to credit card fraud detection techniques. +""" + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from collections import defaultdict +import os +import sys + +# Import from the new module structure +from dme_analysis.utils import ( + DATA_DICTIONARY, + import_dme_data, + get_column_mapping, + get_column_category, + import_data_for_years +) + + +def detect_high_growth_suppliers(df_by_year, metric='DME_Suplr_Mdcr_Pymt_Amt', top_n=50): + """ + Identify suppliers with abnormally high growth rates year over year. + + Parameters: + ----------- + df_by_year : dict + Dictionary containing DataFrames by year + metric : str + The metric to analyze for growth (default: Medicare payments) + top_n : int + Number of top growth suppliers to identify + + Returns: + -------- + growth_df : DataFrame + DataFrame containing suppliers with their growth rates + """ + print(f"Identifying suppliers with highest year-over-year growth rates...") + + # Check if metric exists in all dataframes + for year, df in df_by_year.items(): + if metric not in df.columns: + available_metrics = [ + col for col in df.columns if 'Pymt' in col or 'Amt' in col] + if not available_metrics: + print( + f"Error: No payment metrics found in data for year {year}.") + return pd.DataFrame() + + # Use the first available payment metric + metric = available_metrics[0] + print(f"Using alternate metric: {metric}") + break + + # Get all available years + years = sorted(df_by_year.keys()) + + if len(years) < 2: + print("Error: Need at least two years of data to calculate growth rates") + return pd.DataFrame() + + # Get column mappings from the most recent year's data + recent_year = max(years) + column_map = get_column_mapping(df_by_year[recent_year]) + + # Create a dictionary to store supplier data across years + supplier_data = {} + supplier_info = {} + + # Process each supplier's data for each year + for year in years: + df = df_by_year[year] + + # Get NPI column name + npi_col = column_map['supplier_npi'] + if npi_col is None: + # Create a synthetic NPI using index + df['synthetic_npi'] = 'NPI' + df.index.astype(str) + npi_col = 'synthetic_npi' + + # Group by supplier NPI and sum the metric + supplier_metric = df.groupby(npi_col)[metric].sum().reset_index() + + # Store in dictionary + for _, row in supplier_metric.iterrows(): + npi = row[npi_col] + value = row[metric] + + if npi not in supplier_data: + supplier_data[npi] = {} + + # Store supplier info for later use + supplier_row = df[df[npi_col] == npi].iloc[0] if len( + df[df[npi_col] == npi]) > 0 else None + if supplier_row is not None: + supplier_info[npi] = { + 'name': supplier_row[column_map['supplier_name']] if column_map['supplier_name'] is not None else f"Supplier {npi}", + 'state': supplier_row[column_map['supplier_state']] if column_map['supplier_state'] is not None else 'Unknown' + } + else: + supplier_info[npi] = { + 'name': f"Supplier {npi}", + 'state': 'Unknown' + } + + supplier_data[npi][year] = value + + # Calculate year-over-year growth rates + growth_data = [] + + for npi, year_values in supplier_data.items(): + # Need at least two years of data for this supplier + if len(year_values) < 2: + continue + + for i in range(len(years) - 1): + current_year = years[i] + next_year = years[i + 1] + + # Skip if supplier doesn't have data for both years + if current_year not in year_values or next_year not in year_values: + continue + + current_value = year_values[current_year] + next_value = year_values[next_year] + + # Skip if current value is zero (would result in infinity growth) + if current_value == 0: + continue + + # Calculate growth rate + growth_rate = ((next_value - current_value) / current_value) * 100 + + growth_data.append({ + 'Supplier NPI': npi, + 'Supplier Name': supplier_info[npi]['name'], + 'Supplier State': supplier_info[npi]['state'], + 'Year Period': f"{current_year}-{next_year}", + 'Start Year Value': current_value, + 'End Year Value': next_value, + 'Growth Rate (%)': growth_rate, + 'Absolute Growth': next_value - current_value + }) + + # Convert to DataFrame + growth_df = pd.DataFrame(growth_data) + + # Sort by growth rate (descending) + growth_df = growth_df.sort_values('Growth Rate (%)', ascending=False) + + return growth_df.head(top_n) + + +def plot_high_growth_suppliers(growth_df, top_n=20): + """ + Create a visualization of suppliers with highest growth rates. + + Parameters: + ----------- + growth_df : DataFrame + DataFrame from detect_high_growth_suppliers function + top_n : int + Number of top suppliers to visualize + + Returns: + -------- + fig : Figure + Matplotlib figure object containing the visualization + """ + # Take top N suppliers + plot_df = growth_df.head(top_n) + + # Create figure + fig, ax = plt.subplots(figsize=(14, 10)) + + # Plot horizontal bar chart + bars = sns.barplot( + x='Growth Rate (%)', + y='Supplier Name', + data=plot_df, + palette='viridis', + ax=ax + ) + + # Add value labels + for i, bar in enumerate(bars.patches): + value = plot_df.iloc[i]['Growth Rate (%)'] + ax.text( + bar.get_width() + 10, + bar.get_y() + bar.get_height()/2, + f"{value:,.1f}%", + ha='left', + va='center', + fontweight='bold' + ) + + # Add a second x-axis for absolute growth + ax2 = ax.twiny() + ax2.set_xlabel('Absolute Growth ($)', color='red') + ax2.tick_params(axis='x', colors='red') + + # Plot absolute growth as scatter points + for i, (_, row) in enumerate(plot_df.iterrows()): + ax2.scatter(row['Absolute Growth'], i, color='red', s=100, alpha=0.7) + + # Format the x-axis for absolute growth with dollar amounts + ax2.xaxis.set_major_formatter( + plt.FuncFormatter(lambda x, pos: f'${x:,.0f}')) + + # Set labels and title + ax.set_xlabel('Growth Rate (%)', fontsize=14) + ax.set_ylabel('Supplier', fontsize=14) + ax.set_title(f'Top {top_n} Suppliers by Growth Rate', + fontsize=16, fontweight='bold') + + # Add year period information + if not plot_df.empty: + year_periods = plot_df['Year Period'].unique() + period_str = ', '.join(year_periods) + ax.text( + 0.5, 1.05, f"Year Period(s): {period_str}", transform=ax.transAxes, ha='center') + + # Add grid + ax.grid(axis='x', linestyle='--', alpha=0.7) + + plt.tight_layout() + return fig + + +def main(): + """Main function to import and analyze DME data for fraud detection.""" + print("DME Fraud Detection Analysis") + print("===========================\n") + + # Import data for years 2017-2022 using the utility function + df_by_year = import_data_for_years(range(2017, 2023)) + + if not df_by_year: + print("\nError: No data files were successfully imported. Cannot proceed with analysis.") + return {}, {} + + print(f"\n{len(df_by_year)} year(s) of data imported.") + + # ----- FRAUD DETECTION ANALYSIS ----- + print("\n1. High Growth Rate Analysis") + print("--------------------------\n") + + # Detect suppliers with abnormally high growth rates + growth_df = detect_high_growth_suppliers(df_by_year, top_n=50) + + if growth_df.empty: + print("No suppliers with high growth rates detected.") + return df_by_year, {}, {} + + # Print summary of top 15 high-growth suppliers + print("Top 15 suppliers with highest growth rates:") + + # Format the output for display + formatted_growth_df = growth_df.head(15).copy() + formatted_growth_df['Growth Rate (%)'] = formatted_growth_df['Growth Rate (%)'].apply( + lambda x: f"{x:.2f}%") + formatted_growth_df['Start Year Value'] = formatted_growth_df['Start Year Value'].apply( + lambda x: f"${x:,.2f}") + formatted_growth_df['End Year Value'] = formatted_growth_df['End Year Value'].apply( + lambda x: f"${x:,.2f}") + formatted_growth_df['Absolute Growth'] = formatted_growth_df['Absolute Growth'].apply( + lambda x: f"${x:,.2f}") + + print(formatted_growth_df.to_string(index=False)) + + # ----- VISUALIZATIONS ----- + print("\n2. Generating Fraud Detection Visualizations") + print("------------------------------------------\n") + + # Setting plot style + sns.set_style('whitegrid') + plt.rcParams['figure.figsize'] = [14, 9] + + # Generate visualization + growth_fig = plot_high_growth_suppliers(growth_df, top_n=20) + visualizations = {'high_growth_suppliers': growth_fig} + data = {'high_growth_suppliers': growth_df} + + # Save visualizations to files if not in a notebook environment + try: + # Check if we're in a notebook environment + if 'ipykernel' not in sys.modules: + print("\nSaving visualizations to files...") + os.makedirs('fraud_visualizations', exist_ok=True) + for name, fig in visualizations.items(): + fig.savefig( + f'fraud_visualizations/{name}.png', dpi=300, bbox_inches='tight') + print(f"Saved: fraud_visualizations/{name}.png") + except Exception as e: + print(f"Error saving visualizations: {str(e)}") + print("Note: Visualizations will be displayed if run in a Jupyter notebook") + + # When run in Jupyter, the figures will be displayed inline + return df_by_year, visualizations, data + + +if __name__ == "__main__": + main() From 32c80ebbef5d35913c79d89b936956a0798cd2bf Mon Sep 17 00:00:00 2001 From: Khanan Grauer Date: Tue, 11 Mar 2025 08:54:07 -0400 Subject: [PATCH 2/3] Update --- .cursor/rules/python-jupyter.mdc | 1 + README.md | 90 - analysis.py | 692 ++++ dme_analysis.ipynb | 4876 ++++++++++++++----------- dme_analysis/__init__.py | 8 - dme_analysis/utils/__init__.py | 17 - dme_analysis/utils/data_dictionary.py | 153 - dme_analysis/utils/data_import.py | 155 - dme_data_analysis.py | 781 ---- dme_dictionary.py | 116 + fraud_detector.py | 311 -- requirements.txt | 7 - 12 files changed, 3535 insertions(+), 3672 deletions(-) delete mode 100644 README.md create mode 100644 analysis.py delete mode 100644 dme_analysis/__init__.py delete mode 100644 dme_analysis/utils/__init__.py delete mode 100644 dme_analysis/utils/data_dictionary.py delete mode 100644 dme_analysis/utils/data_import.py delete mode 100644 dme_data_analysis.py create mode 100644 dme_dictionary.py delete mode 100644 fraud_detector.py delete mode 100644 requirements.txt diff --git a/.cursor/rules/python-jupyter.mdc b/.cursor/rules/python-jupyter.mdc index 6fa418e..3b8d0b4 100644 --- a/.cursor/rules/python-jupyter.mdc +++ b/.cursor/rules/python-jupyter.mdc @@ -15,6 +15,7 @@ alwaysApply: false - Prefer vectorized operations over explicit loops for better performance. - Use descriptive variable names that reflect the data they contain. - Follow PEP 8 style guidelines for Python code. + - Only import things that are used Data Analysis and Manipulation: - Use pandas for data manipulation and analysis. diff --git a/README.md b/README.md deleted file mode 100644 index 548a149..0000000 --- a/README.md +++ /dev/null @@ -1,90 +0,0 @@ -# Medicare DME Data Analysis - -This repository contains scripts for analyzing Medicare Durable Medical Equipment (DME) data from 2017-2022. - -## Directory Structure - -``` -. -├── dme_analysis/ # Main package -│ ├── __init__.py # Package initialization -│ ├── utils/ # Utility modules -│ │ ├── __init__.py # Subpackage initialization -│ │ ├── data_dictionary.py # Data dictionary and column categorization -│ │ └── data_import.py # Data import functions -│ └── ... # Analysis modules -├── dme_data_analysis.py # Main analysis script -├── fraud_detector.py # Fraud detection script -└── ... # Data files and other scripts -``` - -## Usage - -### Importing Data - -You can import the DME data using the utility functions: - -```python -from dme_analysis.utils import import_data_for_years - -# Import data for years 2017-2022 -df_by_year = import_data_for_years(range(2017, 2023)) -``` - -### Data Dictionary - -The data dictionary contains descriptions for all columns in the dataset: - -```python -from dme_analysis.utils import DATA_DICTIONARY - -# Get the description of a column -print(DATA_DICTIONARY['DME_Tot_Suplr_Benes']) -``` - -### Analyzing the Data - -The main analysis script can be run directly: - -```bash -python dme_data_analysis.py -``` - -Or imported in a Jupyter notebook: - -```python -import dme_data_analysis as dme -%matplotlib inline - -# Run the analysis -df_by_year, visualizations = dme.main() - -# Display visualizations -visualizations['spending_trends'] -``` - -### Fraud Detection - -The fraud detection script can be used to identify potential fraud patterns: - -```python -import fraud_detector as fd -%matplotlib inline - -# Run the fraud detection analysis -df_by_year, visualizations, data = fd.main() - -# Display fraud indicators -visualizations['high_growth_suppliers'] -``` - -## Column Descriptions - -The DME dataset contains the following types of columns: - -1. Supplier Information (e.g., `Suplr_NPI`, `Suplr_Prvdr_Last_Name_Org`) -2. DME-specific fields (e.g., `DME_Tot_Suplr_Benes`, `DME_Suplr_Mdcr_Pymt_Amt`) -3. Prosthetic and Orthotic fields (e.g., `POS_Tot_Suplr_Benes`, `POS_Suplr_Mdcr_Pymt_Amt`) -4. Drug and Nutritional fields (e.g., `Drug_Tot_Suplr_Benes`, `Drug_Suplr_Mdcr_Pymt_Amt`) -5. Beneficiary Demographics (e.g., `Bene_Avg_Age`, `Bene_Feml_Cnt`) -6. Health Conditions (e.g., `Bene_CC_PH_Hypertension_V2_Pct`, `Bene_CC_BH_Mood_V2_Pct`) diff --git a/analysis.py b/analysis.py new file mode 100644 index 0000000..6959587 --- /dev/null +++ b/analysis.py @@ -0,0 +1,692 @@ +import pandas as pd +import os +import glob +from dme_dictionary import DATA_DICTIONARY +import numpy as np +import locale + +# Set locale for currency formatting +locale.setlocale(locale.LC_ALL, '') + +# Set pandas display options to show all columns +pd.set_option('display.max_columns', None) # Show all columns +pd.set_option('display.width', None) # Don't wrap the output +# Don't add new lines in wide DataFrames +pd.set_option('display.expand_frame_repr', False) + +# Path to data directories +data_dir = 'data' +years = range(2018, 2023) # 2018 to 2022 + +# Initialize an empty list to store DataFrames +dfs = [] + +# Loop through each year +for year in years: + # Get the CSV file path + csv_files = glob.glob(f"{data_dir}/{year}/*.csv") + + if not csv_files: + print(f"No CSV files found for year {year}") + continue + + # Get the first CSV file (there should be only one per year based on our observation) + csv_file = csv_files[0] + print(f"Loading data from {csv_file}") + + # Read the CSV into a DataFrame with mixed type handling + df = pd.read_csv(csv_file, low_memory=False) + + # Add a 'year' column + df['year'] = year + + # Append to our list of DataFrames + dfs.append(df) + +# Function to format dollar amounts (K or M based on size) + + +def format_dollar_amount(amount): + if amount >= 1000000: + return f"${amount/1000000:.1f}M" + else: + return f"${amount/1000:.1f}K" + + +# Combine all DataFrames into one +if dfs: + combined_df = pd.concat(dfs, ignore_index=True) + print(f"Combined DataFrame shape: {combined_df.shape}") + + # Display the first few rows with all columns + # print("\nFirst few rows of the combined DataFrame:") + # print(combined_df.head()) + + # Create a dictionary to store column information + column_info = {} + + # Check which columns from our data are in the data dictionary + for column in combined_df.columns: + if column in DATA_DICTIONARY: + column_info[column] = DATA_DICTIONARY[column] + else: + column_info[column] = "Description not available" + + # Display column information + print("\nColumn Information:") + # for column, count in zip(combined_df.columns, combined_df.count()): + # description = column_info.get(column, "Description not available") + # print(f"Column: {column}") + # print(f" Description: {description}") + # print(f" Non-null count: {count}/{len(combined_df)} entries") + # print(f" Data type: {combined_df[column].dtype}") + # print() + + # Add data dictionary descriptions as attributes + combined_df.attrs['column_descriptions'] = column_info + + # Summary statistics for numerical columns + # print("\nSummary statistics for numerical columns:") + # print(combined_df.describe()) + + print("\n" + "="*100) + print("Year-over-Year Growth Rate Analysis") + print("="*100) + + # Create a DataFrame to analyze suppliers by year-over-year growth rate + # First, group by supplier and year to get annual totals + supplier_yearly = combined_df.groupby(['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org', 'year']).agg({ + 'Suplr_Sbmtd_Chrgs': 'sum', + 'Suplr_Mdcr_Pymt_Amt': 'sum', + 'Tot_Suplr_Benes': 'mean', # Average number of beneficiaries + 'Tot_Suplr_Clms': 'sum' # Total claims + }).reset_index() + + # Create a pivot table to have years as columns + pivot_charges = supplier_yearly.pivot_table( + index=['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org'], + columns='year', + values='Suplr_Mdcr_Pymt_Amt', + fill_value=0 + ) + + # Calculate year-over-year growth rates + growth_rates = pd.DataFrame(index=pivot_charges.index) + + # Calculate growth rate for each year pair (2019/2018, 2020/2019, etc.) + for year_pair in [(2019, 2018), (2020, 2019), (2021, 2020), (2022, 2021)]: + current, previous = year_pair + growth_rates[f'growth_{current}'] = ( + (pivot_charges[current] - pivot_charges[previous]) / + pivot_charges[previous].replace(0, float('nan')) + ) * 100 # Convert to percentage + + # Calculate average growth rate across all years + growth_cols = [ + col for col in growth_rates.columns if col.startswith('growth_')] + growth_rates['avg_growth'] = growth_rates[growth_cols].mean(axis=1) + + # Filter out suppliers that weren't present in all years + valid_suppliers = pivot_charges[(pivot_charges[2018] > 0) & + (pivot_charges[2019] > 0) & + (pivot_charges[2020] > 0) & + (pivot_charges[2021] > 0) & + (pivot_charges[2022] > 0)] + + # Filter suppliers with significant payment amounts (at least $100K in the last year) + significant_suppliers = valid_suppliers[valid_suppliers[2022] >= 100000] + print( + f"Filtering to {len(significant_suppliers)} suppliers with at least $100,000 in payments in 2022") + + # Merge growth rates with valid and significant suppliers + valid_growth = growth_rates.loc[significant_suppliers.index].reset_index() + + # Sort by average growth rate in descending order + top_growth = valid_growth.sort_values('avg_growth', ascending=False) + + # Merge with additional data for reporting + supplier_totals = supplier_yearly.groupby(['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org']).agg({ + 'Suplr_Sbmtd_Chrgs': 'sum', + 'Suplr_Mdcr_Pymt_Amt': 'sum', + 'Tot_Suplr_Benes': 'mean', + 'Tot_Suplr_Clms': 'sum' + }).reset_index() + + top_growth_with_data = pd.merge( + top_growth, + supplier_totals, + on=['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org'] + ) + + # Format the output for the top 10 suppliers + print("The analysis identified suppliers with the highest growth rates based on Medicare payment amounts from 2018 to 2022.") + print("Here are the top 10 suppliers with extraordinary growth (minimum $100K in 2022 payments):\n") + + # Get top 10 suppliers + top_10_suppliers = top_growth_with_data.head(10) + top_10_npi = top_10_suppliers['Suplr_NPI'].tolist() + + # Filter the original data for just these suppliers + top_supplier_data = supplier_yearly[supplier_yearly['Suplr_NPI'].isin( + top_10_npi)] + + # Format and display each supplier's information + for i, (_, supplier) in enumerate(top_10_suppliers.iterrows(), 1): + npi = supplier['Suplr_NPI'] + name = supplier['Suplr_Prvdr_Last_Name_Org'] + avg_growth = supplier['avg_growth'] + total_payments = supplier['Suplr_Mdcr_Pymt_Amt'] + + # Get yearly data for this supplier + yearly_data = top_supplier_data[top_supplier_data['Suplr_NPI'] == npi].sort_values( + 'year') + + print(f"{i}. **{name}** (NPI: {npi})") + print(f" - Average growth rate: {avg_growth:.2f}%") + print( + f" - Total Medicare payments: ${total_payments/1000000:.2f} million") + + # Show yearly payment amounts + yearly_payments = [] + for year in range(2018, 2023): + year_data = yearly_data[yearly_data['year'] == year] + if not year_data.empty: + payment = year_data['Suplr_Mdcr_Pymt_Amt'].values[0] + yearly_payments.append(format_dollar_amount(payment)) + else: + yearly_payments.append("$0") + + print( + f" - Yearly payments: 2018: {yearly_payments[0]}, 2019: {yearly_payments[1]}, 2020: {yearly_payments[2]}, 2021: {yearly_payments[3]}, 2022: {yearly_payments[4]}") + + # Analyze growth pattern + payment_pattern = yearly_data['Suplr_Mdcr_Pymt_Amt'].tolist() + years_list = yearly_data['year'].tolist() + benes_pattern = yearly_data['Tot_Suplr_Benes'].tolist() + + # Identify the largest year-over-year jump + max_jump = 0 + max_jump_year_idx = 0 + for j in range(1, len(payment_pattern)): + if payment_pattern[j-1] > 0: + jump_pct = ( + payment_pattern[j] - payment_pattern[j-1]) / payment_pattern[j-1] * 100 + if jump_pct > max_jump: + max_jump = jump_pct + max_jump_year_idx = j + + if max_jump_year_idx > 0: + from_year = years_list[max_jump_year_idx-1] + to_year = years_list[max_jump_year_idx] + from_amount = payment_pattern[max_jump_year_idx-1] + to_amount = payment_pattern[max_jump_year_idx] + + # Format amounts with K or M suffix based on size + from_amount_str = format_dollar_amount(from_amount) + to_amount_str = format_dollar_amount(to_amount) + + print( + f" - Growth pattern: Major increase from {from_year} to {to_year} ({from_amount_str} to {to_amount_str})") + + # Check for consistent growth + growth_consistent = True + for j in range(1, len(payment_pattern)): + if payment_pattern[j] <= payment_pattern[j-1]: + growth_consistent = False + break + + if growth_consistent and len(payment_pattern) > 2: + print(" - Pattern shows consistent year-over-year growth") + + # Check for beneficiary growth + if not pd.isna(benes_pattern).all() and len(benes_pattern) >= 2: + first_valid_idx = next((i for i, x in enumerate( + benes_pattern) if not pd.isna(x)), None) + last_valid_idx = next((i for i, x in enumerate( + reversed(benes_pattern)) if not pd.isna(x)), None) + if first_valid_idx is not None and last_valid_idx is not None: + last_valid_idx = len(benes_pattern) - 1 - last_valid_idx + first_benes = benes_pattern[first_valid_idx] + last_benes = benes_pattern[last_valid_idx] + if not pd.isna(first_benes) and not pd.isna(last_benes) and first_benes > 0: + bene_growth = (last_benes - first_benes) / \ + first_benes * 100 + print( + f" - Beneficiary growth: {bene_growth:.1f}% increase (from {first_benes:.0f} to {last_benes:.0f})") + + print("") # Add a blank line between suppliers + + # ===================================== + # Analysis of High Submitted Charges vs Low Allowed/Paid Amounts + # ===================================== + print("\n" + "="*100) + print("Analysis of High Submitted Charges with Low Allowed/Paid Amounts") + print("="*100) + + # Aggregate data by supplier across all years + supplier_totals_with_allowed = combined_df.groupby(['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org']).agg({ + 'Suplr_Sbmtd_Chrgs': 'sum', + 'Suplr_Mdcr_Alowd_Amt': 'sum', + 'Suplr_Mdcr_Pymt_Amt': 'sum', + 'Tot_Suplr_Benes': 'mean', + 'Tot_Suplr_Clms': 'sum' + }).reset_index() + + # Calculate ratios + supplier_totals_with_allowed['submitted_allowed_ratio'] = supplier_totals_with_allowed['Suplr_Sbmtd_Chrgs'] / \ + supplier_totals_with_allowed['Suplr_Mdcr_Alowd_Amt'] + supplier_totals_with_allowed['submitted_paid_ratio'] = supplier_totals_with_allowed['Suplr_Sbmtd_Chrgs'] / \ + supplier_totals_with_allowed['Suplr_Mdcr_Pymt_Amt'] + + # Filter for suppliers with substantial submitted charges (at least $100,000) to focus on meaningful outliers + significant_suppliers = supplier_totals_with_allowed[ + supplier_totals_with_allowed['Suplr_Sbmtd_Chrgs'] >= 100000] + + # Find outliers with highest submitted-to-allowed ratio + top_submitted_allowed_outliers = significant_suppliers.sort_values( + 'submitted_allowed_ratio', ascending=False).head(10) + + print("Top 10 Suppliers with Highest Submitted Charges to Allowed Amount Ratio:\n") + + for i, (_, supplier) in enumerate(top_submitted_allowed_outliers.iterrows(), 1): + npi = supplier['Suplr_NPI'] + name = supplier['Suplr_Prvdr_Last_Name_Org'] + submitted = supplier['Suplr_Sbmtd_Chrgs'] + allowed = supplier['Suplr_Mdcr_Alowd_Amt'] + paid = supplier['Suplr_Mdcr_Pymt_Amt'] + ratio = supplier['submitted_allowed_ratio'] + + # Format amounts with K or M suffix based on size + submitted_str = format_dollar_amount(submitted) + allowed_str = format_dollar_amount(allowed) + paid_str = format_dollar_amount(paid) + + print(f"{i}. **{name}** (NPI: {npi})") + print(f" - Submitted charges: {submitted_str}") + print(f" - Allowed amount: {allowed_str}") + print(f" - Paid amount: {paid_str}") + print(f" - Submitted to allowed ratio: {ratio:.2f}x") + print( + f" - Allowed amount is {(allowed/submitted)*100:.1f}% of submitted charges") + print( + f" - Paid amount is {(paid/submitted)*100:.1f}% of submitted charges") + print("") # Add a blank line between suppliers + + # Find outliers with highest submitted-to-paid ratio + top_submitted_paid_outliers = significant_suppliers.sort_values( + 'submitted_paid_ratio', ascending=False).head(10) + + print("\nTop 10 Suppliers with Highest Submitted Charges to Paid Amount Ratio:\n") + + for i, (_, supplier) in enumerate(top_submitted_paid_outliers.iterrows(), 1): + npi = supplier['Suplr_NPI'] + name = supplier['Suplr_Prvdr_Last_Name_Org'] + submitted = supplier['Suplr_Sbmtd_Chrgs'] + allowed = supplier['Suplr_Mdcr_Alowd_Amt'] + paid = supplier['Suplr_Mdcr_Pymt_Amt'] + ratio = supplier['submitted_paid_ratio'] + + # Format amounts with K or M suffix based on size + submitted_str = format_dollar_amount(submitted) + allowed_str = format_dollar_amount(allowed) + paid_str = format_dollar_amount(paid) + + print(f"{i}. **{name}** (NPI: {npi})") + print(f" - Submitted charges: {submitted_str}") + print(f" - Allowed amount: {allowed_str}") + print(f" - Paid amount: {paid_str}") + print(f" - Submitted to paid ratio: {ratio:.2f}x") + print( + f" - Paid amount is {(paid/submitted)*100:.1f}% of submitted charges") + print("") # Add a blank line between suppliers + + # ===================================== + # Peer Group Analysis for Fraud Detection + # ===================================== + print("\n" + "="*100) + print("Peer Group Analysis for Fraud Detection") + print("="*100) + + if dfs: + # Ensure we have the required columns for analysis + required_columns = ['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org', 'Suplr_Prvdr_Spclty_Desc', + 'Suplr_Prvdr_State_Abrvtn', 'Suplr_Sbmtd_Chrgs', 'Suplr_Mdcr_Pymt_Amt', + 'Tot_Suplr_Clms', 'Tot_Suplr_Srvcs'] + + # Check if all required columns exist in the combined dataframe + missing_columns = [ + col for col in required_columns if col not in combined_df.columns] + if missing_columns: + print( + f"Warning: Missing columns needed for peer group analysis: {missing_columns}") + print("Skipping peer group analysis.") + else: + # Calculate aggregated metrics by supplier for analysis + supplier_metrics = combined_df.groupby(['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org', + 'Suplr_Prvdr_Spclty_Desc', 'Suplr_Prvdr_State_Abrvtn']).agg({ + 'Suplr_Sbmtd_Chrgs': 'sum', + 'Suplr_Mdcr_Pymt_Amt': 'sum', + 'Tot_Suplr_Clms': 'sum', + 'Tot_Suplr_Srvcs': 'sum' + }).reset_index() + + # Add derived metrics + supplier_metrics['Avg_Chrg_Per_Clm'] = supplier_metrics['Suplr_Sbmtd_Chrgs'] / \ + supplier_metrics['Tot_Suplr_Clms'] + supplier_metrics['Avg_Pymt_Per_Clm'] = supplier_metrics['Suplr_Mdcr_Pymt_Amt'] / \ + supplier_metrics['Tot_Suplr_Clms'] + supplier_metrics['Avg_Srvcs_Per_Clm'] = supplier_metrics['Tot_Suplr_Srvcs'] / \ + supplier_metrics['Tot_Suplr_Clms'] + + # 1. Analysis by Specialty + print("\nAnalysis by Specialty:") + print("-" * 50) + + # Get the specialties with at least 5 suppliers for meaningful comparison + specialty_counts = supplier_metrics['Suplr_Prvdr_Spclty_Desc'].value_counts( + ) + valid_specialties = specialty_counts[specialty_counts >= 5].index.tolist( + ) + + if valid_specialties: + print( + f"Found {len(valid_specialties)} specialties with at least 5 suppliers for peer comparison.") + + # Calculate peer group metrics for each specialty + peer_specialty_metrics = supplier_metrics[supplier_metrics['Suplr_Prvdr_Spclty_Desc'].isin(valid_specialties)].groupby( + 'Suplr_Prvdr_Spclty_Desc').agg({ + 'Suplr_Sbmtd_Chrgs': ['median', 'mean', 'std'], + 'Suplr_Mdcr_Pymt_Amt': ['median', 'mean', 'std'], + 'Tot_Suplr_Clms': ['median', 'mean', 'std'], + 'Tot_Suplr_Srvcs': ['median', 'mean', 'std'], + 'Avg_Chrg_Per_Clm': ['median', 'mean', 'std'], + 'Avg_Pymt_Per_Clm': ['median', 'mean', 'std'], + 'Avg_Srvcs_Per_Clm': ['median', 'mean', 'std'] + }) + + # Find outliers within each specialty (suppliers with metrics > 3x the median) + outliers_by_specialty = [] + + for specialty in valid_specialties: + specialty_group = supplier_metrics[supplier_metrics['Suplr_Prvdr_Spclty_Desc'] == specialty] + specialty_medians = peer_specialty_metrics.loc[specialty] + + # Check for outliers in claims, charges, and payments + claim_outliers = specialty_group[specialty_group['Tot_Suplr_Clms'] + > 3 * specialty_medians[('Tot_Suplr_Clms', 'median')]] + charge_outliers = specialty_group[specialty_group['Suplr_Sbmtd_Chrgs'] + > 3 * specialty_medians[('Suplr_Sbmtd_Chrgs', 'median')]] + payment_outliers = specialty_group[specialty_group['Suplr_Mdcr_Pymt_Amt'] + > 3 * specialty_medians[('Suplr_Mdcr_Pymt_Amt', 'median')]] + + # Find suppliers that are outliers in at least two categories + all_outliers = pd.concat([ + claim_outliers[['Suplr_NPI']].assign(metric='claims'), + charge_outliers[['Suplr_NPI']].assign( + metric='charges'), + payment_outliers[['Suplr_NPI']].assign( + metric='payments') + ]) + + outlier_counts = all_outliers.groupby('Suplr_NPI').size() + multiple_outliers = outlier_counts[outlier_counts >= 2].index.tolist( + ) + + if multiple_outliers: + for npi in multiple_outliers: + supplier = specialty_group[specialty_group['Suplr_NPI'] + == npi].iloc[0] + outliers_by_specialty.append({ + 'NPI': npi, + 'Name': supplier['Suplr_Prvdr_Last_Name_Org'], + 'Specialty': specialty, + 'State': supplier['Suplr_Prvdr_State_Abrvtn'], + 'Total_Claims': supplier['Tot_Suplr_Clms'], + 'Claim_Ratio': supplier['Tot_Suplr_Clms'] / specialty_medians[('Tot_Suplr_Clms', 'median')], + 'Total_Charges': supplier['Suplr_Sbmtd_Chrgs'], + 'Charge_Ratio': supplier['Suplr_Sbmtd_Chrgs'] / specialty_medians[('Suplr_Sbmtd_Chrgs', 'median')], + 'Total_Payments': supplier['Suplr_Mdcr_Pymt_Amt'], + 'Payment_Ratio': supplier['Suplr_Mdcr_Pymt_Amt'] / specialty_medians[('Suplr_Mdcr_Pymt_Amt', 'median')] + }) + + # Display the top outliers by specialty + if outliers_by_specialty: + # Sort by highest combined ratio (sum of all ratios) + for outlier in sorted(outliers_by_specialty, + key=lambda x: ( + x['Claim_Ratio'] + x['Charge_Ratio'] + x['Payment_Ratio']), + reverse=True)[:10]: + print( + f"\n**{outlier['Name']}** (NPI: {outlier['NPI']})") + print( + f" Specialty: {outlier['Specialty']} | State: {outlier['State']}") + print( + f" Total Claims: {outlier['Total_Claims']:.0f} ({outlier['Claim_Ratio']:.1f}x specialty median)") + + # Format monetary values + charges_str = format_dollar_amount( + outlier['Total_Charges']) + payments_str = format_dollar_amount( + outlier['Total_Payments']) + + print( + f" Total Charges: {charges_str} ({outlier['Charge_Ratio']:.1f}x specialty median)") + print( + f" Total Payments: {payments_str} ({outlier['Payment_Ratio']:.1f}x specialty median)") + else: + print("No significant specialty outliers found.") + else: + print( + "No specialties with enough suppliers for meaningful peer comparison.") + + # 2. Analysis by State + print("\nAnalysis by State:") + print("-" * 50) + + # Get the states with at least 5 suppliers for meaningful comparison + state_counts = supplier_metrics['Suplr_Prvdr_State_Abrvtn'].value_counts( + ) + valid_states = state_counts[state_counts >= 5].index.tolist() + + if valid_states: + print( + f"Found {len(valid_states)} states with at least 5 suppliers for peer comparison.") + + # Calculate peer group metrics for each state + peer_state_metrics = supplier_metrics[supplier_metrics['Suplr_Prvdr_State_Abrvtn'].isin(valid_states)].groupby( + 'Suplr_Prvdr_State_Abrvtn').agg({ + 'Suplr_Sbmtd_Chrgs': ['median', 'mean', 'std'], + 'Suplr_Mdcr_Pymt_Amt': ['median', 'mean', 'std'], + 'Tot_Suplr_Clms': ['median', 'mean', 'std'], + 'Tot_Suplr_Srvcs': ['median', 'mean', 'std'], + 'Avg_Chrg_Per_Clm': ['median', 'mean', 'std'], + 'Avg_Pymt_Per_Clm': ['median', 'mean', 'std'], + 'Avg_Srvcs_Per_Clm': ['median', 'mean', 'std'] + }) + + # Find outliers within each state (suppliers with metrics > 3x the median) + outliers_by_state = [] + + for state in valid_states: + state_group = supplier_metrics[supplier_metrics['Suplr_Prvdr_State_Abrvtn'] == state] + state_medians = peer_state_metrics.loc[state] + + # Check for outliers in claims, charges, and payments + claim_outliers = state_group[state_group['Tot_Suplr_Clms'] + > 3 * state_medians[('Tot_Suplr_Clms', 'median')]] + charge_outliers = state_group[state_group['Suplr_Sbmtd_Chrgs'] + > 3 * state_medians[('Suplr_Sbmtd_Chrgs', 'median')]] + payment_outliers = state_group[state_group['Suplr_Mdcr_Pymt_Amt'] + > 3 * state_medians[('Suplr_Mdcr_Pymt_Amt', 'median')]] + + # Find suppliers that are outliers in at least two categories + all_outliers = pd.concat([ + claim_outliers[['Suplr_NPI']].assign(metric='claims'), + charge_outliers[['Suplr_NPI']].assign( + metric='charges'), + payment_outliers[['Suplr_NPI']].assign( + metric='payments') + ]) + + outlier_counts = all_outliers.groupby('Suplr_NPI').size() + multiple_outliers = outlier_counts[outlier_counts >= 2].index.tolist( + ) + + if multiple_outliers: + for npi in multiple_outliers: + supplier = state_group[state_group['Suplr_NPI'] + == npi].iloc[0] + outliers_by_state.append({ + 'NPI': npi, + 'Name': supplier['Suplr_Prvdr_Last_Name_Org'], + 'Specialty': supplier['Suplr_Prvdr_Spclty_Desc'], + 'State': state, + 'Total_Claims': supplier['Tot_Suplr_Clms'], + 'Claim_Ratio': supplier['Tot_Suplr_Clms'] / state_medians[('Tot_Suplr_Clms', 'median')], + 'Total_Charges': supplier['Suplr_Sbmtd_Chrgs'], + 'Charge_Ratio': supplier['Suplr_Sbmtd_Chrgs'] / state_medians[('Suplr_Sbmtd_Chrgs', 'median')], + 'Total_Payments': supplier['Suplr_Mdcr_Pymt_Amt'], + 'Payment_Ratio': supplier['Suplr_Mdcr_Pymt_Amt'] / state_medians[('Suplr_Mdcr_Pymt_Amt', 'median')] + }) + + # Display the top outliers by state + if outliers_by_state: + # Sort by highest combined ratio (sum of all ratios) + for outlier in sorted(outliers_by_state, + key=lambda x: ( + x['Claim_Ratio'] + x['Charge_Ratio'] + x['Payment_Ratio']), + reverse=True)[:10]: + print( + f"\n**{outlier['Name']}** (NPI: {outlier['NPI']})") + print( + f" State: {outlier['State']} | Specialty: {outlier['Specialty']}") + print( + f" Total Claims: {outlier['Total_Claims']:.0f} ({outlier['Claim_Ratio']:.1f}x state median)") + + # Format monetary values + charges_str = format_dollar_amount( + outlier['Total_Charges']) + payments_str = format_dollar_amount( + outlier['Total_Payments']) + + print( + f" Total Charges: {charges_str} ({outlier['Charge_Ratio']:.1f}x state median)") + print( + f" Total Payments: {payments_str} ({outlier['Payment_Ratio']:.1f}x state median)") + else: + print("No significant state outliers found.") + else: + print("No states with enough suppliers for meaningful peer comparison.") + + # 3. Combined specialty-state analysis for the most precise peer grouping + print("\nAnalysis by Combined Specialty-State Groups:") + print("-" * 50) + + # Create specialty-state combination for more precise peer groups + supplier_metrics['Specialty_State'] = supplier_metrics['Suplr_Prvdr_Spclty_Desc'] + \ + ' - ' + supplier_metrics['Suplr_Prvdr_State_Abrvtn'] + + # Get specialty-state combinations with at least 5 suppliers + specialty_state_counts = supplier_metrics['Specialty_State'].value_counts( + ) + valid_specialty_states = specialty_state_counts[specialty_state_counts >= 5].index.tolist( + ) + + if valid_specialty_states: + print( + f"Found {len(valid_specialty_states)} specialty-state combinations with at least 5 suppliers.") + + # Calculate metrics for each specialty-state combination + peer_combined_metrics = supplier_metrics[supplier_metrics['Specialty_State'].isin(valid_specialty_states)].groupby( + 'Specialty_State').agg({ + 'Suplr_Sbmtd_Chrgs': ['median', 'mean', 'std'], + 'Suplr_Mdcr_Pymt_Amt': ['median', 'mean', 'std'], + 'Tot_Suplr_Clms': ['median', 'mean', 'std'], + 'Tot_Suplr_Srvcs': ['median', 'mean', 'std'], + 'Avg_Chrg_Per_Clm': ['median', 'mean', 'std'], + 'Avg_Pymt_Per_Clm': ['median', 'mean', 'std'], + 'Avg_Srvcs_Per_Clm': ['median', 'mean', 'std'] + }) + + # Find outliers within each specialty-state group + outliers_combined = [] + + for group in valid_specialty_states: + combined_group = supplier_metrics[supplier_metrics['Specialty_State'] == group] + combined_medians = peer_combined_metrics.loc[group] + + # Check for outliers in claims, charges, and payments + claim_outliers = combined_group[combined_group['Tot_Suplr_Clms'] + > 3 * combined_medians[('Tot_Suplr_Clms', 'median')]] + charge_outliers = combined_group[combined_group['Suplr_Sbmtd_Chrgs'] + > 3 * combined_medians[('Suplr_Sbmtd_Chrgs', 'median')]] + payment_outliers = combined_group[combined_group['Suplr_Mdcr_Pymt_Amt'] + > 3 * combined_medians[('Suplr_Mdcr_Pymt_Amt', 'median')]] + + # Find suppliers that are outliers in at least two categories + all_outliers = pd.concat([ + claim_outliers[['Suplr_NPI']].assign(metric='claims'), + charge_outliers[['Suplr_NPI']].assign( + metric='charges'), + payment_outliers[['Suplr_NPI']].assign( + metric='payments') + ]) + + outlier_counts = all_outliers.groupby('Suplr_NPI').size() + multiple_outliers = outlier_counts[outlier_counts >= 2].index.tolist( + ) + + if multiple_outliers: + for npi in multiple_outliers: + supplier = combined_group[combined_group['Suplr_NPI'] + == npi].iloc[0] + outliers_combined.append({ + 'NPI': npi, + 'Name': supplier['Suplr_Prvdr_Last_Name_Org'], + 'Specialty': supplier['Suplr_Prvdr_Spclty_Desc'], + 'State': supplier['Suplr_Prvdr_State_Abrvtn'], + 'Group': group, + 'Total_Claims': supplier['Tot_Suplr_Clms'], + 'Claim_Ratio': supplier['Tot_Suplr_Clms'] / combined_medians[('Tot_Suplr_Clms', 'median')], + 'Total_Charges': supplier['Suplr_Sbmtd_Chrgs'], + 'Charge_Ratio': supplier['Suplr_Sbmtd_Chrgs'] / combined_medians[('Suplr_Sbmtd_Chrgs', 'median')], + 'Total_Payments': supplier['Suplr_Mdcr_Pymt_Amt'], + 'Payment_Ratio': supplier['Suplr_Mdcr_Pymt_Amt'] / combined_medians[('Suplr_Mdcr_Pymt_Amt', 'median')] + }) + + # Display the top outliers by combined group + if outliers_combined: + print( + "\nMost Significant Outliers by Combined Specialty-State Group:") + # Sort by highest combined ratio (sum of all ratios) + for outlier in sorted(outliers_combined, + key=lambda x: ( + x['Claim_Ratio'] + x['Charge_Ratio'] + x['Payment_Ratio']), + reverse=True)[:10]: + print( + f"\n**{outlier['Name']}** (NPI: {outlier['NPI']})") + print( + f" Specialty: {outlier['Specialty']} | State: {outlier['State']}") + print( + f" Total Claims: {outlier['Total_Claims']:.0f} ({outlier['Claim_Ratio']:.1f}x peer group median)") + + # Format monetary values + charges_str = format_dollar_amount( + outlier['Total_Charges']) + payments_str = format_dollar_amount( + outlier['Total_Payments']) + + print( + f" Total Charges: {charges_str} ({outlier['Charge_Ratio']:.1f}x peer group median)") + print( + f" Total Payments: {payments_str} ({outlier['Payment_Ratio']:.1f}x peer group median)") + else: + print("No significant combined specialty-state outliers found.") + else: + print( + "No specialty-state combinations with enough suppliers for meaningful peer comparison.") +else: + print("No data was loaded. Please check if the CSV files exist.") + + +print("Stopping here") diff --git a/dme_analysis.ipynb b/dme_analysis.ipynb index 1649cd2..4a2ba84 100644 --- a/dme_analysis.ipynb +++ b/dme_analysis.ipynb @@ -2,934 +2,1935 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, - "id": "76a4490c", + "execution_count": 1, + "id": "373321ec", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# 1. Imports & Settings\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import glob\n", + "import locale\n", + "from dme_dictionary import DATA_DICTIONARY # Assuming you have a Python file that defines DATA_DICTIONARY\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams[\"figure.figsize\"] = (8, 5)\n", + "\n", + "# Set locale for currency formatting if desired\n", + "locale.setlocale(locale.LC_ALL, '')\n", + "\n", + "# Pandas display options\n", + "pd.set_option('display.max_columns', None) # Show all columns\n", + "pd.set_option('display.width', None) # Avoid wrapping output\n", + "pd.set_option('display.expand_frame_repr', False) # Single-line output for wide DataFrames" + ] + }, + { + "cell_type": "markdown", + "id": "68dda7d8", + "metadata": {}, + "source": [ + "# Medicare DME Supplier Analysis\n", + "\n", + "This notebook demonstrates how to:\n", + "1. Load Medicare Durable Medical Equipment (DME) supplier data spanning multiple years (2018–2022).\n", + "2. Analyze key metrics (submitted charges, Medicare payments, beneficiary counts) over time.\n", + "3. Compute year-over-year growth rates and identify significant spikes.\n", + "4. Examine high submitted vs. low allowed or paid amounts.\n", + "5. Perform peer-group analyses by specialty, state, and combined specialty–state.\n", + "\n", + "We'll highlight outliers that may be worth investigating for potential fraud or anomalies." + ] + }, + { + "cell_type": "markdown", + "id": "9bc5b0ae", "metadata": {}, + "source": [ + "## 2. Data Loading\n", + "We'll load each year's CSV file from 2018 to 2022, then combine them into a single DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c40d3ac0", + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DME Fraud Detection Analysis\n", - "===========================\n", - "\n", - "Importing data for 2017...\n", - "✓ Data for 2017 imported successfully. Shape: (75343, 94)\n", - "Importing data for 2018...\n", - "✓ Data for 2018 imported successfully. Shape: (75805, 94)\n", - "Importing data for 2019...\n", - "✓ Data for 2019 imported successfully. Shape: (72775, 94)\n", - "Importing data for 2020...\n", - "✓ Data for 2020 imported successfully. Shape: (69398, 94)\n", - "Importing data for 2021...\n", - "✓ Data for 2021 imported successfully. Shape: (68227, 94)\n", - "Importing data for 2022...\n", - "✓ Data for 2022 imported successfully. Shape: (66406, 94)\n", - "\n", - "6 year(s) of data imported.\n", - "\n", - "Sample column names from 2017 data:\n", - " 1. Bene_Age_65_74_Cnt\n", - " 2. Bene_Age_75_84_Cnt\n", - " 3. Bene_Age_GT_84_Cnt\n", - " 4. Bene_Age_LT_65_Cnt\n", - " 5. Bene_Avg_Age\n", - " 6. Bene_Avg_Risk_Scre\n", - " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", - " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", - " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", - " 10. Bene_CC_BH_Anxiety_V1_Pct\n", - " 11. Bene_CC_BH_Bipolar_V1_Pct\n", - " 12. Bene_CC_BH_Depress_V1_Pct\n", - " 13. Bene_CC_BH_Mood_V2_Pct\n", - " 14. Bene_CC_BH_PD_V1_Pct\n", - " 15. Bene_CC_BH_PTSD_V1_Pct\n", - " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", - " 17. Bene_CC_BH_Tobacco_V1_Pct\n", - " 18. Bene_CC_PH_Afib_V2_Pct\n", - " 19. Bene_CC_PH_Arthritis_V2_Pct\n", - " 20. Bene_CC_PH_Asthma_V2_Pct\n", - " ... and 74 more columns\n", - "\n", - "1. High Growth Rate Analysis\n", - "--------------------------\n", - "\n", - "Identifying suppliers with highest year-over-year growth rates...\n", - "Warning: No supplier name column found. Using placeholder names.\n", - "\n", - "Available columns in the dataset:\n", - " 1. Bene_Age_65_74_Cnt\n", - " 2. Bene_Age_75_84_Cnt\n", - " 3. Bene_Age_GT_84_Cnt\n", - " 4. Bene_Age_LT_65_Cnt\n", - " 5. Bene_Avg_Age\n", - " 6. Bene_Avg_Risk_Scre\n", - " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", - " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", - " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", - " 10. Bene_CC_BH_Anxiety_V1_Pct\n", - " 11. Bene_CC_BH_Bipolar_V1_Pct\n", - " 12. Bene_CC_BH_Depress_V1_Pct\n", - " 13. Bene_CC_BH_Mood_V2_Pct\n", - " 14. Bene_CC_BH_PD_V1_Pct\n", - " 15. Bene_CC_BH_PTSD_V1_Pct\n", - " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", - " 17. Bene_CC_BH_Tobacco_V1_Pct\n", - " 18. Bene_CC_PH_Afib_V2_Pct\n", - " 19. Bene_CC_PH_Arthritis_V2_Pct\n", - " 20. Bene_CC_PH_Asthma_V2_Pct\n", - " 21. Bene_CC_PH_CKD_V2_Pct\n", - " 22. Bene_CC_PH_COPD_V2_Pct\n", - " 23. Bene_CC_PH_Cancer6_V2_Pct\n", - " 24. Bene_CC_PH_Diabetes_V2_Pct\n", - " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", - " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", - " 27. Bene_CC_PH_Hypertension_V2_Pct\n", - " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", - " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", - " 30. Bene_CC_PH_Parkinson_V2_Pct\n", - " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", - " 32. Bene_Dual_Cnt\n", - " 33. Bene_Feml_Cnt\n", - " 34. Bene_Male_Cnt\n", - " 35. Bene_Ndual_Cnt\n", - " 36. Bene_Race_Api_Cnt\n", - " 37. Bene_Race_Black_Cnt\n", - " 38. Bene_Race_Hspnc_Cnt\n", - " 39. Bene_Race_Natind_Cnt\n", - " 40. Bene_Race_Othr_Cnt\n", - " 41. Bene_Race_Wht_Cnt\n", - " 42. DME_Sprsn_Ind\n", - " 43. DME_Suplr_Mdcr_Alowd_Amt\n", - " 44. DME_Suplr_Mdcr_Pymt_Amt\n", - " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 46. DME_Suplr_Sbmtd_Chrgs\n", - " 47. DME_Tot_Suplr_Benes\n", - " 48. DME_Tot_Suplr_Clms\n", - " 49. DME_Tot_Suplr_HCPCS_Cds\n", - " 50. DME_Tot_Suplr_Srvcs\n", - " 51. Drug_Sprsn_Ind\n", - " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", - " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", - " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 55. Drug_Suplr_Sbmtd_Chrgs\n", - " 56. Drug_Tot_Suplr_Benes\n", - " 57. Drug_Tot_Suplr_Clms\n", - " 58. Drug_Tot_Suplr_HCPCS_Cds\n", - " 59. Drug_Tot_Suplr_Srvcs\n", - " 60. POS_Sprsn_Ind\n", - " 61. POS_Suplr_Mdcr_Alowd_Amt\n", - " 62. POS_Suplr_Mdcr_Pymt_Amt\n", - " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 64. POS_Suplr_Sbmtd_Chrgs\n", - " 65. POS_Tot_Suplr_Benes\n", - " 66. POS_Tot_Suplr_Clms\n", - " 67. POS_Tot_Suplr_HCPCS_Cds\n", - " 68. POS_Tot_Suplr_Srvcs\n", - " 69. Suplr_Mdcr_Alowd_Amt\n", - " 70. Suplr_Mdcr_Pymt_Amt\n", - " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 72. Suplr_NPI\n", - " 73. Suplr_Prvdr_City\n", - " 74. Suplr_Prvdr_Cntry\n", - " 75. Suplr_Prvdr_Crdntls\n", - " 76. Suplr_Prvdr_Ent_Cd\n", - " 77. Suplr_Prvdr_First_Name\n", - " 78. Suplr_Prvdr_Gndr\n", - " 79. Suplr_Prvdr_Last_Name_Org\n", - " 80. Suplr_Prvdr_MI\n", - " 81. Suplr_Prvdr_RUCA\n", - " 82. Suplr_Prvdr_RUCA_Desc\n", - " 83. Suplr_Prvdr_Spclty_Desc\n", - " 84. Suplr_Prvdr_Spclty_Srce\n", - " 85. Suplr_Prvdr_St1\n", - " 86. Suplr_Prvdr_St2\n", - " 87. Suplr_Prvdr_State_Abrvtn\n", - " 88. Suplr_Prvdr_State_FIPS\n", - " 89. Suplr_Prvdr_Zip5\n", - " 90. Suplr_Sbmtd_Chrgs\n", - " 91. Tot_Suplr_Benes\n", - " 92. Tot_Suplr_Clms\n", - " 93. Tot_Suplr_HCPCS_Cds\n", - " 94. Tot_Suplr_Srvcs\n", - "\n", - "Please adjust the script to use the correct column names for your dataset.\n", - "Top 15 suppliers with highest growth rates:\n", - " Supplier NPI Supplier Name Supplier State Year Period Start Year Value End Year Value Growth Rate (%) Absolute Growth\n", - " 1437771474.0 Supplier 1437771474.0 TX 2021-2022 $1,019.39 $9,748,100.46 956168.01% $9,747,081.07\n", - " 1851421663.0 Supplier 1851421663.0 NC 2018-2019 $645.96 $908,231.92 140501.88% $907,585.96\n", - " 1235190232.0 Supplier 1235190232.0 KY 2021-2022 $10,705.50 $9,922,745.68 92588.30% $9,912,040.18\n", - " 1558553040.0 Supplier 1558553040.0 TX 2017-2018 $226.62 $191,169.55 84256.87% $190,942.93\n", - " 1235754094.0 Supplier 1235754094.0 TN 2021-2022 $7,661.90 $6,030,413.89 78606.51% $6,022,751.99\n", - " 1962081679.0 Supplier 1962081679.0 FL 2021-2022 $1,716.12 $1,084,887.33 63117.45% $1,083,171.21\n", - " 1881972040.0 Supplier 1881972040.0 FL 2018-2019 $713.12 $442,124.30 61898.58% $441,411.18\n", - " 1063967768.0 Supplier 1063967768.0 MS 2021-2022 $26,062.96 $15,797,494.45 60512.82% $15,771,431.49\n", - " 1740458694.0 Supplier 1740458694.0 OH 2017-2018 $3.48 $2,034.40 58359.77% $2,030.92\n", - " 1861847824.0 Supplier 1861847824.0 PA 2021-2022 $737.30 $409,565.31 55449.34% $408,828.01\n", - " 1295801421.0 Supplier 1295801421.0 CA 2017-2018 $1,620.57 $812,890.78 50060.79% $811,270.21\n", - " 1316438849.0 Supplier 1316438849.0 OH 2018-2019 $317.81 $158,292.95 49707.42% $157,975.14\n", - " 1952948002.0 Supplier 1952948002.0 KY 2021-2022 $108,739.54 $50,139,168.76 46009.42% $50,030,429.22\n", - " 1891275590.0 Supplier 1891275590.0 CT 2018-2019 $8,142.60 $3,539,476.72 43368.63% $3,531,334.12\n", - " 1043627060.0 Supplier 1043627060.0 CA 2017-2018 $2,063.37 $784,360.94 37913.59% $782,297.57\n", - "\n", - "2. Geographic Fraud Hotspots\n", - "-------------------------\n", - "\n", - "States with highest number of suspicious suppliers:\n", - "State Suspicious Suppliers Average Growth Rate (%) Total Growth ($)\n", - " FL 8 27795.87% $19,559,249.39\n", - " TX 6 187306.97% $27,882,045.41\n", - " WA 4 17343.04% $1,585,521.59\n", - " OH 4 34423.24% $318,671.52\n", - " CA 4 30767.56% $1,782,205.08\n", - " MI 2 20004.69% $406,913.44\n", - " TN 2 46639.87% $6,108,188.37\n", - " PA 2 36701.58% $549,356.09\n", - " IN 2 24899.44% $368,638.47\n", - " KY 2 69298.86% $59,942,469.40\n", - "\n", - "3. Outlier Claim Amount Analysis\n", - "-----------------------------\n", - "\n", - "Identifying suppliers with abnormally high average claim amounts in 2022...\n", - "Warning: No supplier name column found. Using placeholder names.\n", - "\n", - "Available columns in the dataset:\n", - " 1. Bene_Age_65_74_Cnt\n", - " 2. Bene_Age_75_84_Cnt\n", - " 3. Bene_Age_GT_84_Cnt\n", - " 4. Bene_Age_LT_65_Cnt\n", - " 5. Bene_Avg_Age\n", - " 6. Bene_Avg_Risk_Scre\n", - " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", - " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", - " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", - " 10. Bene_CC_BH_Anxiety_V1_Pct\n", - " 11. Bene_CC_BH_Bipolar_V1_Pct\n", - " 12. Bene_CC_BH_Depress_V1_Pct\n", - " 13. Bene_CC_BH_Mood_V2_Pct\n", - " 14. Bene_CC_BH_PD_V1_Pct\n", - " 15. Bene_CC_BH_PTSD_V1_Pct\n", - " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", - " 17. Bene_CC_BH_Tobacco_V1_Pct\n", - " 18. Bene_CC_PH_Afib_V2_Pct\n", - " 19. Bene_CC_PH_Arthritis_V2_Pct\n", - " 20. Bene_CC_PH_Asthma_V2_Pct\n", - " 21. Bene_CC_PH_CKD_V2_Pct\n", - " 22. Bene_CC_PH_COPD_V2_Pct\n", - " 23. Bene_CC_PH_Cancer6_V2_Pct\n", - " 24. Bene_CC_PH_Diabetes_V2_Pct\n", - " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", - " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", - " 27. Bene_CC_PH_Hypertension_V2_Pct\n", - " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", - " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", - " 30. Bene_CC_PH_Parkinson_V2_Pct\n", - " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", - " 32. Bene_Dual_Cnt\n", - " 33. Bene_Feml_Cnt\n", - " 34. Bene_Male_Cnt\n", - " 35. Bene_Ndual_Cnt\n", - " 36. Bene_Race_Api_Cnt\n", - " 37. Bene_Race_Black_Cnt\n", - " 38. Bene_Race_Hspnc_Cnt\n", - " 39. Bene_Race_Natind_Cnt\n", - " 40. Bene_Race_Othr_Cnt\n", - " 41. Bene_Race_Wht_Cnt\n", - " 42. DME_Sprsn_Ind\n", - " 43. DME_Suplr_Mdcr_Alowd_Amt\n", - " 44. DME_Suplr_Mdcr_Pymt_Amt\n", - " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 46. DME_Suplr_Sbmtd_Chrgs\n", - " 47. DME_Tot_Suplr_Benes\n", - " 48. DME_Tot_Suplr_Clms\n", - " 49. DME_Tot_Suplr_HCPCS_Cds\n", - " 50. DME_Tot_Suplr_Srvcs\n", - " 51. Drug_Sprsn_Ind\n", - " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", - " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", - " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 55. Drug_Suplr_Sbmtd_Chrgs\n", - " 56. Drug_Tot_Suplr_Benes\n", - " 57. Drug_Tot_Suplr_Clms\n", - " 58. Drug_Tot_Suplr_HCPCS_Cds\n", - " 59. Drug_Tot_Suplr_Srvcs\n", - " 60. POS_Sprsn_Ind\n", - " 61. POS_Suplr_Mdcr_Alowd_Amt\n", - " 62. POS_Suplr_Mdcr_Pymt_Amt\n", - " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 64. POS_Suplr_Sbmtd_Chrgs\n", - " 65. POS_Tot_Suplr_Benes\n", - " 66. POS_Tot_Suplr_Clms\n", - " 67. POS_Tot_Suplr_HCPCS_Cds\n", - " 68. POS_Tot_Suplr_Srvcs\n", - " 69. Suplr_Mdcr_Alowd_Amt\n", - " 70. Suplr_Mdcr_Pymt_Amt\n", - " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 72. Suplr_NPI\n", - " 73. Suplr_Prvdr_City\n", - " 74. Suplr_Prvdr_Cntry\n", - " 75. Suplr_Prvdr_Crdntls\n", - " 76. Suplr_Prvdr_Ent_Cd\n", - " 77. Suplr_Prvdr_First_Name\n", - " 78. Suplr_Prvdr_Gndr\n", - " 79. Suplr_Prvdr_Last_Name_Org\n", - " 80. Suplr_Prvdr_MI\n", - " 81. Suplr_Prvdr_RUCA\n", - " 82. Suplr_Prvdr_RUCA_Desc\n", - " 83. Suplr_Prvdr_Spclty_Desc\n", - " 84. Suplr_Prvdr_Spclty_Srce\n", - " 85. Suplr_Prvdr_St1\n", - " 86. Suplr_Prvdr_St2\n", - " 87. Suplr_Prvdr_State_Abrvtn\n", - " 88. Suplr_Prvdr_State_FIPS\n", - " 89. Suplr_Prvdr_Zip5\n", - " 90. Suplr_Sbmtd_Chrgs\n", - " 91. Tot_Suplr_Benes\n", - " 92. Tot_Suplr_Clms\n", - " 93. Tot_Suplr_HCPCS_Cds\n", - " 94. Tot_Suplr_Srvcs\n", - "\n", - "Please adjust the script to use the correct column names for your dataset.\n", - "Error: No average charge metrics found in data for year 2022.\n", - "No suppliers with outlier claim amounts detected in 2022.\n", - "\n", - "4. Unusual Beneficiary-to-Claim Ratio Analysis\n", - "-----------------------------------------\n", - "\n", - "Identifying suppliers with unusual claims per beneficiary in 2022...\n", - "Warning: No supplier name column found. Using placeholder names.\n", - "\n", - "Available columns in the dataset:\n", - " 1. Bene_Age_65_74_Cnt\n", - " 2. Bene_Age_75_84_Cnt\n", - " 3. Bene_Age_GT_84_Cnt\n", - " 4. Bene_Age_LT_65_Cnt\n", - " 5. Bene_Avg_Age\n", - " 6. Bene_Avg_Risk_Scre\n", - " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", - " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", - " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", - " 10. Bene_CC_BH_Anxiety_V1_Pct\n", - " 11. Bene_CC_BH_Bipolar_V1_Pct\n", - " 12. Bene_CC_BH_Depress_V1_Pct\n", - " 13. Bene_CC_BH_Mood_V2_Pct\n", - " 14. Bene_CC_BH_PD_V1_Pct\n", - " 15. Bene_CC_BH_PTSD_V1_Pct\n", - " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", - " 17. Bene_CC_BH_Tobacco_V1_Pct\n", - " 18. Bene_CC_PH_Afib_V2_Pct\n", - " 19. Bene_CC_PH_Arthritis_V2_Pct\n", - " 20. Bene_CC_PH_Asthma_V2_Pct\n", - " 21. Bene_CC_PH_CKD_V2_Pct\n", - " 22. Bene_CC_PH_COPD_V2_Pct\n", - " 23. Bene_CC_PH_Cancer6_V2_Pct\n", - " 24. Bene_CC_PH_Diabetes_V2_Pct\n", - " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", - " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", - " 27. Bene_CC_PH_Hypertension_V2_Pct\n", - " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", - " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", - " 30. Bene_CC_PH_Parkinson_V2_Pct\n", - " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", - " 32. Bene_Dual_Cnt\n", - " 33. Bene_Feml_Cnt\n", - " 34. Bene_Male_Cnt\n", - " 35. Bene_Ndual_Cnt\n", - " 36. Bene_Race_Api_Cnt\n", - " 37. Bene_Race_Black_Cnt\n", - " 38. Bene_Race_Hspnc_Cnt\n", - " 39. Bene_Race_Natind_Cnt\n", - " 40. Bene_Race_Othr_Cnt\n", - " 41. Bene_Race_Wht_Cnt\n", - " 42. DME_Sprsn_Ind\n", - " 43. DME_Suplr_Mdcr_Alowd_Amt\n", - " 44. DME_Suplr_Mdcr_Pymt_Amt\n", - " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 46. DME_Suplr_Sbmtd_Chrgs\n", - " 47. DME_Tot_Suplr_Benes\n", - " 48. DME_Tot_Suplr_Clms\n", - " 49. DME_Tot_Suplr_HCPCS_Cds\n", - " 50. DME_Tot_Suplr_Srvcs\n", - " 51. Drug_Sprsn_Ind\n", - " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", - " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", - " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 55. Drug_Suplr_Sbmtd_Chrgs\n", - " 56. Drug_Tot_Suplr_Benes\n", - " 57. Drug_Tot_Suplr_Clms\n", - " 58. Drug_Tot_Suplr_HCPCS_Cds\n", - " 59. Drug_Tot_Suplr_Srvcs\n", - " 60. POS_Sprsn_Ind\n", - " 61. POS_Suplr_Mdcr_Alowd_Amt\n", - " 62. POS_Suplr_Mdcr_Pymt_Amt\n", - " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 64. POS_Suplr_Sbmtd_Chrgs\n", - " 65. POS_Tot_Suplr_Benes\n", - " 66. POS_Tot_Suplr_Clms\n", - " 67. POS_Tot_Suplr_HCPCS_Cds\n", - " 68. POS_Tot_Suplr_Srvcs\n", - " 69. Suplr_Mdcr_Alowd_Amt\n", - " 70. Suplr_Mdcr_Pymt_Amt\n", - " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 72. Suplr_NPI\n", - " 73. Suplr_Prvdr_City\n", - " 74. Suplr_Prvdr_Cntry\n", - " 75. Suplr_Prvdr_Crdntls\n", - " 76. Suplr_Prvdr_Ent_Cd\n", - " 77. Suplr_Prvdr_First_Name\n", - " 78. Suplr_Prvdr_Gndr\n", - " 79. Suplr_Prvdr_Last_Name_Org\n", - " 80. Suplr_Prvdr_MI\n", - " 81. Suplr_Prvdr_RUCA\n", - " 82. Suplr_Prvdr_RUCA_Desc\n", - " 83. Suplr_Prvdr_Spclty_Desc\n", - " 84. Suplr_Prvdr_Spclty_Srce\n", - " 85. Suplr_Prvdr_St1\n", - " 86. Suplr_Prvdr_St2\n", - " 87. Suplr_Prvdr_State_Abrvtn\n", - " 88. Suplr_Prvdr_State_FIPS\n", - " 89. Suplr_Prvdr_Zip5\n", - " 90. Suplr_Sbmtd_Chrgs\n", - " 91. Tot_Suplr_Benes\n", - " 92. Tot_Suplr_Clms\n", - " 93. Tot_Suplr_HCPCS_Cds\n", - " 94. Tot_Suplr_Srvcs\n", - "\n", - "Please adjust the script to use the correct column names for your dataset.\n", - "Top 10 suppliers with unusual claims per beneficiary in 2022:\n", - " Suplr_NPI Suplr_Prvdr_Org_Name Suplr_Prvdr_State_Abrvtn DME_Tot_Suplr_Benes DME_Tot_Suplr_Clms Claims_Per_Beneficiary Claims_Per_Beneficiary_zscore\n", - "1902023013 Supplier 1902023013 TX 18.0 828.0 46.00 29.92\n", - "1477647337 Supplier 1477647337 NJ 13.0 464.0 35.69 22.67\n", - "1912199381 Supplier 1912199381 TX 59.0 1771.0 30.02 18.68\n", - "1043656614 Supplier 1043656614 FL 12.0 324.0 27.00 16.56\n", - "1316924061 Supplier 1316924061 MI 13.0 333.0 25.62 15.58\n", - "1942216577 Supplier 1942216577 CA 13.0 313.0 24.08 14.50\n", - "1437649456 Supplier 1437649456 NJ 24.0 538.0 22.42 13.33\n", - "1891736286 Supplier 1891736286 CA 57.0 1260.0 22.11 13.11\n", - "1952312456 Supplier 1952312456 IL 18.0 373.0 20.72 12.14\n", - "1053412643 Supplier 1053412643 NJ 13.0 268.0 20.62 12.07\n", - "\n", - "5. Combined Fraud Indicators\n", - "-------------------------\n", - "\n", - "Identifying suppliers with multiple fraud indicators...\n", - "Warning: One or more fraud indicator dataframes are empty. Cannot perform combined analysis.\n", - "No suppliers with multiple fraud indicators identified.\n", - "\n", - "\n", - "6. Generating Fraud Detection Visualizations\n", - "------------------------------------------\n", - "\n", - "\n", - "Column names available in the most recent year's data:\n", - " 1. Bene_Age_65_74_Cnt\n", - " 2. Bene_Age_75_84_Cnt\n", - " 3. Bene_Age_GT_84_Cnt\n", - " 4. Bene_Age_LT_65_Cnt\n", - " 5. Bene_Avg_Age\n", - " 6. Bene_Avg_Risk_Scre\n", - " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", - " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", - " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", - " 10. Bene_CC_BH_Anxiety_V1_Pct\n", - " 11. Bene_CC_BH_Bipolar_V1_Pct\n", - " 12. Bene_CC_BH_Depress_V1_Pct\n", - " 13. Bene_CC_BH_Mood_V2_Pct\n", - " 14. Bene_CC_BH_PD_V1_Pct\n", - " 15. Bene_CC_BH_PTSD_V1_Pct\n", - " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", - " 17. Bene_CC_BH_Tobacco_V1_Pct\n", - " 18. Bene_CC_PH_Afib_V2_Pct\n", - " 19. Bene_CC_PH_Arthritis_V2_Pct\n", - " 20. Bene_CC_PH_Asthma_V2_Pct\n", - " 21. Bene_CC_PH_CKD_V2_Pct\n", - " 22. Bene_CC_PH_COPD_V2_Pct\n", - " 23. Bene_CC_PH_Cancer6_V2_Pct\n", - " 24. Bene_CC_PH_Diabetes_V2_Pct\n", - " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", - " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", - " 27. Bene_CC_PH_Hypertension_V2_Pct\n", - " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", - " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", - " 30. Bene_CC_PH_Parkinson_V2_Pct\n", - " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", - " 32. Bene_Dual_Cnt\n", - " 33. Bene_Feml_Cnt\n", - " 34. Bene_Male_Cnt\n", - " 35. Bene_Ndual_Cnt\n", - " 36. Bene_Race_Api_Cnt\n", - " 37. Bene_Race_Black_Cnt\n", - " 38. Bene_Race_Hspnc_Cnt\n", - " 39. Bene_Race_Natind_Cnt\n", - " 40. Bene_Race_Othr_Cnt\n", - " 41. Bene_Race_Wht_Cnt\n", - " 42. DME_Sprsn_Ind\n", - " 43. DME_Suplr_Mdcr_Alowd_Amt\n", - " 44. DME_Suplr_Mdcr_Pymt_Amt\n", - " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 46. DME_Suplr_Sbmtd_Chrgs\n", - " 47. DME_Tot_Suplr_Benes\n", - " 48. DME_Tot_Suplr_Clms\n", - " 49. DME_Tot_Suplr_HCPCS_Cds\n", - " 50. DME_Tot_Suplr_Srvcs\n", - " 51. Drug_Sprsn_Ind\n", - " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", - " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", - " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 55. Drug_Suplr_Sbmtd_Chrgs\n", - " 56. Drug_Tot_Suplr_Benes\n", - " 57. Drug_Tot_Suplr_Clms\n", - " 58. Drug_Tot_Suplr_HCPCS_Cds\n", - " 59. Drug_Tot_Suplr_Srvcs\n", - " 60. POS_Sprsn_Ind\n", - " 61. POS_Suplr_Mdcr_Alowd_Amt\n", - " 62. POS_Suplr_Mdcr_Pymt_Amt\n", - " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 64. POS_Suplr_Sbmtd_Chrgs\n", - " 65. POS_Tot_Suplr_Benes\n", - " 66. POS_Tot_Suplr_Clms\n", - " 67. POS_Tot_Suplr_HCPCS_Cds\n", - " 68. POS_Tot_Suplr_Srvcs\n", - " 69. Suplr_Mdcr_Alowd_Amt\n", - " 70. Suplr_Mdcr_Pymt_Amt\n", - " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 72. Suplr_NPI\n", - " 73. Suplr_Prvdr_City\n", - " 74. Suplr_Prvdr_Cntry\n", - " 75. Suplr_Prvdr_Crdntls\n", - " 76. Suplr_Prvdr_Ent_Cd\n", - " 77. Suplr_Prvdr_First_Name\n", - " 78. Suplr_Prvdr_Gndr\n", - " 79. Suplr_Prvdr_Last_Name_Org\n", - " 80. Suplr_Prvdr_MI\n", - " 81. Suplr_Prvdr_RUCA\n", - " 82. Suplr_Prvdr_RUCA_Desc\n", - " 83. Suplr_Prvdr_Spclty_Desc\n", - " 84. Suplr_Prvdr_Spclty_Srce\n", - " 85. Suplr_Prvdr_St1\n", - " 86. Suplr_Prvdr_St2\n", - " 87. Suplr_Prvdr_State_Abrvtn\n", - " 88. Suplr_Prvdr_State_FIPS\n", - " 89. Suplr_Prvdr_Zip5\n", - " 90. Suplr_Sbmtd_Chrgs\n", - " 91. Tot_Suplr_Benes\n", - " 92. Tot_Suplr_Clms\n", - " 93. Tot_Suplr_HCPCS_Cds\n", - " 94. Tot_Suplr_Srvcs\n", - "\n", - "Detecting high growth suppliers...\n", - "Identifying suppliers with highest year-over-year growth rates...\n", - "Warning: No supplier name column found. Using placeholder names.\n", - "\n", - "Available columns in the dataset:\n", - " 1. Bene_Age_65_74_Cnt\n", - " 2. Bene_Age_75_84_Cnt\n", - " 3. Bene_Age_GT_84_Cnt\n", - " 4. Bene_Age_LT_65_Cnt\n", - " 5. Bene_Avg_Age\n", - " 6. Bene_Avg_Risk_Scre\n", - " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", - " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", - " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", - " 10. Bene_CC_BH_Anxiety_V1_Pct\n", - " 11. Bene_CC_BH_Bipolar_V1_Pct\n", - " 12. Bene_CC_BH_Depress_V1_Pct\n", - " 13. Bene_CC_BH_Mood_V2_Pct\n", - " 14. Bene_CC_BH_PD_V1_Pct\n", - " 15. Bene_CC_BH_PTSD_V1_Pct\n", - " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", - " 17. Bene_CC_BH_Tobacco_V1_Pct\n", - " 18. Bene_CC_PH_Afib_V2_Pct\n", - " 19. Bene_CC_PH_Arthritis_V2_Pct\n", - " 20. Bene_CC_PH_Asthma_V2_Pct\n", - " 21. Bene_CC_PH_CKD_V2_Pct\n", - " 22. Bene_CC_PH_COPD_V2_Pct\n", - " 23. Bene_CC_PH_Cancer6_V2_Pct\n", - " 24. Bene_CC_PH_Diabetes_V2_Pct\n", - " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", - " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", - " 27. Bene_CC_PH_Hypertension_V2_Pct\n", - " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", - " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", - " 30. Bene_CC_PH_Parkinson_V2_Pct\n", - " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", - " 32. Bene_Dual_Cnt\n", - " 33. Bene_Feml_Cnt\n", - " 34. Bene_Male_Cnt\n", - " 35. Bene_Ndual_Cnt\n", - " 36. Bene_Race_Api_Cnt\n", - " 37. Bene_Race_Black_Cnt\n", - " 38. Bene_Race_Hspnc_Cnt\n", - " 39. Bene_Race_Natind_Cnt\n", - " 40. Bene_Race_Othr_Cnt\n", - " 41. Bene_Race_Wht_Cnt\n", - " 42. DME_Sprsn_Ind\n", - " 43. DME_Suplr_Mdcr_Alowd_Amt\n", - " 44. DME_Suplr_Mdcr_Pymt_Amt\n", - " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 46. DME_Suplr_Sbmtd_Chrgs\n", - " 47. DME_Tot_Suplr_Benes\n", - " 48. DME_Tot_Suplr_Clms\n", - " 49. DME_Tot_Suplr_HCPCS_Cds\n", - " 50. DME_Tot_Suplr_Srvcs\n", - " 51. Drug_Sprsn_Ind\n", - " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", - " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", - " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 55. Drug_Suplr_Sbmtd_Chrgs\n", - " 56. Drug_Tot_Suplr_Benes\n", - " 57. Drug_Tot_Suplr_Clms\n", - " 58. Drug_Tot_Suplr_HCPCS_Cds\n", - " 59. Drug_Tot_Suplr_Srvcs\n", - " 60. POS_Sprsn_Ind\n", - " 61. POS_Suplr_Mdcr_Alowd_Amt\n", - " 62. POS_Suplr_Mdcr_Pymt_Amt\n", - " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 64. POS_Suplr_Sbmtd_Chrgs\n", - " 65. POS_Tot_Suplr_Benes\n", - " 66. POS_Tot_Suplr_Clms\n", - " 67. POS_Tot_Suplr_HCPCS_Cds\n", - " 68. POS_Tot_Suplr_Srvcs\n", - " 69. Suplr_Mdcr_Alowd_Amt\n", - " 70. Suplr_Mdcr_Pymt_Amt\n", - " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 72. Suplr_NPI\n", - " 73. Suplr_Prvdr_City\n", - " 74. Suplr_Prvdr_Cntry\n", - " 75. Suplr_Prvdr_Crdntls\n", - " 76. Suplr_Prvdr_Ent_Cd\n", - " 77. Suplr_Prvdr_First_Name\n", - " 78. Suplr_Prvdr_Gndr\n", - " 79. Suplr_Prvdr_Last_Name_Org\n", - " 80. Suplr_Prvdr_MI\n", - " 81. Suplr_Prvdr_RUCA\n", - " 82. Suplr_Prvdr_RUCA_Desc\n", - " 83. Suplr_Prvdr_Spclty_Desc\n", - " 84. Suplr_Prvdr_Spclty_Srce\n", - " 85. Suplr_Prvdr_St1\n", - " 86. Suplr_Prvdr_St2\n", - " 87. Suplr_Prvdr_State_Abrvtn\n", - " 88. Suplr_Prvdr_State_FIPS\n", - " 89. Suplr_Prvdr_Zip5\n", - " 90. Suplr_Sbmtd_Chrgs\n", - " 91. Tot_Suplr_Benes\n", - " 92. Tot_Suplr_Clms\n", - " 93. Tot_Suplr_HCPCS_Cds\n", - " 94. Tot_Suplr_Srvcs\n", - "\n", - "Please adjust the script to use the correct column names for your dataset.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_18399/2260217778.py:489: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " bars = sns.barplot(\n", - "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_18399/2260217778.py:581: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "Loading data from data/2018/mup_dme_ry24_p05_v10_dy18_supr.csv\n", + "Loading data from data/2019/mup_dme_ry24_p05_v10_dy19_supr.csv\n", + "Loading data from data/2020/mup_dme_ry24_p05_v10_dy20_supr.csv\n", + "Loading data from data/2021/mup_dme_ry24_p05_v10_dy21_supr.csv\n", + "Loading data from data/2022/mup_dme_ry24_p05_v10_dy22_supr.csv\n", "\n", - " sns.barplot(\n", - "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_18399/2260217778.py:596: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(\n" + "Combined DataFrame shape: (352611, 95)\n" ] - }, + } + ], + "source": [ + "data_dir = 'data' # Adjust if your data folder is elsewhere\n", + "years = range(2018, 2023) # 2018 to 2022\n", + "\n", + "dfs = []\n", + "\n", + "for year in years:\n", + " csv_files = glob.glob(f\"{data_dir}/{year}/*.csv\")\n", + " if not csv_files:\n", + " print(f\"No CSV files found for year {year}\")\n", + " continue\n", + " \n", + " # Take the first CSV found\n", + " csv_file = csv_files[0]\n", + " print(f\"Loading data from {csv_file}\")\n", + " \n", + " # Read the CSV, then add a 'year' column\n", + " df = pd.read_csv(csv_file, low_memory=False)\n", + " df['year'] = year\n", + " \n", + " dfs.append(df)\n", + "\n", + "if dfs:\n", + " combined_df = pd.concat(dfs, ignore_index=True)\n", + " print(f\"\\nCombined DataFrame shape: {combined_df.shape}\")\n", + "else:\n", + " combined_df = pd.DataFrame()\n", + " print(\"No data was loaded.\")" + ] + }, + { + "cell_type": "markdown", + "id": "37a28f37", + "metadata": {}, + "source": [ + "### Basic Exploration\n", + "Let's do a quick look at the combined DataFrame's structure, and ensure we have the columns we expect." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c7036199", + "metadata": { + "tags": [] + }, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "✓ High growth suppliers visualization created successfully.\n", - "\n", - "Detecting geographic fraud hotspots...\n", - "✓ Geographic hotspots visualization created successfully.\n", - "\n", - "Detecting outlier claim amounts...\n", - "Identifying suppliers with abnormally high average claim amounts in 2022...\n", - "Warning: No supplier name column found. Using placeholder names.\n", - "\n", - "Available columns in the dataset:\n", - " 1. Bene_Age_65_74_Cnt\n", - " 2. Bene_Age_75_84_Cnt\n", - " 3. Bene_Age_GT_84_Cnt\n", - " 4. Bene_Age_LT_65_Cnt\n", - " 5. Bene_Avg_Age\n", - " 6. Bene_Avg_Risk_Scre\n", - " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", - " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", - " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", - " 10. Bene_CC_BH_Anxiety_V1_Pct\n", - " 11. Bene_CC_BH_Bipolar_V1_Pct\n", - " 12. Bene_CC_BH_Depress_V1_Pct\n", - " 13. Bene_CC_BH_Mood_V2_Pct\n", - " 14. Bene_CC_BH_PD_V1_Pct\n", - " 15. Bene_CC_BH_PTSD_V1_Pct\n", - " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", - " 17. Bene_CC_BH_Tobacco_V1_Pct\n", - " 18. Bene_CC_PH_Afib_V2_Pct\n", - " 19. Bene_CC_PH_Arthritis_V2_Pct\n", - " 20. Bene_CC_PH_Asthma_V2_Pct\n", - " 21. Bene_CC_PH_CKD_V2_Pct\n", - " 22. Bene_CC_PH_COPD_V2_Pct\n", - " 23. Bene_CC_PH_Cancer6_V2_Pct\n", - " 24. Bene_CC_PH_Diabetes_V2_Pct\n", - " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", - " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", - " 27. Bene_CC_PH_Hypertension_V2_Pct\n", - " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", - " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", - " 30. Bene_CC_PH_Parkinson_V2_Pct\n", - " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", - " 32. Bene_Dual_Cnt\n", - " 33. Bene_Feml_Cnt\n", - " 34. Bene_Male_Cnt\n", - " 35. Bene_Ndual_Cnt\n", - " 36. Bene_Race_Api_Cnt\n", - " 37. Bene_Race_Black_Cnt\n", - " 38. Bene_Race_Hspnc_Cnt\n", - " 39. Bene_Race_Natind_Cnt\n", - " 40. Bene_Race_Othr_Cnt\n", - " 41. Bene_Race_Wht_Cnt\n", - " 42. DME_Sprsn_Ind\n", - " 43. DME_Suplr_Mdcr_Alowd_Amt\n", - " 44. DME_Suplr_Mdcr_Pymt_Amt\n", - " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 46. DME_Suplr_Sbmtd_Chrgs\n", - " 47. DME_Tot_Suplr_Benes\n", - " 48. DME_Tot_Suplr_Clms\n", - " 49. DME_Tot_Suplr_HCPCS_Cds\n", - " 50. DME_Tot_Suplr_Srvcs\n", - " 51. Drug_Sprsn_Ind\n", - " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", - " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", - " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 55. Drug_Suplr_Sbmtd_Chrgs\n", - " 56. Drug_Tot_Suplr_Benes\n", - " 57. Drug_Tot_Suplr_Clms\n", - " 58. Drug_Tot_Suplr_HCPCS_Cds\n", - " 59. Drug_Tot_Suplr_Srvcs\n", - " 60. POS_Sprsn_Ind\n", - " 61. POS_Suplr_Mdcr_Alowd_Amt\n", - " 62. POS_Suplr_Mdcr_Pymt_Amt\n", - " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 64. POS_Suplr_Sbmtd_Chrgs\n", - " 65. POS_Tot_Suplr_Benes\n", - " 66. POS_Tot_Suplr_Clms\n", - " 67. POS_Tot_Suplr_HCPCS_Cds\n", - " 68. POS_Tot_Suplr_Srvcs\n", - " 69. Suplr_Mdcr_Alowd_Amt\n", - " 70. Suplr_Mdcr_Pymt_Amt\n", - " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 72. Suplr_NPI\n", - " 73. Suplr_Prvdr_City\n", - " 74. Suplr_Prvdr_Cntry\n", - " 75. Suplr_Prvdr_Crdntls\n", - " 76. Suplr_Prvdr_Ent_Cd\n", - " 77. Suplr_Prvdr_First_Name\n", - " 78. Suplr_Prvdr_Gndr\n", - " 79. Suplr_Prvdr_Last_Name_Org\n", - " 80. Suplr_Prvdr_MI\n", - " 81. Suplr_Prvdr_RUCA\n", - " 82. Suplr_Prvdr_RUCA_Desc\n", - " 83. Suplr_Prvdr_Spclty_Desc\n", - " 84. Suplr_Prvdr_Spclty_Srce\n", - " 85. Suplr_Prvdr_St1\n", - " 86. Suplr_Prvdr_St2\n", - " 87. Suplr_Prvdr_State_Abrvtn\n", - " 88. Suplr_Prvdr_State_FIPS\n", - " 89. Suplr_Prvdr_Zip5\n", - " 90. Suplr_Sbmtd_Chrgs\n", - " 91. Tot_Suplr_Benes\n", - " 92. Tot_Suplr_Clms\n", - " 93. Tot_Suplr_HCPCS_Cds\n", - " 94. Tot_Suplr_Srvcs\n", - "\n", - "Please adjust the script to use the correct column names for your dataset.\n", - "Error: No average charge metrics found in data for year 2022.\n", - "⚠ No outlier claim amounts identified.\n", - "\n", - "Detecting unusual beneficiary-to-claim ratios...\n", - "Identifying suppliers with unusual claims per beneficiary in 2022...\n", - "Warning: No supplier name column found. Using placeholder names.\n", - "\n", - "Available columns in the dataset:\n", - " 1. Bene_Age_65_74_Cnt\n", - " 2. Bene_Age_75_84_Cnt\n", - " 3. Bene_Age_GT_84_Cnt\n", - " 4. Bene_Age_LT_65_Cnt\n", - " 5. Bene_Avg_Age\n", - " 6. Bene_Avg_Risk_Scre\n", - " 7. Bene_CC_BH_ADHD_OthCD_V1_Pct\n", - " 8. Bene_CC_BH_Alcohol_Drug_V1_Pct\n", - " 9. Bene_CC_BH_Alz_NonAlzdem_V2_Pct\n", - " 10. Bene_CC_BH_Anxiety_V1_Pct\n", - " 11. Bene_CC_BH_Bipolar_V1_Pct\n", - " 12. Bene_CC_BH_Depress_V1_Pct\n", - " 13. Bene_CC_BH_Mood_V2_Pct\n", - " 14. Bene_CC_BH_PD_V1_Pct\n", - " 15. Bene_CC_BH_PTSD_V1_Pct\n", - " 16. Bene_CC_BH_Schizo_OthPsy_V1_Pct\n", - " 17. Bene_CC_BH_Tobacco_V1_Pct\n", - " 18. Bene_CC_PH_Afib_V2_Pct\n", - " 19. Bene_CC_PH_Arthritis_V2_Pct\n", - " 20. Bene_CC_PH_Asthma_V2_Pct\n", - " 21. Bene_CC_PH_CKD_V2_Pct\n", - " 22. Bene_CC_PH_COPD_V2_Pct\n", - " 23. Bene_CC_PH_Cancer6_V2_Pct\n", - " 24. Bene_CC_PH_Diabetes_V2_Pct\n", - " 25. Bene_CC_PH_HF_NonIHD_V2_Pct\n", - " 26. Bene_CC_PH_Hyperlipidemia_V2_Pct\n", - " 27. Bene_CC_PH_Hypertension_V2_Pct\n", - " 28. Bene_CC_PH_IschemicHeart_V2_Pct\n", - " 29. Bene_CC_PH_Osteoporosis_V2_Pct\n", - " 30. Bene_CC_PH_Parkinson_V2_Pct\n", - " 31. Bene_CC_PH_Stroke_TIA_V2_Pct\n", - " 32. Bene_Dual_Cnt\n", - " 33. Bene_Feml_Cnt\n", - " 34. Bene_Male_Cnt\n", - " 35. Bene_Ndual_Cnt\n", - " 36. Bene_Race_Api_Cnt\n", - " 37. Bene_Race_Black_Cnt\n", - " 38. Bene_Race_Hspnc_Cnt\n", - " 39. Bene_Race_Natind_Cnt\n", - " 40. Bene_Race_Othr_Cnt\n", - " 41. Bene_Race_Wht_Cnt\n", - " 42. DME_Sprsn_Ind\n", - " 43. DME_Suplr_Mdcr_Alowd_Amt\n", - " 44. DME_Suplr_Mdcr_Pymt_Amt\n", - " 45. DME_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 46. DME_Suplr_Sbmtd_Chrgs\n", - " 47. DME_Tot_Suplr_Benes\n", - " 48. DME_Tot_Suplr_Clms\n", - " 49. DME_Tot_Suplr_HCPCS_Cds\n", - " 50. DME_Tot_Suplr_Srvcs\n", - " 51. Drug_Sprsn_Ind\n", - " 52. Drug_Suplr_Mdcr_Alowd_Amt\n", - " 53. Drug_Suplr_Mdcr_Pymt_Amt\n", - " 54. Drug_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 55. Drug_Suplr_Sbmtd_Chrgs\n", - " 56. Drug_Tot_Suplr_Benes\n", - " 57. Drug_Tot_Suplr_Clms\n", - " 58. Drug_Tot_Suplr_HCPCS_Cds\n", - " 59. Drug_Tot_Suplr_Srvcs\n", - " 60. POS_Sprsn_Ind\n", - " 61. POS_Suplr_Mdcr_Alowd_Amt\n", - " 62. POS_Suplr_Mdcr_Pymt_Amt\n", - " 63. POS_Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 64. POS_Suplr_Sbmtd_Chrgs\n", - " 65. POS_Tot_Suplr_Benes\n", - " 66. POS_Tot_Suplr_Clms\n", - " 67. POS_Tot_Suplr_HCPCS_Cds\n", - " 68. POS_Tot_Suplr_Srvcs\n", - " 69. Suplr_Mdcr_Alowd_Amt\n", - " 70. Suplr_Mdcr_Pymt_Amt\n", - " 71. Suplr_Mdcr_Stdzd_Pymt_Amt\n", - " 72. Suplr_NPI\n", - " 73. Suplr_Prvdr_City\n", - " 74. Suplr_Prvdr_Cntry\n", - " 75. Suplr_Prvdr_Crdntls\n", - " 76. Suplr_Prvdr_Ent_Cd\n", - " 77. Suplr_Prvdr_First_Name\n", - " 78. Suplr_Prvdr_Gndr\n", - " 79. Suplr_Prvdr_Last_Name_Org\n", - " 80. Suplr_Prvdr_MI\n", - " 81. Suplr_Prvdr_RUCA\n", - " 82. Suplr_Prvdr_RUCA_Desc\n", - " 83. Suplr_Prvdr_Spclty_Desc\n", - " 84. Suplr_Prvdr_Spclty_Srce\n", - " 85. Suplr_Prvdr_St1\n", - " 86. Suplr_Prvdr_St2\n", - " 87. Suplr_Prvdr_State_Abrvtn\n", - " 88. Suplr_Prvdr_State_FIPS\n", - " 89. Suplr_Prvdr_Zip5\n", - " 90. Suplr_Sbmtd_Chrgs\n", - " 91. Tot_Suplr_Benes\n", - " 92. Tot_Suplr_Clms\n", - " 93. Tot_Suplr_HCPCS_Cds\n", - " 94. Tot_Suplr_Srvcs\n", - "\n", - "Please adjust the script to use the correct column names for your dataset.\n", - "✓ Unusual beneficiary-to-claim ratios visualization created successfully.\n", - "\n", - "Identifying suppliers with multiple fraud indicators...\n", - "Identifying suppliers with multiple fraud indicators...\n", - "Warning: One or more fraud indicator dataframes are empty. Cannot perform combined analysis.\n", - "⚠ No suppliers with multiple fraud indicators identified.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_18399/2260217778.py:705: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", "\n", - " bars = sns.barplot(\n" + "First few rows:\n" ] }, { "data": { - "image/png": "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", + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Suplr_NPISuplr_Prvdr_Last_Name_OrgSuplr_Prvdr_First_NameSuplr_Prvdr_MISuplr_Prvdr_CrdntlsSuplr_Prvdr_GndrSuplr_Prvdr_Ent_CdSuplr_Prvdr_St1Suplr_Prvdr_St2Suplr_Prvdr_CitySuplr_Prvdr_State_AbrvtnSuplr_Prvdr_State_FIPSSuplr_Prvdr_Zip5Suplr_Prvdr_RUCASuplr_Prvdr_RUCA_DescSuplr_Prvdr_CntrySuplr_Prvdr_Spclty_DescSuplr_Prvdr_Spclty_SrceTot_Suplr_HCPCS_CdsTot_Suplr_BenesTot_Suplr_ClmsTot_Suplr_SrvcsSuplr_Sbmtd_ChrgsSuplr_Mdcr_Alowd_AmtSuplr_Mdcr_Pymt_AmtSuplr_Mdcr_Stdzd_Pymt_AmtDME_Sprsn_IndDME_Tot_Suplr_HCPCS_CdsDME_Tot_Suplr_BenesDME_Tot_Suplr_ClmsDME_Tot_Suplr_SrvcsDME_Suplr_Sbmtd_ChrgsDME_Suplr_Mdcr_Alowd_AmtDME_Suplr_Mdcr_Pymt_AmtDME_Suplr_Mdcr_Stdzd_Pymt_AmtPOS_Sprsn_IndPOS_Tot_Suplr_HCPCS_CdsPOS_Tot_Suplr_BenesPOS_Tot_Suplr_ClmsPOS_Tot_Suplr_SrvcsPOS_Suplr_Sbmtd_ChrgsPOS_Suplr_Mdcr_Alowd_AmtPOS_Suplr_Mdcr_Pymt_AmtPOS_Suplr_Mdcr_Stdzd_Pymt_AmtDrug_Sprsn_IndDrug_Tot_Suplr_HCPCS_CdsDrug_Tot_Suplr_BenesDrug_Tot_Suplr_ClmsDrug_Tot_Suplr_SrvcsDrug_Suplr_Sbmtd_ChrgsDrug_Suplr_Mdcr_Alowd_AmtDrug_Suplr_Mdcr_Pymt_AmtDrug_Suplr_Mdcr_Stdzd_Pymt_AmtBene_Avg_AgeBene_Age_LT_65_CntBene_Age_65_74_CntBene_Age_75_84_CntBene_Age_GT_84_CntBene_Feml_CntBene_Male_CntBene_Race_Wht_CntBene_Race_Black_CntBene_Race_Api_CntBene_Race_Hspnc_CntBene_Race_Natind_CntBene_Race_Othr_CntBene_Ndual_CntBene_Dual_CntBene_CC_BH_ADHD_OthCD_V1_PctBene_CC_BH_Alcohol_Drug_V1_PctBene_CC_BH_Tobacco_V1_PctBene_CC_BH_Alz_NonAlzdem_V2_PctBene_CC_BH_Anxiety_V1_PctBene_CC_BH_Bipolar_V1_PctBene_CC_BH_Mood_V2_PctBene_CC_BH_Depress_V1_PctBene_CC_BH_PD_V1_PctBene_CC_BH_PTSD_V1_PctBene_CC_BH_Schizo_OthPsy_V1_PctBene_CC_PH_Asthma_V2_PctBene_CC_PH_Afib_V2_PctBene_CC_PH_Cancer6_V2_PctBene_CC_PH_CKD_V2_PctBene_CC_PH_COPD_V2_PctBene_CC_PH_Diabetes_V2_PctBene_CC_PH_HF_NonIHD_V2_PctBene_CC_PH_Hyperlipidemia_V2_PctBene_CC_PH_Hypertension_V2_PctBene_CC_PH_IschemicHeart_V2_PctBene_CC_PH_Osteoporosis_V2_PctBene_CC_PH_Parkinson_V2_PctBene_CC_PH_Arthritis_V2_PctBene_CC_PH_Stroke_TIA_V2_PctBene_Avg_Risk_Screyear
01003000399Reconstructive Hand To Shoulder Of Indiana, LlcNaNNaNNaNNaNO13431 Old Meridian StreetSuite 225CarmelIN18460321.0Metropolitan area core: primary flow within an...USGeneral SurgeryClaim-Specialty15235.030134083033.0070600.4054545.8556320.86NaN0.00.00.00.00.000.000.000.00NaN15.0235.0301.0340.083033.0070600.4054545.8556320.86NaN0.00.00.00.00.000.000.00.072.86266120.0120.074.021.0148.087.0222.0NaNNaNNaN0.0NaN220.015.0NaN0.0510640.102128NaN0.200000NaN0.2553190.234043NaNNaNNaN0.0851060.0851060.1319150.1021280.1659570.2170210.0936170.6765960.6680850.2297870.1446810.00.6468090.0468090.9758012018
11003000845James D.Schlenker MdscNaNNaNNaNNaNO6311 W 95th StNaNOak LawnIL17604531.0Metropolitan area core: primary flow within an...USPlastic and Reconstructive SurgeryClaim-Specialty819.022224168.004034.223138.124635.72NaN0.00.00.00.00.000.000.000.00NaN8.019.022.022.04168.004034.223138.124635.72NaN0.00.00.00.00.000.000.00.074.6315790.011.0NaNNaNNaNNaN16.0NaN0.0NaN0.0NaNNaNNaN0.00.0000000.000000NaNNaN0.0NaNNaN0.00.00.00.000000NaNNaNNaNNaNNaN0.0000000.8421050.736842NaNNaNNaN0.736842NaN1.0650532018
21003001934Yi Rui International CorpNaNNaNNaNNaNO4307 8th AveNaNBrooklynNY36112321.0Metropolitan area core: primary flow within an...USPharmacyClaim-Specialty5NaN377962739.60549.08339.39407.47NaN4.0NaN35.046.02448.28512.46321.75389.83#NaNNaNNaNNaNNaNNaNNaNNaN*NaNNaNNaNNaNNaNNaNNaNNaN75.285714NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.6685782018
31003002254Walgreen Co.NaNNaNNaNNaNO5104 Bobby Hicks HwyNaNGrayTN47376151.0Metropolitan area core: primary flow within an...USCentralized FluClaim-Specialty1056.0150368131078.365276.873699.853835.41NaN6.056.0148.0390.026475.283226.012111.052246.61NaN0.00.00.00.00.000.000.000.00NaN4.0NaN12.03291.04603.082050.861588.81588.871.240000NaN28.016.0NaN37.019.056.00.00.00.00.00.045.011.00.0NaNNaNNaN0.214286NaN0.2857140.2678570.00.00.00.196429NaNNaN0.196429NaN0.821429NaN0.7142860.8392860.232143NaN0.00.446429NaN1.1719452018
41003002767Thomas J Mcelligott Md PcNaNNaNNaNNaNO2415 Wall St SeSuite BConyersGA13300131.0Metropolitan area core: primary flow within an...USOrthopedic SurgeryClaim-Specialty1038.044454920.714808.813344.823420.32NaN0.00.00.00.00.000.000.000.00NaN10.038.044.045.04920.714808.813344.823420.32NaN0.00.00.00.00.000.000.00.073.727273NaN15.013.0NaN26.012.026.011.0NaN0.00.0NaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaN0.6315790.631579NaNNaN0.00.447368NaN1.3028572018
\n", + "
" + ], "text/plain": [ - "
" + " Suplr_NPI Suplr_Prvdr_Last_Name_Org Suplr_Prvdr_First_Name Suplr_Prvdr_MI Suplr_Prvdr_Crdntls Suplr_Prvdr_Gndr Suplr_Prvdr_Ent_Cd Suplr_Prvdr_St1 Suplr_Prvdr_St2 Suplr_Prvdr_City Suplr_Prvdr_State_Abrvtn Suplr_Prvdr_State_FIPS Suplr_Prvdr_Zip5 Suplr_Prvdr_RUCA Suplr_Prvdr_RUCA_Desc Suplr_Prvdr_Cntry Suplr_Prvdr_Spclty_Desc Suplr_Prvdr_Spclty_Srce Tot_Suplr_HCPCS_Cds Tot_Suplr_Benes Tot_Suplr_Clms Tot_Suplr_Srvcs Suplr_Sbmtd_Chrgs Suplr_Mdcr_Alowd_Amt Suplr_Mdcr_Pymt_Amt Suplr_Mdcr_Stdzd_Pymt_Amt DME_Sprsn_Ind DME_Tot_Suplr_HCPCS_Cds DME_Tot_Suplr_Benes DME_Tot_Suplr_Clms DME_Tot_Suplr_Srvcs DME_Suplr_Sbmtd_Chrgs DME_Suplr_Mdcr_Alowd_Amt DME_Suplr_Mdcr_Pymt_Amt DME_Suplr_Mdcr_Stdzd_Pymt_Amt POS_Sprsn_Ind POS_Tot_Suplr_HCPCS_Cds POS_Tot_Suplr_Benes POS_Tot_Suplr_Clms POS_Tot_Suplr_Srvcs POS_Suplr_Sbmtd_Chrgs POS_Suplr_Mdcr_Alowd_Amt POS_Suplr_Mdcr_Pymt_Amt POS_Suplr_Mdcr_Stdzd_Pymt_Amt Drug_Sprsn_Ind Drug_Tot_Suplr_HCPCS_Cds Drug_Tot_Suplr_Benes Drug_Tot_Suplr_Clms Drug_Tot_Suplr_Srvcs Drug_Suplr_Sbmtd_Chrgs Drug_Suplr_Mdcr_Alowd_Amt Drug_Suplr_Mdcr_Pymt_Amt Drug_Suplr_Mdcr_Stdzd_Pymt_Amt Bene_Avg_Age Bene_Age_LT_65_Cnt Bene_Age_65_74_Cnt Bene_Age_75_84_Cnt Bene_Age_GT_84_Cnt Bene_Feml_Cnt Bene_Male_Cnt Bene_Race_Wht_Cnt Bene_Race_Black_Cnt Bene_Race_Api_Cnt Bene_Race_Hspnc_Cnt Bene_Race_Natind_Cnt Bene_Race_Othr_Cnt Bene_Ndual_Cnt Bene_Dual_Cnt Bene_CC_BH_ADHD_OthCD_V1_Pct Bene_CC_BH_Alcohol_Drug_V1_Pct Bene_CC_BH_Tobacco_V1_Pct Bene_CC_BH_Alz_NonAlzdem_V2_Pct Bene_CC_BH_Anxiety_V1_Pct Bene_CC_BH_Bipolar_V1_Pct Bene_CC_BH_Mood_V2_Pct Bene_CC_BH_Depress_V1_Pct Bene_CC_BH_PD_V1_Pct Bene_CC_BH_PTSD_V1_Pct Bene_CC_BH_Schizo_OthPsy_V1_Pct Bene_CC_PH_Asthma_V2_Pct Bene_CC_PH_Afib_V2_Pct Bene_CC_PH_Cancer6_V2_Pct Bene_CC_PH_CKD_V2_Pct Bene_CC_PH_COPD_V2_Pct Bene_CC_PH_Diabetes_V2_Pct Bene_CC_PH_HF_NonIHD_V2_Pct Bene_CC_PH_Hyperlipidemia_V2_Pct Bene_CC_PH_Hypertension_V2_Pct Bene_CC_PH_IschemicHeart_V2_Pct Bene_CC_PH_Osteoporosis_V2_Pct Bene_CC_PH_Parkinson_V2_Pct Bene_CC_PH_Arthritis_V2_Pct Bene_CC_PH_Stroke_TIA_V2_Pct Bene_Avg_Risk_Scre year\n", + "0 1003000399 Reconstructive Hand To Shoulder Of Indiana, Llc NaN NaN NaN NaN O 13431 Old Meridian Street Suite 225 Carmel IN 18 46032 1.0 Metropolitan area core: primary flow within an... US General Surgery Claim-Specialty 15 235.0 301 340 83033.00 70600.40 54545.85 56320.86 NaN 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.00 NaN 15.0 235.0 301.0 340.0 83033.00 70600.40 54545.85 56320.86 NaN 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 72.862661 20.0 120.0 74.0 21.0 148.0 87.0 222.0 NaN NaN NaN 0.0 NaN 220.0 15.0 NaN 0.051064 0.102128 NaN 0.200000 NaN 0.255319 0.234043 NaN NaN NaN 0.085106 0.085106 0.131915 0.102128 0.165957 0.217021 0.093617 0.676596 0.668085 0.229787 0.144681 0.0 0.646809 0.046809 0.975801 2018\n", + "1 1003000845 James D.Schlenker Mdsc NaN NaN NaN NaN O 6311 W 95th St NaN Oak Lawn IL 17 60453 1.0 Metropolitan area core: primary flow within an... US Plastic and Reconstructive Surgery Claim-Specialty 8 19.0 22 22 4168.00 4034.22 3138.12 4635.72 NaN 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.00 NaN 8.0 19.0 22.0 22.0 4168.00 4034.22 3138.12 4635.72 NaN 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 74.631579 0.0 11.0 NaN NaN NaN NaN 16.0 NaN 0.0 NaN 0.0 NaN NaN NaN 0.0 0.000000 0.000000 NaN NaN 0.0 NaN NaN 0.0 0.0 0.0 0.000000 NaN NaN NaN NaN NaN 0.000000 0.842105 0.736842 NaN NaN NaN 0.736842 NaN 1.065053 2018\n", + "2 1003001934 Yi Rui International Corp NaN NaN NaN NaN O 4307 8th Ave NaN Brooklyn NY 36 11232 1.0 Metropolitan area core: primary flow within an... US Pharmacy Claim-Specialty 5 NaN 37 796 2739.60 549.08 339.39 407.47 NaN 4.0 NaN 35.0 46.0 2448.28 512.46 321.75 389.83 # NaN NaN NaN NaN NaN NaN NaN NaN * NaN NaN NaN NaN NaN NaN NaN NaN 75.285714 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 3.668578 2018\n", + "3 1003002254 Walgreen Co. NaN NaN NaN NaN O 5104 Bobby Hicks Hwy NaN Gray TN 47 37615 1.0 Metropolitan area core: primary flow within an... US Centralized Flu Claim-Specialty 10 56.0 150 3681 31078.36 5276.87 3699.85 3835.41 NaN 6.0 56.0 148.0 390.0 26475.28 3226.01 2111.05 2246.61 NaN 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.00 NaN 4.0 NaN 12.0 3291.0 4603.08 2050.86 1588.8 1588.8 71.240000 NaN 28.0 16.0 NaN 37.0 19.0 56.0 0.0 0.0 0.0 0.0 0.0 45.0 11.0 0.0 NaN NaN NaN 0.214286 NaN 0.285714 0.267857 0.0 0.0 0.0 0.196429 NaN NaN 0.196429 NaN 0.821429 NaN 0.714286 0.839286 0.232143 NaN 0.0 0.446429 NaN 1.171945 2018\n", + "4 1003002767 Thomas J Mcelligott Md Pc NaN NaN NaN NaN O 2415 Wall St Se Suite B Conyers GA 13 30013 1.0 Metropolitan area core: primary flow within an... US Orthopedic Surgery Claim-Specialty 10 38.0 44 45 4920.71 4808.81 3344.82 3420.32 NaN 0.0 0.0 0.0 0.0 0.00 0.00 0.00 0.00 NaN 10.0 38.0 44.0 45.0 4920.71 4808.81 3344.82 3420.32 NaN 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.0 73.727273 NaN 15.0 13.0 NaN 26.0 12.0 26.0 11.0 NaN 0.0 0.0 NaN NaN NaN 0.0 NaN NaN NaN NaN NaN NaN NaN 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.631579 0.631579 NaN NaN 0.0 0.447368 NaN 1.302857 2018" ] }, "metadata": {}, "output_type": "display_data" }, { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Column Names:\n", + "['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org', 'Suplr_Prvdr_First_Name', 'Suplr_Prvdr_MI', 'Suplr_Prvdr_Crdntls', 'Suplr_Prvdr_Gndr', 'Suplr_Prvdr_Ent_Cd', 'Suplr_Prvdr_St1', 'Suplr_Prvdr_St2', 'Suplr_Prvdr_City', 'Suplr_Prvdr_State_Abrvtn', 'Suplr_Prvdr_State_FIPS', 'Suplr_Prvdr_Zip5', 'Suplr_Prvdr_RUCA', 'Suplr_Prvdr_RUCA_Desc', 'Suplr_Prvdr_Cntry', 'Suplr_Prvdr_Spclty_Desc', 'Suplr_Prvdr_Spclty_Srce', 'Tot_Suplr_HCPCS_Cds', 'Tot_Suplr_Benes', 'Tot_Suplr_Clms', 'Tot_Suplr_Srvcs', 'Suplr_Sbmtd_Chrgs', 'Suplr_Mdcr_Alowd_Amt', 'Suplr_Mdcr_Pymt_Amt', 'Suplr_Mdcr_Stdzd_Pymt_Amt', 'DME_Sprsn_Ind', 'DME_Tot_Suplr_HCPCS_Cds', 'DME_Tot_Suplr_Benes', 'DME_Tot_Suplr_Clms', 'DME_Tot_Suplr_Srvcs', 'DME_Suplr_Sbmtd_Chrgs', 'DME_Suplr_Mdcr_Alowd_Amt', 'DME_Suplr_Mdcr_Pymt_Amt', 'DME_Suplr_Mdcr_Stdzd_Pymt_Amt', 'POS_Sprsn_Ind', 'POS_Tot_Suplr_HCPCS_Cds', 'POS_Tot_Suplr_Benes', 'POS_Tot_Suplr_Clms', 'POS_Tot_Suplr_Srvcs', 'POS_Suplr_Sbmtd_Chrgs', 'POS_Suplr_Mdcr_Alowd_Amt', 'POS_Suplr_Mdcr_Pymt_Amt', 'POS_Suplr_Mdcr_Stdzd_Pymt_Amt', 'Drug_Sprsn_Ind', 'Drug_Tot_Suplr_HCPCS_Cds', 'Drug_Tot_Suplr_Benes', 'Drug_Tot_Suplr_Clms', 'Drug_Tot_Suplr_Srvcs', 'Drug_Suplr_Sbmtd_Chrgs', 'Drug_Suplr_Mdcr_Alowd_Amt', 'Drug_Suplr_Mdcr_Pymt_Amt', 'Drug_Suplr_Mdcr_Stdzd_Pymt_Amt', 'Bene_Avg_Age', 'Bene_Age_LT_65_Cnt', 'Bene_Age_65_74_Cnt', 'Bene_Age_75_84_Cnt', 'Bene_Age_GT_84_Cnt', 'Bene_Feml_Cnt', 'Bene_Male_Cnt', 'Bene_Race_Wht_Cnt', 'Bene_Race_Black_Cnt', 'Bene_Race_Api_Cnt', 'Bene_Race_Hspnc_Cnt', 'Bene_Race_Natind_Cnt', 'Bene_Race_Othr_Cnt', 'Bene_Ndual_Cnt', 'Bene_Dual_Cnt', 'Bene_CC_BH_ADHD_OthCD_V1_Pct', 'Bene_CC_BH_Alcohol_Drug_V1_Pct', 'Bene_CC_BH_Tobacco_V1_Pct', 'Bene_CC_BH_Alz_NonAlzdem_V2_Pct', 'Bene_CC_BH_Anxiety_V1_Pct', 'Bene_CC_BH_Bipolar_V1_Pct', 'Bene_CC_BH_Mood_V2_Pct', 'Bene_CC_BH_Depress_V1_Pct', 'Bene_CC_BH_PD_V1_Pct', 'Bene_CC_BH_PTSD_V1_Pct', 'Bene_CC_BH_Schizo_OthPsy_V1_Pct', 'Bene_CC_PH_Asthma_V2_Pct', 'Bene_CC_PH_Afib_V2_Pct', 'Bene_CC_PH_Cancer6_V2_Pct', 'Bene_CC_PH_CKD_V2_Pct', 'Bene_CC_PH_COPD_V2_Pct', 'Bene_CC_PH_Diabetes_V2_Pct', 'Bene_CC_PH_HF_NonIHD_V2_Pct', 'Bene_CC_PH_Hyperlipidemia_V2_Pct', 'Bene_CC_PH_Hypertension_V2_Pct', 'Bene_CC_PH_IschemicHeart_V2_Pct', 'Bene_CC_PH_Osteoporosis_V2_Pct', 'Bene_CC_PH_Parkinson_V2_Pct', 'Bene_CC_PH_Arthritis_V2_Pct', 'Bene_CC_PH_Stroke_TIA_V2_Pct', 'Bene_Avg_Risk_Scre', 'year']\n", + "\n", + "Number of unique suppliers: 86467\n", + "\n", + "Summary of numeric columns:\n" + ] }, { "data": { - "image/png": "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", + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Suplr_NPISuplr_Prvdr_Zip5Suplr_Prvdr_RUCATot_Suplr_HCPCS_CdsTot_Suplr_BenesTot_Suplr_ClmsTot_Suplr_SrvcsSuplr_Sbmtd_ChrgsSuplr_Mdcr_Alowd_AmtSuplr_Mdcr_Pymt_AmtSuplr_Mdcr_Stdzd_Pymt_AmtDME_Tot_Suplr_HCPCS_CdsDME_Tot_Suplr_BenesDME_Tot_Suplr_ClmsDME_Tot_Suplr_SrvcsDME_Suplr_Sbmtd_ChrgsDME_Suplr_Mdcr_Alowd_AmtDME_Suplr_Mdcr_Pymt_AmtDME_Suplr_Mdcr_Stdzd_Pymt_AmtPOS_Tot_Suplr_HCPCS_CdsPOS_Tot_Suplr_BenesPOS_Tot_Suplr_ClmsPOS_Tot_Suplr_SrvcsPOS_Suplr_Sbmtd_ChrgsPOS_Suplr_Mdcr_Alowd_AmtPOS_Suplr_Mdcr_Pymt_AmtPOS_Suplr_Mdcr_Stdzd_Pymt_AmtDrug_Tot_Suplr_HCPCS_CdsDrug_Tot_Suplr_BenesDrug_Tot_Suplr_ClmsDrug_Tot_Suplr_SrvcsDrug_Suplr_Sbmtd_ChrgsDrug_Suplr_Mdcr_Alowd_AmtDrug_Suplr_Mdcr_Pymt_AmtDrug_Suplr_Mdcr_Stdzd_Pymt_AmtBene_Avg_AgeBene_Age_LT_65_CntBene_Age_65_74_CntBene_Age_75_84_CntBene_Age_GT_84_CntBene_Feml_CntBene_Male_CntBene_Race_Wht_CntBene_Race_Black_CntBene_Race_Api_CntBene_Race_Hspnc_CntBene_Race_Natind_CntBene_Race_Othr_CntBene_Ndual_CntBene_Dual_CntBene_CC_BH_ADHD_OthCD_V1_PctBene_CC_BH_Alcohol_Drug_V1_PctBene_CC_BH_Tobacco_V1_PctBene_CC_BH_Alz_NonAlzdem_V2_PctBene_CC_BH_Anxiety_V1_PctBene_CC_BH_Bipolar_V1_PctBene_CC_BH_Mood_V2_PctBene_CC_BH_Depress_V1_PctBene_CC_BH_PD_V1_PctBene_CC_BH_PTSD_V1_PctBene_CC_BH_Schizo_OthPsy_V1_PctBene_CC_PH_Asthma_V2_PctBene_CC_PH_Afib_V2_PctBene_CC_PH_Cancer6_V2_PctBene_CC_PH_CKD_V2_PctBene_CC_PH_COPD_V2_PctBene_CC_PH_Diabetes_V2_PctBene_CC_PH_HF_NonIHD_V2_PctBene_CC_PH_Hyperlipidemia_V2_PctBene_CC_PH_Hypertension_V2_PctBene_CC_PH_IschemicHeart_V2_PctBene_CC_PH_Osteoporosis_V2_PctBene_CC_PH_Parkinson_V2_PctBene_CC_PH_Arthritis_V2_PctBene_CC_PH_Stroke_TIA_V2_PctBene_Avg_Risk_Screyear
count3.526110e+05352611.000000352575.000000352611.000000331904.000000352611.0000003.526110e+053.526110e+053.526110e+053.526110e+053.526110e+05334378.000000312252.000000334378.0000003.343780e+053.343780e+053.343780e+053.343780e+053.343780e+05292989.000000271386.000000292989.0000002.929890e+052.929890e+052.929890e+052.929890e+052.929890e+05279663.000000195592.000000279663.0000002.796630e+052.796630e+052.796630e+052.796630e+052.796630e+05352549.000000112648.000000257838.000000210441.00000090651.000000253106.000000253106.000000297628.000000135898.000000152090.000000153492.000000281658.000000130667.000000179575.000000179575.000000210454.000000110545.000000128558.000000103844.000000184461.000000121693.000000194930.000000182561.000000170846.000000180278.000000151709.000000133002.000000157471.000000131889.000000231256.000000189497.000000289577.000000176141.000000311898.000000315415.000000227186.000000115383.000000163625.000000264873.000000102463.000000352548.000000352611.000000
mean1.499823e+0947761.9744361.93881619.162681180.003712723.6306472.894472e+044.327424e+051.558504e+051.198852e+051.189166e+058.810921145.596758642.8793223.905078e+032.565282e+058.591894e+046.550221e+046.456093e+046.51356948.19331980.8564253.355593e+036.501689e+044.470813e+043.454446e+043.460400e+042.65192715.18476253.6438791.554591e+043.928560e+041.697234e+041.326474e+041.314945e+0472.13598770.16583593.10910383.16049268.880751126.068106103.751768157.49612638.6258596.40213720.3632180.6837736.168627216.67766781.8023390.0013650.0564370.1374170.0758320.2702680.0232780.2954740.2687350.0036250.0034380.0112970.1547840.2056200.1706300.3649540.2960010.7383030.2454000.8034310.8548080.3569550.1325410.0048150.4919850.0853241.7595402019.933791
std2.877778e+0828443.0777922.59361525.0239501318.4647155759.1841301.137952e+066.107114e+062.104732e+061.642990e+061.640688e+0617.9591071210.2072915472.5819131.183744e+054.536824e+061.222667e+069.536508e+059.520616e+0518.036465582.6542431658.0465241.774209e+051.399145e+067.558673e+055.801982e+055.969880e+052.654579508.3613462599.4240591.233497e+062.072189e+069.877956e+057.734391e+057.665664e+054.203287362.180891576.926665551.356612375.786927859.590068663.6520591086.588625283.25932153.643838181.6471299.81206545.9844461322.832122533.4985170.0089510.0763740.1004860.1021990.1029020.0440750.1085440.1022750.0129570.0131200.0447550.0877420.0846320.1359770.1539520.1406410.2511180.1089720.1455350.1469750.1129580.0834790.0171980.1372640.0704670.6554101.417299
min1.003000e+09601.0000001.0000001.00000011.00000011.0000001.100000e+011.960000e+011.662000e+010.000000e+000.000000e+000.0000000.0000000.0000000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.0000000.0000000.0000000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.0000000.0000000.0000000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.1760002018.000000
25%1.255346e+0925504.0000001.0000008.00000028.00000060.0000004.770000e+021.507708e+044.234210e+033.093515e+033.221385e+033.00000015.00000026.0000004.900000e+012.726097e+036.501400e+024.462525e+024.783675e+020.0000000.0000000.0000000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.0000000.0000000.0000000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+0070.28000013.00000018.00000017.00000011.00000024.00000019.00000023.0000000.0000000.0000000.0000000.0000000.00000029.00000015.0000000.0000000.0000000.0747210.0000000.2059500.0000000.2258060.2051280.0000000.0000000.0000000.1104970.1562500.1180560.2771080.2037040.5375000.1808120.7142860.7692310.2826090.0917030.0000000.4000000.0000001.3630452019.000000
50%1.497926e+0944125.0000001.00000012.00000053.000000146.0000003.592000e+033.964752e+041.171618e+048.781310e+038.958790e+035.00000039.000000112.0000002.430000e+021.437400e+042.624245e+031.843840e+031.960370e+030.0000000.0000000.0000000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+003.0000000.00000015.0000001.954000e+033.273830e+033.129500e+022.231000e+022.212200e+0272.55769224.00000030.00000026.00000019.00000039.00000031.00000045.00000011.0000000.0000000.0000000.0000000.00000059.00000025.0000000.0000000.0407100.1358020.0570750.2596150.0000000.2823530.2580650.0000000.0000000.0000000.1470590.2018350.1518990.3513510.2771080.8115940.2368420.8064520.8571430.3490570.1304350.0000000.4747290.0863971.6658592020.000000
75%1.740658e+0974104.0000001.00000018.000000105.000000315.0000001.050350e+049.733840e+043.444066e+042.628954e+042.645689e+046.00000081.000000256.0000005.780000e+023.535016e+046.448807e+034.567375e+034.816860e+034.00000019.00000023.0000005.800000e+018.737850e+036.100790e+034.672270e+034.877140e+035.00000016.00000042.0000006.352000e+031.723049e+045.056030e+033.889745e+033.861795e+0374.58333354.00000055.00000049.00000048.00000074.00000059.00000093.00000025.0000000.00000015.0000000.0000000.000000125.00000054.0000000.0000000.0943950.1936820.1091950.3228350.0424760.3500000.3207550.0000000.0000000.0000000.1935480.2549020.1904400.4324320.3666670.9200000.3028570.8941180.9375000.4210530.1735540.0000000.5648150.1237112.0182672021.000000
max1.993000e+0999901.00000099.000000538.000000237906.000000685662.0000003.290728e+081.836805e+093.991887e+083.116929e+083.117852e+08313.000000237906.000000685662.0000002.316198e+071.830416e+092.082855e+081.622225e+081.622216e+08277.00000094760.000000255217.0000003.156666e+072.291182e+081.343600e+081.055647e+081.126160e+0814.00000098084.000000619016.0000003.286327e+084.578468e+081.979528e+081.550935e+081.530220e+08108.00000046481.00000082335.00000081159.00000027931.000000142147.00000095759.000000170138.00000044508.0000004326.00000028959.000000740.0000002859.000000158443.00000079463.0000000.5652171.6363641.6363641.0684211.7272731.5454551.7272731.6363640.8333330.4444441.6363641.4545452.4615382.3809523.6015621.6363642.5898443.1538463.2617193.7539062.3671881.2727271.1440681.7500000.84615416.3404662022.000000
\n", + "
" + ], "text/plain": [ - "
" + " Suplr_NPI Suplr_Prvdr_Zip5 Suplr_Prvdr_RUCA Tot_Suplr_HCPCS_Cds Tot_Suplr_Benes Tot_Suplr_Clms Tot_Suplr_Srvcs Suplr_Sbmtd_Chrgs Suplr_Mdcr_Alowd_Amt Suplr_Mdcr_Pymt_Amt Suplr_Mdcr_Stdzd_Pymt_Amt DME_Tot_Suplr_HCPCS_Cds DME_Tot_Suplr_Benes DME_Tot_Suplr_Clms DME_Tot_Suplr_Srvcs DME_Suplr_Sbmtd_Chrgs DME_Suplr_Mdcr_Alowd_Amt DME_Suplr_Mdcr_Pymt_Amt DME_Suplr_Mdcr_Stdzd_Pymt_Amt POS_Tot_Suplr_HCPCS_Cds POS_Tot_Suplr_Benes POS_Tot_Suplr_Clms POS_Tot_Suplr_Srvcs POS_Suplr_Sbmtd_Chrgs POS_Suplr_Mdcr_Alowd_Amt POS_Suplr_Mdcr_Pymt_Amt POS_Suplr_Mdcr_Stdzd_Pymt_Amt Drug_Tot_Suplr_HCPCS_Cds Drug_Tot_Suplr_Benes Drug_Tot_Suplr_Clms Drug_Tot_Suplr_Srvcs Drug_Suplr_Sbmtd_Chrgs Drug_Suplr_Mdcr_Alowd_Amt Drug_Suplr_Mdcr_Pymt_Amt Drug_Suplr_Mdcr_Stdzd_Pymt_Amt Bene_Avg_Age Bene_Age_LT_65_Cnt Bene_Age_65_74_Cnt Bene_Age_75_84_Cnt Bene_Age_GT_84_Cnt Bene_Feml_Cnt Bene_Male_Cnt Bene_Race_Wht_Cnt Bene_Race_Black_Cnt Bene_Race_Api_Cnt Bene_Race_Hspnc_Cnt Bene_Race_Natind_Cnt Bene_Race_Othr_Cnt Bene_Ndual_Cnt Bene_Dual_Cnt Bene_CC_BH_ADHD_OthCD_V1_Pct Bene_CC_BH_Alcohol_Drug_V1_Pct Bene_CC_BH_Tobacco_V1_Pct Bene_CC_BH_Alz_NonAlzdem_V2_Pct Bene_CC_BH_Anxiety_V1_Pct Bene_CC_BH_Bipolar_V1_Pct Bene_CC_BH_Mood_V2_Pct Bene_CC_BH_Depress_V1_Pct Bene_CC_BH_PD_V1_Pct Bene_CC_BH_PTSD_V1_Pct Bene_CC_BH_Schizo_OthPsy_V1_Pct Bene_CC_PH_Asthma_V2_Pct Bene_CC_PH_Afib_V2_Pct Bene_CC_PH_Cancer6_V2_Pct Bene_CC_PH_CKD_V2_Pct Bene_CC_PH_COPD_V2_Pct Bene_CC_PH_Diabetes_V2_Pct Bene_CC_PH_HF_NonIHD_V2_Pct Bene_CC_PH_Hyperlipidemia_V2_Pct Bene_CC_PH_Hypertension_V2_Pct Bene_CC_PH_IschemicHeart_V2_Pct Bene_CC_PH_Osteoporosis_V2_Pct Bene_CC_PH_Parkinson_V2_Pct Bene_CC_PH_Arthritis_V2_Pct Bene_CC_PH_Stroke_TIA_V2_Pct Bene_Avg_Risk_Scre year\n", + "count 3.526110e+05 352611.000000 352575.000000 352611.000000 331904.000000 352611.000000 3.526110e+05 3.526110e+05 3.526110e+05 3.526110e+05 3.526110e+05 334378.000000 312252.000000 334378.000000 3.343780e+05 3.343780e+05 3.343780e+05 3.343780e+05 3.343780e+05 292989.000000 271386.000000 292989.000000 2.929890e+05 2.929890e+05 2.929890e+05 2.929890e+05 2.929890e+05 279663.000000 195592.000000 279663.000000 2.796630e+05 2.796630e+05 2.796630e+05 2.796630e+05 2.796630e+05 352549.000000 112648.000000 257838.000000 210441.000000 90651.000000 253106.000000 253106.000000 297628.000000 135898.000000 152090.000000 153492.000000 281658.000000 130667.000000 179575.000000 179575.000000 210454.000000 110545.000000 128558.000000 103844.000000 184461.000000 121693.000000 194930.000000 182561.000000 170846.000000 180278.000000 151709.000000 133002.000000 157471.000000 131889.000000 231256.000000 189497.000000 289577.000000 176141.000000 311898.000000 315415.000000 227186.000000 115383.000000 163625.000000 264873.000000 102463.000000 352548.000000 352611.000000\n", + "mean 1.499823e+09 47761.974436 1.938816 19.162681 180.003712 723.630647 2.894472e+04 4.327424e+05 1.558504e+05 1.198852e+05 1.189166e+05 8.810921 145.596758 642.879322 3.905078e+03 2.565282e+05 8.591894e+04 6.550221e+04 6.456093e+04 6.513569 48.193319 80.856425 3.355593e+03 6.501689e+04 4.470813e+04 3.454446e+04 3.460400e+04 2.651927 15.184762 53.643879 1.554591e+04 3.928560e+04 1.697234e+04 1.326474e+04 1.314945e+04 72.135987 70.165835 93.109103 83.160492 68.880751 126.068106 103.751768 157.496126 38.625859 6.402137 20.363218 0.683773 6.168627 216.677667 81.802339 0.001365 0.056437 0.137417 0.075832 0.270268 0.023278 0.295474 0.268735 0.003625 0.003438 0.011297 0.154784 0.205620 0.170630 0.364954 0.296001 0.738303 0.245400 0.803431 0.854808 0.356955 0.132541 0.004815 0.491985 0.085324 1.759540 2019.933791\n", + "std 2.877778e+08 28443.077792 2.593615 25.023950 1318.464715 5759.184130 1.137952e+06 6.107114e+06 2.104732e+06 1.642990e+06 1.640688e+06 17.959107 1210.207291 5472.581913 1.183744e+05 4.536824e+06 1.222667e+06 9.536508e+05 9.520616e+05 18.036465 582.654243 1658.046524 1.774209e+05 1.399145e+06 7.558673e+05 5.801982e+05 5.969880e+05 2.654579 508.361346 2599.424059 1.233497e+06 2.072189e+06 9.877956e+05 7.734391e+05 7.665664e+05 4.203287 362.180891 576.926665 551.356612 375.786927 859.590068 663.652059 1086.588625 283.259321 53.643838 181.647129 9.812065 45.984446 1322.832122 533.498517 0.008951 0.076374 0.100486 0.102199 0.102902 0.044075 0.108544 0.102275 0.012957 0.013120 0.044755 0.087742 0.084632 0.135977 0.153952 0.140641 0.251118 0.108972 0.145535 0.146975 0.112958 0.083479 0.017198 0.137264 0.070467 0.655410 1.417299\n", + "min 1.003000e+09 601.000000 1.000000 1.000000 11.000000 11.000000 1.100000e+01 1.960000e+01 1.662000e+01 0.000000e+00 0.000000e+00 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.176000 2018.000000\n", + "25% 1.255346e+09 25504.000000 1.000000 8.000000 28.000000 60.000000 4.770000e+02 1.507708e+04 4.234210e+03 3.093515e+03 3.221385e+03 3.000000 15.000000 26.000000 4.900000e+01 2.726097e+03 6.501400e+02 4.462525e+02 4.783675e+02 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 70.280000 13.000000 18.000000 17.000000 11.000000 24.000000 19.000000 23.000000 0.000000 0.000000 0.000000 0.000000 0.000000 29.000000 15.000000 0.000000 0.000000 0.074721 0.000000 0.205950 0.000000 0.225806 0.205128 0.000000 0.000000 0.000000 0.110497 0.156250 0.118056 0.277108 0.203704 0.537500 0.180812 0.714286 0.769231 0.282609 0.091703 0.000000 0.400000 0.000000 1.363045 2019.000000\n", + "50% 1.497926e+09 44125.000000 1.000000 12.000000 53.000000 146.000000 3.592000e+03 3.964752e+04 1.171618e+04 8.781310e+03 8.958790e+03 5.000000 39.000000 112.000000 2.430000e+02 1.437400e+04 2.624245e+03 1.843840e+03 1.960370e+03 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 3.000000 0.000000 15.000000 1.954000e+03 3.273830e+03 3.129500e+02 2.231000e+02 2.212200e+02 72.557692 24.000000 30.000000 26.000000 19.000000 39.000000 31.000000 45.000000 11.000000 0.000000 0.000000 0.000000 0.000000 59.000000 25.000000 0.000000 0.040710 0.135802 0.057075 0.259615 0.000000 0.282353 0.258065 0.000000 0.000000 0.000000 0.147059 0.201835 0.151899 0.351351 0.277108 0.811594 0.236842 0.806452 0.857143 0.349057 0.130435 0.000000 0.474729 0.086397 1.665859 2020.000000\n", + "75% 1.740658e+09 74104.000000 1.000000 18.000000 105.000000 315.000000 1.050350e+04 9.733840e+04 3.444066e+04 2.628954e+04 2.645689e+04 6.000000 81.000000 256.000000 5.780000e+02 3.535016e+04 6.448807e+03 4.567375e+03 4.816860e+03 4.000000 19.000000 23.000000 5.800000e+01 8.737850e+03 6.100790e+03 4.672270e+03 4.877140e+03 5.000000 16.000000 42.000000 6.352000e+03 1.723049e+04 5.056030e+03 3.889745e+03 3.861795e+03 74.583333 54.000000 55.000000 49.000000 48.000000 74.000000 59.000000 93.000000 25.000000 0.000000 15.000000 0.000000 0.000000 125.000000 54.000000 0.000000 0.094395 0.193682 0.109195 0.322835 0.042476 0.350000 0.320755 0.000000 0.000000 0.000000 0.193548 0.254902 0.190440 0.432432 0.366667 0.920000 0.302857 0.894118 0.937500 0.421053 0.173554 0.000000 0.564815 0.123711 2.018267 2021.000000\n", + "max 1.993000e+09 99901.000000 99.000000 538.000000 237906.000000 685662.000000 3.290728e+08 1.836805e+09 3.991887e+08 3.116929e+08 3.117852e+08 313.000000 237906.000000 685662.000000 2.316198e+07 1.830416e+09 2.082855e+08 1.622225e+08 1.622216e+08 277.000000 94760.000000 255217.000000 3.156666e+07 2.291182e+08 1.343600e+08 1.055647e+08 1.126160e+08 14.000000 98084.000000 619016.000000 3.286327e+08 4.578468e+08 1.979528e+08 1.550935e+08 1.530220e+08 108.000000 46481.000000 82335.000000 81159.000000 27931.000000 142147.000000 95759.000000 170138.000000 44508.000000 4326.000000 28959.000000 740.000000 2859.000000 158443.000000 79463.000000 0.565217 1.636364 1.636364 1.068421 1.727273 1.545455 1.727273 1.636364 0.833333 0.444444 1.636364 1.454545 2.461538 2.380952 3.601562 1.636364 2.589844 3.153846 3.261719 3.753906 2.367188 1.272727 1.144068 1.750000 0.846154 16.340466 2022.000000" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "if not combined_df.empty:\n", + " print(\"\\nFirst few rows:\")\n", + " display(combined_df.head())\n", + "\n", + " print(\"\\nColumn Names:\")\n", + " print(combined_df.columns.tolist())\n", + "\n", + " print(f\"\\nNumber of unique suppliers: {combined_df['Suplr_NPI'].nunique()}\")\n", + " print(\"\\nSummary of numeric columns:\")\n", + " display(combined_df.describe(include=[np.number]))" + ] + }, + { + "cell_type": "markdown", + "id": "f0106077", + "metadata": {}, + "source": [ + "## 3. Mapping Columns to Data Dictionary\n", + "We've got a `DATA_DICTIONARY` that provides definitions for each column. Let's map them to the DataFrame's columns." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "744715a8", + "metadata": { + "tags": [] + }, + "outputs": [ { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Dictionary Mapping:\n", + "\n", + "- Suplr_NPI: Supplier NPI - NPI for the Supplier on the DMEPOS claim\n", + "- Suplr_Prvdr_Last_Name_Org: Supplier Last Name/Organization Name - When registered as individual, the Supplier's last name. When registered as organization, this is the organization name\n", + "- Suplr_Prvdr_First_Name: Supplier First Name - When registered as individual, the Supplier's first name\n", + "- Suplr_Prvdr_MI: Supplier Middle Initial - When registered as individual, the Supplier's middle initial\n", + "- Suplr_Prvdr_Crdntls: Supplier Credentials - When registered as individual, these are the Supplier's credentials\n", + "- Suplr_Prvdr_Gndr: Supplier Gender - When registered as individual, this is the Supplier's gender\n", + "- Suplr_Prvdr_Ent_Cd: Supplier Entity Code - 'I' identifies Suppliers registered as individuals, 'O' identifies Suppliers registered as organizations\n", + "- Suplr_Prvdr_St1: Supplier Street 1 - First line of the Supplier's street address\n", + "- Suplr_Prvdr_St2: Supplier Street 2 - Second line of the Supplier's street address\n", + "- Suplr_Prvdr_City: Supplier City - The city where the Supplier is located\n", + "- Suplr_Prvdr_State_Abrvtn: Supplier State - State postal abbreviation where the Supplier is located\n", + "- Suplr_Prvdr_State_FIPS: Supplier State FIPS Code - FIPS code for Supplier's state\n", + "- Suplr_Prvdr_Zip5: Supplier ZIP - The Supplier's ZIP code\n", + "- Suplr_Prvdr_RUCA: Supplier RUCA - Rural-Urban Commuting Area Code for the Supplier ZIP code\n", + "- Suplr_Prvdr_RUCA_Desc: Supplier RUCA Description - Description of Rural-Urban Commuting Area (RUCA) Code\n", + "- Suplr_Prvdr_Cntry: Supplier Country - Country where the Supplier is located\n", + "- Suplr_Prvdr_Spclty_Desc: Supplier Provider Specialty Description - Derived from Medicare provider/supplier specialty code\n", + "- Suplr_Prvdr_Spclty_Srce: Supplier Provider Specialty Source - Source of the Supplier Specialty (claims-specialty or NPPES-specialty)\n", + "- Tot_Suplr_HCPCS_Cds: Number of Supplier HCPCS - Total unique DMEPOS product/service HCPCS codes\n", + "- Tot_Suplr_Benes: Number of Supplier Beneficiaries - Total unique beneficiaries (<11 are suppressed)\n", + "- Tot_Suplr_Clms: Number of Supplier Claims - Total DMEPOS claims submitted\n", + "- Tot_Suplr_Srvcs: Number of Supplier Services - Total DMEPOS products/services rendered\n", + "- Suplr_Sbmtd_Chrgs: Supplier Submitted Charges - Total charges submitted for DMEPOS products/services\n", + "- Suplr_Mdcr_Alowd_Amt: Supplier Medicare Allowed Amount - Total Medicare allowed amount\n", + "- Suplr_Mdcr_Pymt_Amt: Supplier Medicare Payment Amount - Amount Medicare paid after deductible/coinsurance\n", + "- Suplr_Mdcr_Stdzd_Pymt_Amt: Supplier Medicare Standard Payment Amount - Standardized Medicare payments\n", + "- DME_Sprsn_Ind: Durable Medical Equipment Suppression Indicator - '*'=suppressed (1-10 claims), '#'=counter-suppressed\n", + "- DME_Tot_Suplr_HCPCS_Cds: Number of DME HCPCS - Total unique DME HCPCS codes\n", + "- DME_Tot_Suplr_Benes: Number of DME Beneficiaries - Total unique beneficiaries with DME claims (<11 are suppressed)\n", + "- DME_Tot_Suplr_Clms: Number of DME Claims - Total DME claims submitted\n", + "- DME_Tot_Suplr_Srvcs: Number of DME Services - Total DME products/services rendered\n", + "- DME_Suplr_Sbmtd_Chrgs: DME Submitted Charges - Total charges submitted for DME products/services\n", + "- DME_Suplr_Mdcr_Alowd_Amt: DME Medicare Allowed Amount - Total Medicare allowed amount for DME\n", + "- DME_Suplr_Mdcr_Pymt_Amt: DME Medicare Payment Amount - Amount Medicare paid for DME after deductible/coinsurance\n", + "- DME_Suplr_Mdcr_Stdzd_Pymt_Amt: DME Medicare Standard Payment Amount - Standardized Medicare payments for DME\n", + "- POS_Sprsn_Ind: Prosthetic and Orthotic Suppression Indicator - '*'=suppressed (1-10 claims), '#'=counter-suppressed\n", + "- POS_Tot_Suplr_HCPCS_Cds: Number of Prosthetic/Orthotic HCPCS - Total unique prosthetic/orthotic HCPCS codes\n", + "- POS_Tot_Suplr_Benes: Number of Prosthetic/Orthotic Beneficiaries - Total unique beneficiaries\n", + "- POS_Tot_Suplr_Clms: Number of Prosthetic/Orthotic Claims - Total prosthetic/orthotic claims submitted\n", + "- POS_Tot_Suplr_Srvcs: Number of Prosthetic/Orthotic Services - Total prosthetic/orthotic products/services\n", + "- POS_Suplr_Sbmtd_Chrgs: Prosthetic/Orthotic Submitted Charges - Total charges submitted for prosthetic/orthotic\n", + "- POS_Suplr_Mdcr_Alowd_Amt: Prosthetic/Orthotic Medicare Allowed Amount - Total Medicare allowed amount\n", + "- POS_Suplr_Mdcr_Pymt_Amt: Prosthetic/Orthotic Medicare Payment Amount - Amount Medicare paid after deductible/coinsurance\n", + "- POS_Suplr_Mdcr_Stdzd_Pymt_Amt: Prosthetic/Orthotic Medicare Standard Payment Amount - Standardized Medicare payments\n", + "- Drug_Sprsn_Ind: Drug and Nutritional Suppression Indicator - '*'=suppressed (1-10 claims), '#'=counter-suppressed\n", + "- Drug_Tot_Suplr_HCPCS_Cds: Number of Drug/Nutritional HCPCS - Total unique drug/nutritional HCPCS codes\n", + "- Drug_Tot_Suplr_Benes: Number of Drug/Nutritional Beneficiaries - Total unique beneficiaries\n", + "- Drug_Tot_Suplr_Clms: Number of Drug/Nutritional Claims - Total drug/nutritional claims submitted\n", + "- Drug_Tot_Suplr_Srvcs: Number of Drug/Nutritional Services - Total drug/nutritional products/services\n", + "- Drug_Suplr_Sbmtd_Chrgs: Drug/Nutritional Submitted Charges - Total charges submitted for drug/nutritional\n", + "- Drug_Suplr_Mdcr_Alowd_Amt: Drug/Nutritional Medicare Allowed Amount - Total Medicare allowed amount\n", + "- Drug_Suplr_Mdcr_Pymt_Amt: Drug/Nutritional Medicare Payment Amount - Amount Medicare paid after deductible/coinsurance\n", + "- Drug_Suplr_Mdcr_Stdzd_Pymt_Amt: Drug/Nutritional Medicare Standard Payment Amount - Standardized Medicare payments\n", + "- Bene_Avg_Age: Average Age of Beneficiaries - Average age at end of calendar year or time of death\n", + "- Bene_Age_LT_65_Cnt: Number of Beneficiaries <65 - Count of beneficiaries under 65 years old\n", + "- Bene_Age_65_74_Cnt: Number of Beneficiaries 65-74 - Count of beneficiaries between 65-74 years old\n", + "- Bene_Age_75_84_Cnt: Number of Beneficiaries 75-84 - Count of beneficiaries between 75-84 years old\n", + "- Bene_Age_GT_84_Cnt: Number of Beneficiaries >84 - Count of beneficiaries over 84 years old\n", + "- Bene_Feml_Cnt: Number of Female Beneficiaries - Count of female beneficiaries\n", + "- Bene_Male_Cnt: Number of Male Beneficiaries - Count of male beneficiaries\n", + "- Bene_Race_Wht_Cnt: Number of White Beneficiaries - Count of non-Hispanic white beneficiaries\n", + "- Bene_Race_Black_Cnt: Number of Black Beneficiaries - Count of non-Hispanic Black/African American beneficiaries\n", + "- Bene_Race_Api_Cnt: Number of Asian/PI Beneficiaries - Count of Asian Pacific Islander beneficiaries\n", + "- Bene_Race_Hspnc_Cnt: Number of Hispanic Beneficiaries - Count of Hispanic beneficiaries\n", + "- Bene_Race_Natind_Cnt: Number of Native American/Alaska Native Beneficiaries - Count of American Indian/Alaska Native beneficiaries\n", + "- Bene_Race_Othr_Cnt: Number of Other Race Beneficiaries - Count of beneficiaries with race not elsewhere classified\n", + "- Bene_Ndual_Cnt: Number of Medicare & Medicaid Beneficiaries - Count of dual-eligible beneficiaries\n", + "- Bene_Dual_Cnt: Number of Medicare-Only Beneficiaries - Count of Medicare-only beneficiaries\n", + "- Bene_CC_BH_ADHD_OthCD_V1_Pct: Percent with ADHD and Other Conduct Disorders\n", + "- Bene_CC_BH_Alcohol_Drug_V1_Pct: Percent with Alcohol and Drug Use Disorders\n", + "- Bene_CC_BH_Tobacco_V1_Pct: Percent with Tobacco Use Disorders\n", + "- Bene_CC_BH_Alz_NonAlzdem_V2_Pct: Percent with Alzheimer's and Non-Alzheimer's Dementia\n", + "- Bene_CC_BH_Anxiety_V1_Pct: Percent with Anxiety Disorders\n", + "- Bene_CC_BH_Bipolar_V1_Pct: Percent with Bipolar Disorder\n", + "- Bene_CC_BH_Mood_V2_Pct: Percent with Depression, Bipolar or Other Mood Disorders\n", + "- Bene_CC_BH_Depress_V1_Pct: Percent with Major Depressive Affective Disorder\n", + "- Bene_CC_BH_PD_V1_Pct: Percent with Personality Disorders\n", + "- Bene_CC_BH_PTSD_V1_Pct: Percent with Post-Traumatic Stress Disorder\n", + "- Bene_CC_BH_Schizo_OthPsy_V1_Pct: Percent with Schizophrenia and Other Psychotic Disorders\n", + "- Bene_CC_PH_Asthma_V2_Pct: Percent with Asthma\n", + "- Bene_CC_PH_Afib_V2_Pct: Percent with Atrial Fibrillation and Flutter\n", + "- Bene_CC_PH_Cancer6_V2_Pct: Percent with Cancer (combined 6 cancer indicators)\n", + "- Bene_CC_PH_CKD_V2_Pct: Percent with Chronic Kidney Disease\n", + "- Bene_CC_PH_COPD_V2_Pct: Percent with Chronic Obstructive Pulmonary Disease\n", + "- Bene_CC_PH_Diabetes_V2_Pct: Percent with Diabetes\n", + "- Bene_CC_PH_HF_NonIHD_V2_Pct: Percent with Heart Failure and Non-Ischemic Heart Disease\n", + "- Bene_CC_PH_Hyperlipidemia_V2_Pct: Percent with Hyperlipidemia\n", + "- Bene_CC_PH_Hypertension_V2_Pct: Percent with Hypertension\n", + "- Bene_CC_PH_IschemicHeart_V2_Pct: Percent with Ischemic Heart Disease\n", + "- Bene_CC_PH_Osteoporosis_V2_Pct: Percent with Osteoporosis\n", + "- Bene_CC_PH_Parkinson_V2_Pct: Percent with Parkinson's Disease\n", + "- Bene_CC_PH_Arthritis_V2_Pct: Percent with Rheumatoid Arthritis/Osteoarthritis\n", + "- Bene_CC_PH_Stroke_TIA_V2_Pct: Percent with Stroke/Transient Ischemic Attack\n", + "- Bene_Avg_Risk_Scre: Average HCC Risk Score of Beneficiaries\n", + "- year: Year of the data\n" + ] + } + ], + "source": [ + "if not combined_df.empty:\n", + " column_info = {}\n", + " for column in combined_df.columns:\n", + " if column in DATA_DICTIONARY:\n", + " column_info[column] = DATA_DICTIONARY[column]\n", + " else:\n", + " column_info[column] = \"Description not available\"\n", + " \n", + " # Optionally store in DataFrame attributes (just for reference, not required)\n", + " combined_df.attrs['column_descriptions'] = column_info\n", + "\n", + " # Display an overview\n", + " print(\"Data Dictionary Mapping:\\n\")\n", + " for col in combined_df.columns:\n", + " desc = column_info[col]\n", + " print(f\"- {col}: {desc}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a009a7cc", + "metadata": {}, + "source": [ + "## 4. Helper: Format Dollar Amounts\n", + "A small function to display large numbers with K/M suffixes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0462c05c", + "metadata": {}, + "outputs": [], + "source": [ + "def format_dollar_amount(amount):\n", + " \"\"\"Return a string formatted with $ and K/M if needed.\"\"\"\n", + " if amount >= 1_000_000:\n", + " return f\"${amount/1_000_000:.1f}M\"\n", + " elif amount >= 1_000:\n", + " return f\"${amount/1_000:.1f}K\"\n", + " else:\n", + " return f\"${amount:,.0f}\"" + ] + }, + { + "cell_type": "markdown", + "id": "29348be9", + "metadata": {}, + "source": [ + "# 5. Year-over-Year Growth Analysis\n", + "We'll look at *Medicare Payment Amount* by Supplier (NPI) across years, and compute YOY growth.\n", + "- Filter for suppliers that appear in all relevant years (2018–2022).\n", + "- Only consider suppliers with a meaningful (>= 100k) total in 2022 to focus on large-volume providers.\n", + "- Identify top 10 by average growth rate." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0cdba951", + "metadata": { + "tags": [] + }, + "outputs": [ { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Top 10 Suppliers by Average Year-over-Year Growth (2018–2022), \n", + "\n", + " Filtered to those with >= $100K in 2022 payments:\n" + ] }, { "data": { - "image/png": "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", + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Suplr_NPISuplr_Prvdr_Last_Name_Orggrowth_2019growth_2020growth_2021growth_2022avg_growthSuplr_Sbmtd_ChrgsSuplr_Mdcr_Pymt_AmtTot_Suplr_BenesTot_Suplr_Clms
5001063967768P-Cares Medical Supplies, Llc216.762990-18.279120-1.43859659704.30803114975.3383262.996986e+0715894839.273029.6021002
59581891275590Lincare Inc43427.67715540.0425239.8629991.39727310869.7449871.351100e+0819498239.268006.60250598
30781457837080Respiratory Services Of Western New York, Inc.24521.584413489.45845159.86909746.6212086279.3832921.742520e+06636034.24442.008743
55381821424789Vohra Post Acute Care Physicians Of Texas, Pllc24557.093269105.80191524.5898301.3040096172.1972561.954372e+077851147.071268.2521386
33891508938127Aahi St Joseph Mercy Hospital Inc-73.766346-96.93252923208.805119139.6463935794.4381591.052284e+06477838.88NaN421
11971174553804Care One Medical Equipment And Supplies, Inc.19205.12723550.732207-6.81621034.9413584820.9961483.241600e+061038009.02529.2511495
33651508826199The Home Health Store Of Tomball, Inc.70.63894423.05368553.07501017908.4240454513.7979212.914811e+0715253804.775578.4056914
50221750391751Amerihealth Medical Group, Inc.18039.357850-46.8047416.478163-16.9423264495.5222362.947875e+061106496.20873.6019592
39231598044208Scooter Chair Repair Georgia, Llc16495.68470646.102762-17.790577-18.3488404126.4120131.078492e+074825597.72219.603544
28651437108214Christian Home Health Services, Inc16471.811385-19.95177110.8562424.7422564116.8645281.948006e+06573413.07391.757344
\n", + "
" + ], "text/plain": [ - "
" + " Suplr_NPI Suplr_Prvdr_Last_Name_Org growth_2019 growth_2020 growth_2021 growth_2022 avg_growth Suplr_Sbmtd_Chrgs Suplr_Mdcr_Pymt_Amt Tot_Suplr_Benes Tot_Suplr_Clms\n", + "500 1063967768 P-Cares Medical Supplies, Llc 216.762990 -18.279120 -1.438596 59704.308031 14975.338326 2.996986e+07 15894839.27 3029.60 21002\n", + "5958 1891275590 Lincare Inc 43427.677155 40.042523 9.862999 1.397273 10869.744987 1.351100e+08 19498239.26 8006.60 250598\n", + "3078 1457837080 Respiratory Services Of Western New York, Inc. 24521.584413 489.458451 59.869097 46.621208 6279.383292 1.742520e+06 636034.24 442.00 8743\n", + "5538 1821424789 Vohra Post Acute Care Physicians Of Texas, Pllc 24557.093269 105.801915 24.589830 1.304009 6172.197256 1.954372e+07 7851147.07 1268.25 21386\n", + "3389 1508938127 Aahi St Joseph Mercy Hospital Inc -73.766346 -96.932529 23208.805119 139.646393 5794.438159 1.052284e+06 477838.88 NaN 421\n", + "1197 1174553804 Care One Medical Equipment And Supplies, Inc. 19205.127235 50.732207 -6.816210 34.941358 4820.996148 3.241600e+06 1038009.02 529.25 11495\n", + "3365 1508826199 The Home Health Store Of Tomball, Inc. 70.638944 23.053685 53.075010 17908.424045 4513.797921 2.914811e+07 15253804.77 5578.40 56914\n", + "5022 1750391751 Amerihealth Medical Group, Inc. 18039.357850 -46.804741 6.478163 -16.942326 4495.522236 2.947875e+06 1106496.20 873.60 19592\n", + "3923 1598044208 Scooter Chair Repair Georgia, Llc 16495.684706 46.102762 -17.790577 -18.348840 4126.412013 1.078492e+07 4825597.72 219.60 3544\n", + "2865 1437108214 Christian Home Health Services, Inc 16471.811385 -19.951771 10.856242 4.742256 4116.864528 1.948006e+06 573413.07 391.75 7344" ] }, "metadata": {}, @@ -937,1280 +1938,855 @@ } ], "source": [ - "#!/usr/bin/env python3\n", - "# -*- coding: utf-8 -*-\n", - "\n", - "\"\"\"\n", - "DME Fraud Detection Script\n", - "This script analyzes Medicare DME supplier data to identify potential fraud indicators,\n", - "with a focus on suspicious growth patterns similar to credit card fraud detection techniques.\n", - "\"\"\"\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from collections import defaultdict\n", - "import os\n", - "import sys\n", - "\n", - "# Set visualization style\n", - "plt.style.use('seaborn-v0_8-whitegrid')\n", - "sns.set_palette('viridis')\n", - "\n", - "\n", - "def import_dme_data(file_path):\n", - " \"\"\"\n", - " Import and preprocess DME data from a CSV file.\n", - "\n", - " Parameters:\n", - " -----------\n", - " file_path : str\n", - " Path to the CSV file containing DME data\n", - "\n", - " Returns:\n", - " --------\n", - " df : DataFrame\n", - " Processed DataFrame containing DME data\n", - " \"\"\"\n", - " print(f\"Importing data from {file_path}...\")\n", - "\n", - " try:\n", - " # Import data with appropriate dtypes to handle monetary values correctly\n", - " df = pd.read_csv(file_path, low_memory=False)\n", - "\n", - " # Convert monetary columns to numeric\n", - " money_columns = [\n", - " col for col in df.columns if 'Pymt' in col or 'Amt' in col]\n", - " for col in money_columns:\n", - " if col in df.columns:\n", - " df[col] = pd.to_numeric(df[col], errors='coerce')\n", - "\n", - " print(f\"Successfully imported data with shape: {df.shape}\")\n", - " return df\n", - "\n", - " except Exception as e:\n", - " print(f\"Error importing data: {str(e)}\")\n", - " return None\n", - "\n", - "\n", - "def get_column_mapping(df):\n", - " \"\"\"\n", - " Get a mapping of expected column names to actual column names in the DataFrame.\n", - " This helps handle variations in column names across different datasets.\n", - "\n", - " Parameters:\n", - " -----------\n", - " df : DataFrame\n", - " DataFrame to inspect for column names\n", - "\n", - " Returns:\n", - " --------\n", - " column_map : dict\n", - " Dictionary mapping expected column names to actual column names\n", - " \"\"\"\n", - " column_map = {}\n", - "\n", - " # Map for supplier organization name\n", - " if 'Suplr_Prvdr_Org_Name' in df.columns:\n", - " column_map['supplier_name'] = 'Suplr_Prvdr_Org_Name'\n", - " elif 'Suplr_Name' in df.columns:\n", - " column_map['supplier_name'] = 'Suplr_Name'\n", - " elif 'Supplier_Name' in df.columns:\n", - " column_map['supplier_name'] = 'Supplier_Name'\n", - " elif 'Provider_Org_Name' in df.columns:\n", - " column_map['supplier_name'] = 'Provider_Org_Name'\n", - " else:\n", - " # If no suitable column exists, create a placeholder\n", - " print(\"Warning: No supplier name column found. Using placeholder names.\")\n", - " column_map['supplier_name'] = None\n", - "\n", - " # Map for supplier state\n", - " if 'Suplr_Prvdr_State_Abrvtn' in df.columns:\n", - " column_map['supplier_state'] = 'Suplr_Prvdr_State_Abrvtn'\n", - " elif 'Suplr_State' in df.columns:\n", - " column_map['supplier_state'] = 'Suplr_State'\n", - " elif 'State' in df.columns:\n", - " column_map['supplier_state'] = 'State'\n", - " else:\n", - " print(\"Warning: No supplier state column found. Using placeholder.\")\n", - " column_map['supplier_state'] = None\n", - "\n", - " # Map for supplier NPI\n", - " if 'Suplr_NPI' in df.columns:\n", - " column_map['supplier_npi'] = 'Suplr_NPI'\n", - " elif 'NPI' in df.columns:\n", - " column_map['supplier_npi'] = 'NPI'\n", - " elif 'Provider_NPI' in df.columns:\n", - " column_map['supplier_npi'] = 'Provider_NPI'\n", - " else:\n", - " print(\"Warning: No NPI column found. Using index as placeholder.\")\n", - " column_map['supplier_npi'] = None\n", - "\n", - " # Print available columns if key columns are missing\n", - " if None in column_map.values():\n", - " print(\"\\nAvailable columns in the dataset:\")\n", - " for i, col in enumerate(sorted(df.columns)):\n", - " print(f\" {i+1}. {col}\")\n", - " print(\n", - " \"\\nPlease adjust the script to use the correct column names for your dataset.\")\n", - "\n", - " return column_map\n", - "\n", - "\n", - "def detect_high_growth_suppliers(df_by_year, metric='DME_Suplr_Mdcr_Pymt_Amt', top_n=50):\n", - " \"\"\"\n", - " Identify suppliers with abnormally high growth rates year over year.\n", - "\n", - " Parameters:\n", - " -----------\n", - " df_by_year : dict\n", - " Dictionary containing DataFrames by year\n", - " metric : str\n", - " The metric to analyze for growth (default: Medicare payments)\n", - " top_n : int\n", - " Number of top growth suppliers to identify\n", - "\n", - " Returns:\n", - " --------\n", - " growth_df : DataFrame\n", - " DataFrame containing suppliers with their growth rates\n", - " \"\"\"\n", - " print(f\"Identifying suppliers with highest year-over-year growth rates...\")\n", - "\n", - " # Check if metric exists in all dataframes\n", - " for year, df in df_by_year.items():\n", - " if metric not in df.columns:\n", - " available_metrics = [\n", - " col for col in df.columns if 'Pymt' in col or 'Amt' in col]\n", - " if not available_metrics:\n", - " print(\n", - " f\"Error: No payment metrics found in data for year {year}.\")\n", - " return pd.DataFrame()\n", - "\n", - " # Use the first available payment metric\n", - " metric = available_metrics[0]\n", - " print(f\"Using alternate metric: {metric}\")\n", - " break\n", - "\n", - " # Get all available years\n", - " years = sorted(df_by_year.keys())\n", - "\n", - " if len(years) < 2:\n", - " print(\"Error: Need at least two years of data to calculate growth rates\")\n", - " return pd.DataFrame()\n", - "\n", - " # Get column mappings from the most recent year's data\n", - " recent_year = max(years)\n", - " column_map = get_column_mapping(df_by_year[recent_year])\n", - "\n", - " # Create a dictionary to store supplier data across years\n", - " supplier_data = {}\n", - " supplier_info = {}\n", - "\n", - " # Process each supplier's data for each year\n", - " for year in years:\n", - " df = df_by_year[year]\n", - "\n", - " # Get NPI column name\n", - " npi_col = column_map['supplier_npi']\n", - " if npi_col is None:\n", - " # Create a synthetic NPI using index\n", - " df['synthetic_npi'] = 'NPI' + df.index.astype(str)\n", - " npi_col = 'synthetic_npi'\n", - "\n", - " # Group by supplier NPI and sum the metric\n", - " supplier_metric = df.groupby(npi_col)[metric].sum().reset_index()\n", - "\n", - " # Store in dictionary\n", - " for _, row in supplier_metric.iterrows():\n", - " npi = row[npi_col]\n", - " value = row[metric]\n", - "\n", - " if npi not in supplier_data:\n", - " supplier_data[npi] = {}\n", - "\n", - " # Store supplier info for later use\n", - " supplier_row = df[df[npi_col] == npi].iloc[0] if len(\n", - " df[df[npi_col] == npi]) > 0 else None\n", - " if supplier_row is not None:\n", - " supplier_info[npi] = {\n", - " 'name': supplier_row[column_map['supplier_name']] if column_map['supplier_name'] is not None else f\"Supplier {npi}\",\n", - " 'state': supplier_row[column_map['supplier_state']] if column_map['supplier_state'] is not None else 'Unknown'\n", - " }\n", - " else:\n", - " supplier_info[npi] = {\n", - " 'name': f\"Supplier {npi}\",\n", - " 'state': 'Unknown'\n", - " }\n", - "\n", - " supplier_data[npi][year] = value\n", - "\n", - " # Calculate year-over-year growth rates\n", - " growth_data = []\n", - "\n", - " for npi, year_values in supplier_data.items():\n", - " # Need at least two years of data for this supplier\n", - " if len(year_values) < 2:\n", - " continue\n", - "\n", - " for i in range(len(years) - 1):\n", - " current_year = years[i]\n", - " next_year = years[i + 1]\n", - "\n", - " # Skip if supplier doesn't have data for both years\n", - " if current_year not in year_values or next_year not in year_values:\n", - " continue\n", - "\n", - " current_value = year_values[current_year]\n", - " next_value = year_values[next_year]\n", - "\n", - " # Skip if current value is zero (would result in infinity growth)\n", - " if current_value == 0:\n", - " continue\n", - "\n", - " # Calculate growth rate\n", - " growth_rate = ((next_value - current_value) / current_value) * 100\n", - "\n", - " growth_data.append({\n", - " 'Supplier NPI': npi,\n", - " 'Supplier Name': supplier_info[npi]['name'],\n", - " 'Supplier State': supplier_info[npi]['state'],\n", - " 'Year Period': f\"{current_year}-{next_year}\",\n", - " 'Start Year Value': current_value,\n", - " 'End Year Value': next_value,\n", - " 'Growth Rate (%)': growth_rate,\n", - " 'Absolute Growth': next_value - current_value\n", - " })\n", - "\n", - " # Convert to DataFrame\n", - " growth_df = pd.DataFrame(growth_data)\n", - "\n", - " # Sort by growth rate (descending)\n", - " growth_df = growth_df.sort_values('Growth Rate (%)', ascending=False)\n", - "\n", - " return growth_df.head(top_n)\n", - "\n", - "\n", - "def detect_outlier_claim_amounts(df, year, metric='DME_Avg_Sbmtd_Chrg', threshold=2.0):\n", - " \"\"\"\n", - " Identify suppliers with abnormally high average claim amounts.\n", - "\n", - " Parameters:\n", - " -----------\n", - " df : DataFrame\n", - " DataFrame for a specific year\n", - " year : int\n", - " The year being analyzed\n", - " metric : str\n", - " The metric to analyze for outliers (default: average submitted charge)\n", - " threshold : float\n", - " Z-score threshold for flagging outliers (default: 2.0)\n", - "\n", - " Returns:\n", - " --------\n", - " outlier_df : DataFrame\n", - " DataFrame containing suppliers with outlier claim amounts\n", - " \"\"\"\n", - " print(\n", - " f\"Identifying suppliers with abnormally high average claim amounts in {year}...\")\n", - "\n", - " # Get column mappings\n", - " column_map = get_column_mapping(df)\n", - "\n", - " # Verify the metric exists\n", - " if metric not in df.columns:\n", - " available_metrics = [col for col in df.columns if 'Avg' in col and (\n", - " 'Chrg' in col or 'Amt' in col)]\n", - " if not available_metrics:\n", - " print(\n", - " f\"Error: No average charge metrics found in data for year {year}.\")\n", - " return pd.DataFrame()\n", - "\n", - " # Use the first available charge metric\n", - " metric = available_metrics[0]\n", - " print(f\"Using alternate metric: {metric}\")\n", - "\n", - " # Create a copy of the DataFrame to avoid modifying original\n", - " df_copy = df.copy()\n", - "\n", - " # Calculate z-scores for the metric\n", - " df_copy[f'{metric}_zscore'] = (\n", - " df_copy[metric] - df_copy[metric].mean()) / df_copy[metric].std()\n", - "\n", - " # Filter for outliers\n", - " outlier_df = df_copy[df_copy[f'{metric}_zscore'] > threshold].copy()\n", - "\n", - " # Sort by z-score (descending)\n", - " outlier_df = outlier_df.sort_values(f'{metric}_zscore', ascending=False)\n", - "\n", - " # Get NPI column name\n", - " npi_col = column_map['supplier_npi']\n", - " if npi_col is None:\n", - " # Create a synthetic NPI using index\n", - " outlier_df['synthetic_npi'] = 'NPI' + outlier_df.index.astype(str)\n", - " npi_col = 'synthetic_npi'\n", - "\n", - " # Get name column\n", - " name_col = column_map['supplier_name']\n", - " if name_col is None:\n", - " # Create a synthetic name\n", - " outlier_df['synthetic_name'] = outlier_df[npi_col].apply(\n", - " lambda x: f\"Supplier {x}\")\n", - " name_col = 'synthetic_name'\n", - "\n", - " # Get state column\n", - " state_col = column_map['supplier_state']\n", - " if state_col is None:\n", - " # Create a synthetic state\n", - " outlier_df['synthetic_state'] = 'Unknown'\n", - " state_col = 'synthetic_state'\n", - "\n", - " # Get beneficiary and claims columns if they exist\n", - " bene_col = 'DME_Tot_Suplr_Benes' if 'DME_Tot_Suplr_Benes' in df.columns else None\n", - " claims_col = 'DME_Tot_Suplr_Clms' if 'DME_Tot_Suplr_Clms' in df.columns else None\n", - "\n", - " # Select relevant columns\n", - " columns = [npi_col, name_col, state_col, metric, f'{metric}_zscore']\n", - " if bene_col:\n", - " columns.append(bene_col)\n", - " if claims_col:\n", - " columns.append(claims_col)\n", - "\n", - " # Rename columns to standard names\n", - " result_df = outlier_df[columns].copy()\n", - " result_df.columns = [\n", + "if not combined_df.empty:\n", + " # 5.1 Group by (Supplier, year), then sum relevant metrics\n", + " supplier_yearly = combined_df.groupby([\n", " 'Suplr_NPI',\n", - " 'Suplr_Prvdr_Org_Name',\n", - " 'Suplr_Prvdr_State_Abrvtn',\n", - " metric,\n", - " f'{metric}_zscore'\n", - " ] + (['DME_Tot_Suplr_Benes'] if bene_col else []) + (['DME_Tot_Suplr_Clms'] if claims_col else [])\n", - "\n", - " return result_df\n", - "\n", - "\n", - "def detect_unusual_beneficiary_to_claim_ratio(df, year, threshold=2.0):\n", - " \"\"\"\n", - " Identify suppliers with abnormally high claim per beneficiary ratios.\n", - "\n", - " Parameters:\n", - " -----------\n", - " df : DataFrame\n", - " DataFrame for a specific year\n", - " year : int\n", - " The year being analyzed\n", - " threshold : float\n", - " Z-score threshold for flagging outliers (default: 2.0)\n", - "\n", - " Returns:\n", - " --------\n", - " ratio_df : DataFrame\n", - " DataFrame containing suppliers with unusual claim-to-beneficiary ratios\n", - " \"\"\"\n", - " print(\n", - " f\"Identifying suppliers with unusual claims per beneficiary in {year}...\")\n", - "\n", - " # Get column mappings\n", - " column_map = get_column_mapping(df)\n", - "\n", - " # Check for beneficiary and claims columns\n", - " bene_col = None\n", - " claims_col = None\n", - "\n", - " # Look for beneficiary column\n", - " for potential_col in ['DME_Tot_Suplr_Benes', 'Tot_Benes', 'Beneficiaries', 'Total_Beneficiaries']:\n", - " if potential_col in df.columns:\n", - " bene_col = potential_col\n", - " break\n", - "\n", - " # Look for claims column\n", - " for potential_col in ['DME_Tot_Suplr_Clms', 'Tot_Clms', 'Claims', 'Total_Claims']:\n", - " if potential_col in df.columns:\n", - " claims_col = potential_col\n", - " break\n", - "\n", - " if bene_col is None or claims_col is None:\n", - " print(\n", - " f\"Error: Unable to find beneficiary or claims columns in data for year {year}.\")\n", - " print(\"Available columns: \", \", \".join(sorted(df.columns)))\n", - " return pd.DataFrame()\n", - "\n", - " # Create a copy of the DataFrame to avoid modifying original\n", - " df_copy = df.copy()\n", - "\n", - " # Calculate claims per beneficiary\n", - " df_copy['Claims_Per_Beneficiary'] = df_copy[claims_col] / df_copy[bene_col]\n", - "\n", - " # Calculate z-scores\n", - " df_copy['Claims_Per_Beneficiary_zscore'] = (\n", - " df_copy['Claims_Per_Beneficiary'] - df_copy['Claims_Per_Beneficiary'].mean()) / df_copy['Claims_Per_Beneficiary'].std()\n", - "\n", - " # Filter for outliers\n", - " ratio_df = df_copy[df_copy['Claims_Per_Beneficiary_zscore']\n", - " > threshold].copy()\n", - "\n", - " # Sort by z-score (descending)\n", - " ratio_df = ratio_df.sort_values(\n", - " 'Claims_Per_Beneficiary_zscore', ascending=False)\n", - "\n", - " # Get NPI column name\n", - " npi_col = column_map['supplier_npi']\n", - " if npi_col is None:\n", - " # Create a synthetic NPI using index\n", - " ratio_df['synthetic_npi'] = 'NPI' + ratio_df.index.astype(str)\n", - " npi_col = 'synthetic_npi'\n", - "\n", - " # Get name column\n", - " name_col = column_map['supplier_name']\n", - " if name_col is None:\n", - " # Create a synthetic name\n", - " ratio_df['synthetic_name'] = ratio_df[npi_col].apply(\n", - " lambda x: f\"Supplier {x}\")\n", - " name_col = 'synthetic_name'\n", + " 'Suplr_Prvdr_Last_Name_Org',\n", + " 'year'\n", + " ], as_index=False).agg({\n", + " 'Suplr_Sbmtd_Chrgs': 'sum',\n", + " 'Suplr_Mdcr_Pymt_Amt': 'sum',\n", + " 'Tot_Suplr_Benes': 'mean', # average across rows\n", + " 'Tot_Suplr_Clms': 'sum'\n", + " })\n", + "\n", + " # Create a pivot where columns are years, values are 'Suplr_Mdcr_Pymt_Amt'\n", + " pivot_charges = supplier_yearly.pivot_table(\n", + " index=['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org'],\n", + " columns='year',\n", + " values='Suplr_Mdcr_Pymt_Amt',\n", + " fill_value=0\n", + " )\n", "\n", - " # Get state column\n", - " state_col = column_map['supplier_state']\n", - " if state_col is None:\n", - " # Create a synthetic state\n", - " ratio_df['synthetic_state'] = 'Unknown'\n", - " state_col = 'synthetic_state'\n", + " # We'll calculate YOY growth for (2019 vs 2018), (2020 vs 2019), etc.\n", + " growth_rates = pd.DataFrame(index=pivot_charges.index)\n", + " for year_pair in [(2019, 2018), (2020, 2019), (2021, 2020), (2022, 2021)]:\n", + " current, previous = year_pair\n", + " growth_column = f'growth_{current}'\n", + " growth_rates[growth_column] = (\n", + " (pivot_charges[current] - pivot_charges[previous]) /\n", + " pivot_charges[previous].replace(0, np.nan)\n", + " ) * 100\n", + "\n", + " growth_cols = [col for col in growth_rates.columns if col.startswith('growth_')]\n", + " growth_rates['avg_growth'] = growth_rates[growth_cols].mean(axis=1)\n", + "\n", + " # Filter: Supplier must have >0 in all years, and >=100k in 2022\n", + " filter_mask = (\n", + " (pivot_charges[2018] > 0) &\n", + " (pivot_charges[2019] > 0) &\n", + " (pivot_charges[2020] > 0) &\n", + " (pivot_charges[2021] > 0) &\n", + " (pivot_charges[2022] >= 100000)\n", + " )\n", "\n", - " # Select relevant columns\n", - " columns = [\n", - " npi_col,\n", - " name_col,\n", - " state_col,\n", - " bene_col,\n", - " claims_col,\n", - " 'Claims_Per_Beneficiary',\n", - " 'Claims_Per_Beneficiary_zscore'\n", - " ]\n", + " valid_suppliers = pivot_charges[filter_mask]\n", + " valid_growth = growth_rates.loc[valid_suppliers.index].reset_index()\n", "\n", - " # Rename columns to standard names\n", - " result_df = ratio_df[columns].copy()\n", - " result_df.columns = [\n", + " # Merge with aggregated totals (all years combined) just for more reporting info\n", + " supplier_totals = supplier_yearly.groupby([\n", " 'Suplr_NPI',\n", - " 'Suplr_Prvdr_Org_Name',\n", - " 'Suplr_Prvdr_State_Abrvtn',\n", - " 'DME_Tot_Suplr_Benes',\n", - " 'DME_Tot_Suplr_Clms',\n", - " 'Claims_Per_Beneficiary',\n", - " 'Claims_Per_Beneficiary_zscore'\n", - " ]\n", - "\n", - " return result_df\n", - "\n", - "\n", - "def plot_high_growth_suppliers(growth_df, top_n=20):\n", - " \"\"\"\n", - " Create a visualization of suppliers with highest growth rates.\n", - "\n", - " Parameters:\n", - " -----------\n", - " growth_df : DataFrame\n", - " DataFrame from detect_high_growth_suppliers function\n", - " top_n : int\n", - " Number of top suppliers to visualize\n", - "\n", - " Returns:\n", - " --------\n", - " fig : Figure\n", - " Matplotlib figure object containing the visualization\n", - " \"\"\"\n", - " # Take top N suppliers\n", - " plot_df = growth_df.head(top_n)\n", - "\n", - " # Create figure\n", - " fig, ax = plt.subplots(figsize=(14, 10))\n", - "\n", - " # Plot horizontal bar chart\n", - " bars = sns.barplot(\n", - " x='Growth Rate (%)',\n", - " y='Supplier Name',\n", - " data=plot_df,\n", - " palette='viridis',\n", - " ax=ax\n", + " 'Suplr_Prvdr_Last_Name_Org'\n", + " ], as_index=False).agg({\n", + " 'Suplr_Sbmtd_Chrgs': 'sum',\n", + " 'Suplr_Mdcr_Pymt_Amt': 'sum',\n", + " 'Tot_Suplr_Benes': 'mean',\n", + " 'Tot_Suplr_Clms': 'sum'\n", + " })\n", + "\n", + " growth_merged = pd.merge(\n", + " valid_growth,\n", + " supplier_totals,\n", + " on=['Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org'],\n", + " how='left'\n", " )\n", "\n", - " # Add value labels\n", - " for i, bar in enumerate(bars.patches):\n", - " value = plot_df.iloc[i]['Growth Rate (%)']\n", - " ax.text(\n", - " bar.get_width() + 10,\n", - " bar.get_y() + bar.get_height()/2,\n", - " f\"{value:,.1f}%\",\n", - " ha='left',\n", - " va='center',\n", - " fontweight='bold'\n", - " )\n", - "\n", - " # Add a second x-axis for absolute growth\n", - " ax2 = ax.twiny()\n", - " ax2.set_xlabel('Absolute Growth ($)', color='red')\n", - " ax2.tick_params(axis='x', colors='red')\n", - "\n", - " # Plot absolute growth as scatter points\n", - " for i, (_, row) in enumerate(plot_df.iterrows()):\n", - " ax2.scatter(row['Absolute Growth'], i, color='red', s=100, alpha=0.7)\n", - "\n", - " # Format the x-axis for absolute growth with dollar amounts\n", - " ax2.xaxis.set_major_formatter(\n", - " plt.FuncFormatter(lambda x, pos: f'${x:,.0f}'))\n", - "\n", - " # Set labels and title\n", - " ax.set_xlabel('Growth Rate (%)', fontsize=14)\n", - " ax.set_ylabel('Supplier', fontsize=14)\n", - " ax.set_title(f'Top {top_n} Suppliers by Growth Rate',\n", - " fontsize=16, fontweight='bold')\n", - "\n", - " # Add year period information\n", - " if not plot_df.empty:\n", - " year_periods = plot_df['Year Period'].unique()\n", - " period_str = ', '.join(year_periods)\n", - " ax.text(\n", - " 0.5, 1.05, f\"Year Period(s): {period_str}\", transform=ax.transAxes, ha='center')\n", - "\n", - " # Add grid\n", - " ax.grid(axis='x', linestyle='--', alpha=0.7)\n", - "\n", - " plt.tight_layout()\n", - " return fig\n", - "\n", - "\n", - "def plot_geographic_fraud_hotspots(df_by_year, growth_df, threshold=200):\n", - " \"\"\"\n", - " Create a visualization of geographical hotspots for high-growth suppliers.\n", - "\n", - " Parameters:\n", - " -----------\n", - " df_by_year : dict\n", - " Dictionary containing DataFrames by year\n", - " growth_df : DataFrame\n", - " DataFrame from detect_high_growth_suppliers function\n", - " threshold : float\n", - " Growth rate threshold for inclusion in hotspots\n", - "\n", - " Returns:\n", - " --------\n", - " fig : Figure\n", - " Matplotlib figure object containing the visualization\n", - " \"\"\"\n", - " # Filter for extremely high growth rates\n", - " high_growth_df = growth_df[growth_df['Growth Rate (%)'] > threshold].copy()\n", - "\n", - " # Group by state and count unique suppliers\n", - " state_counts = high_growth_df.groupby('Supplier State').agg({\n", - " 'Supplier NPI': 'nunique',\n", - " 'Growth Rate (%)': 'mean',\n", - " 'Absolute Growth': 'sum'\n", - " }).reset_index()\n", - "\n", - " state_counts.columns = ['State', 'Suspicious Suppliers',\n", - " 'Average Growth Rate (%)', 'Total Growth ($)']\n", - "\n", - " # Sort by count (descending)\n", - " state_counts = state_counts.sort_values(\n", - " 'Suspicious Suppliers', ascending=False)\n", - "\n", - " # Create figure with two subplots\n", - " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 10))\n", - "\n", - " # Plot supplier counts\n", - " sns.barplot(\n", - " x='Suspicious Suppliers',\n", - " y='State',\n", - " data=state_counts.head(15),\n", - " palette='Reds_r',\n", - " ax=ax1\n", - " )\n", - "\n", - " ax1.set_xlabel('Number of Suspicious Suppliers', fontsize=14)\n", - " ax1.set_ylabel('State', fontsize=14)\n", - " ax1.set_title('States with Highest Number of Suspicious Suppliers',\n", - " fontsize=16, fontweight='bold')\n", - " ax1.grid(axis='x', linestyle='--', alpha=0.7)\n", - "\n", - " # Plot total growth by state\n", - " sns.barplot(\n", - " x='Total Growth ($)',\n", - " y='State',\n", - " data=state_counts.head(15),\n", - " palette='Blues_r',\n", - " ax=ax2\n", - " )\n", - "\n", - " # Format with dollar amounts\n", - " ax2.xaxis.set_major_formatter(\n", - " plt.FuncFormatter(lambda x, pos: f'${x:,.0f}'))\n", - "\n", - " ax2.set_xlabel('Total Suspicious Growth ($)', fontsize=14)\n", - " ax2.set_ylabel('', fontsize=14) # No y-label for second plot\n", - " ax2.set_title('States with Highest Suspicious Dollar Growth',\n", - " fontsize=16, fontweight='bold')\n", - " ax2.grid(axis='x', linestyle='--', alpha=0.7)\n", - "\n", - " plt.tight_layout()\n", - " return fig\n", - "\n", - "\n", - "def plot_outlier_claim_patterns(outlier_df, metric='DME_Avg_Sbmtd_Chrg', top_n=20):\n", - " \"\"\"\n", - " Create a visualization of suppliers with outlier claim patterns.\n", - "\n", - " Parameters:\n", - " -----------\n", - " outlier_df : DataFrame\n", - " DataFrame from detect_outlier_claim_amounts function\n", - " metric : str\n", - " The metric being analyzed\n", - " top_n : int\n", - " Number of top suppliers to visualize\n", - "\n", - " Returns:\n", - " --------\n", - " fig : Figure\n", - " Matplotlib figure object containing the visualization\n", - " \"\"\"\n", - " # Take top N suppliers\n", - " plot_df = outlier_df.head(top_n)\n", - "\n", - " # Create figure\n", - " fig, ax = plt.subplots(figsize=(14, 10))\n", - "\n", - " # Plot horizontal bar chart\n", - " bars = sns.barplot(\n", - " x=metric,\n", - " y='Suplr_Prvdr_Org_Name',\n", - " data=plot_df,\n", - " palette='plasma',\n", - " ax=ax\n", + " # Sort by average growth descending\n", + " top_growth = growth_merged.sort_values('avg_growth', ascending=False).head(10)\n", + " \n", + " print(\"\\nTop 10 Suppliers by Average Year-over-Year Growth (2018–2022), \\n\\n\",\n", + " \"Filtered to those with >= $100K in 2022 payments:\")\n", + " display(top_growth)\n" + ] + }, + { + "cell_type": "markdown", + "id": "1af5e0cf", + "metadata": {}, + "source": [ + "### Display Year-by-Year Payment Patterns for Top 10\n", + "We'll show each supplier's biggest jump and beneficiary growth, if available." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "25fc60d9", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Detailed Patterns for Top 10 Growth Suppliers:\n", + "\n", + "1. P-Cares Medical Supplies, Llc (NPI: 1063967768)\n", + " - Average Growth: 14975.34%\n", + " - Total Medicare Payments (2018–2022): $15.9M\n", + " - Year-by-year Payments: 2018: $10.4K, 2019: $32.8K, 2020: $26.8K, 2021: $26.4K, 2022: $15.8M\n", + " - Largest Jump: 2021.0 to 2022.0 (+59704.31%)\n", + " - Beneficiary Growth: 52782.1% \n", + "\n", + "2. Lincare Inc (NPI: 1891275590)\n", + " - Average Growth: 10869.74%\n", + " - Total Medicare Payments (2018–2022): $19.5M\n", + " - Year-by-year Payments: 2018: $8.1K, 2019: $3.5M, 2020: $5.0M, 2021: $5.5M, 2022: $5.5M\n", + " - Largest Jump: 2018.0 to 2019.0 (+43427.68%)\n", + " - Beneficiary Growth: 10389.0% \n", + "\n", + "3. Respiratory Services Of Western New York, Inc. (NPI: 1457837080)\n", + " - Average Growth: 6279.38%\n", + " - Total Medicare Payments (2018–2022): $636.0K\n", + " - Year-by-year Payments: 2018: $86, 2019: $21.1K, 2020: $124.4K, 2021: $198.9K, 2022: $291.6K\n", + " - Largest Jump: 2018.0 to 2019.0 (+24521.58%)\n", + " - Beneficiary Growth: 480.9% \n", + "\n", + "4. Vohra Post Acute Care Physicians Of Texas, Pllc (NPI: 1821424789)\n", + " - Average Growth: 6172.20%\n", + " - Total Medicare Payments (2018–2022): $7.9M\n", + " - Year-by-year Payments: 2018: $3.9K, 2019: $954.7K, 2020: $2.0M, 2021: $2.4M, 2022: $2.5M\n", + " - Largest Jump: 2018.0 to 2019.0 (+24557.09%)\n", + " - Beneficiary Growth: 134.7% \n", + "\n", + "5. Aahi St Joseph Mercy Hospital Inc (NPI: 1508938127)\n", + " - Average Growth: 5794.44%\n", + " - Total Medicare Payments (2018–2022): $477.8K\n", + " - Year-by-year Payments: 2018: $62.5K, 2019: $16.4K, 2020: $503, 2021: $117.3K, 2022: $281.1K\n", + " - Largest Jump: 2020.0 to 2021.0 (+23208.81%)\n", + "\n", + "6. Care One Medical Equipment And Supplies, Inc. (NPI: 1174553804)\n", + " - Average Growth: 4821.00%\n", + " - Total Medicare Payments (2018–2022): $1.0M\n", + " - Year-by-year Payments: 2018: $925, 2019: $178.6K, 2020: $269.2K, 2021: $250.8K, 2022: $338.5K\n", + " - Largest Jump: 2018.0 to 2019.0 (+19205.13%)\n", + " - Beneficiary Growth: 70.1% \n", + "\n", + "7. The Home Health Store Of Tomball, Inc. (NPI: 1508826199)\n", + " - Average Growth: 4513.80%\n", + " - Total Medicare Payments (2018–2022): $15.3M\n", + " - Year-by-year Payments: 2018: $26.0K, 2019: $44.4K, 2020: $54.6K, 2021: $83.5K, 2022: $15.0M\n", + " - Largest Jump: 2021.0 to 2022.0 (+17908.42%)\n", + " - Beneficiary Growth: 25079.6% \n", + "\n", + "8. Amerihealth Medical Group, Inc. (NPI: 1750391751)\n", + " - Average Growth: 4495.52%\n", + " - Total Medicare Payments (2018–2022): $1.1M\n", + " - Year-by-year Payments: 2018: $2.4K, 2019: $429.8K, 2020: $228.6K, 2021: $243.5K, 2022: $202.2K\n", + " - Largest Jump: 2018.0 to 2019.0 (+18039.36%)\n", + " - Beneficiary Growth: 2878.8% \n", + "\n", + "9. Scooter Chair Repair Georgia, Llc (NPI: 1598044208)\n", + " - Average Growth: 4126.41%\n", + " - Total Medicare Payments (2018–2022): $4.8M\n", + " - Year-by-year Payments: 2018: $6.3K, 2019: $1.0M, 2020: $1.5M, 2021: $1.2M, 2022: $1.0M\n", + " - Largest Jump: 2018.0 to 2019.0 (+16495.68%)\n", + " - Beneficiary Growth: 131.0% \n", + "\n", + "10. Christian Home Health Services, Inc (NPI: 1437108214)\n", + " - Average Growth: 4116.86%\n", + " - Total Medicare Payments (2018–2022): $573.4K\n", + " - Year-by-year Payments: 2018: $955, 2019: $158.3K, 2020: $126.7K, 2021: $140.4K, 2022: $147.1K\n", + " - Largest Jump: 2018.0 to 2019.0 (+16471.81%)\n", + " - Beneficiary Growth: -27.0% \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n", + "/var/folders/g6/2s70_fq11hn4czzmpgd40ky80000gn/T/ipykernel_34747/4120139511.py:19: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data.sort_values('year', inplace=True)\n" + ] + } + ], + "source": [ + "if not combined_df.empty:\n", + " # Create a function to display details for the top-10\n", + " def show_top_10_growth_details(top_df, supplier_yearly_df):\n", + " print(\"\\nDetailed Patterns for Top 10 Growth Suppliers:\\n\")\n", + " top_npi = top_df['Suplr_NPI'].tolist()\n", + "\n", + " # Filter original groupby results for just these suppliers\n", + " subset = supplier_yearly_df[supplier_yearly_df['Suplr_NPI'].isin(top_npi)].copy()\n", + " subset.sort_values(['Suplr_NPI', 'year'], inplace=True)\n", + "\n", + " for i, row in enumerate(top_df.itertuples(), start=1):\n", + " npi = row.Suplr_NPI\n", + " name = row.Suplr_Prvdr_Last_Name_Org\n", + " avg_growth = row.avg_growth\n", + " total_pay = row.Suplr_Mdcr_Pymt_Amt\n", + "\n", + " # Grab the subset for this supplier\n", + " data = subset[subset['Suplr_NPI'] == npi]\n", + " data.sort_values('year', inplace=True)\n", + "\n", + " print(f\"{i}. {name} (NPI: {npi})\")\n", + " print(f\" - Average Growth: {avg_growth:.2f}%\")\n", + " print(f\" - Total Medicare Payments (2018–2022): {format_dollar_amount(total_pay)}\")\n", + "\n", + " # Show year-by-year\n", + " year_strs = []\n", + " for y in range(2018, 2023):\n", + " row_y = data[data['year'] == y]\n", + " if not row_y.empty:\n", + " pay = row_y.iloc[0]['Suplr_Mdcr_Pymt_Amt']\n", + " year_strs.append(f\"{y}: {format_dollar_amount(pay)}\")\n", + " else:\n", + " year_strs.append(f\"{y}: $0\")\n", + " print(\" - Year-by-year Payments: \" + \", \".join(year_strs))\n", + "\n", + " # Identify the largest yoy jump\n", + " data_list = data[['year', 'Suplr_Mdcr_Pymt_Amt']].sort_values('year').values.tolist()\n", + " max_jump = 0\n", + " jump_year = None\n", + " for idx in range(1, len(data_list)):\n", + " prev_amt = data_list[idx-1][1]\n", + " curr_amt = data_list[idx][1]\n", + " if prev_amt > 0:\n", + " yoy_pct = (curr_amt - prev_amt) / prev_amt * 100\n", + " if yoy_pct > max_jump:\n", + " max_jump = yoy_pct\n", + " jump_year = (data_list[idx-1][0], data_list[idx][0])\n", + "\n", + " if jump_year:\n", + " print(f\" - Largest Jump: {jump_year[0]} to {jump_year[1]} (+{max_jump:.2f}%)\")\n", + "\n", + " # Check beneficiary growth\n", + " benes = data[['year', 'Tot_Suplr_Benes']].dropna()\n", + " if len(benes) > 1:\n", + " benes.sort_values('year', inplace=True)\n", + " first_benes = benes.iloc[0]['Tot_Suplr_Benes']\n", + " last_benes = benes.iloc[-1]['Tot_Suplr_Benes']\n", + " if first_benes > 0:\n", + " bene_growth = (last_benes - first_benes) / first_benes * 100\n", + " print(f\" - Beneficiary Growth: {bene_growth:.1f}% \")\n", + "\n", + " print(\"\")\n", + "\n", + " show_top_10_growth_details(top_growth, supplier_yearly)" + ] + }, + { + "cell_type": "markdown", + "id": "bee5376d", + "metadata": {}, + "source": [ + "# 6. Analysis of High Submitted vs. Low Allowed/Paid Amounts\n", + "We check each supplier's total submitted charges vs. the allowed and paid amounts across **all** years." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2201637e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 10 Suppliers: Highest Submitted Charges vs. Allowed Amount Ratio\n", + "\n", + "- Flatbush Rx Corp (NPI: 1669839536)\n", + " Submitted: $252.8K, Allowed: $1.1K, Paid: $616\n", + " Submitted : Allowed = 221.97x\n", + "\n", + "- Arooba Corp (NPI: 1649225152)\n", + " Submitted: $312.0K, Allowed: $1.7K, Paid: $1.1K\n", + " Submitted : Allowed = 182.41x\n", + "\n", + "- Mingocare Inc (NPI: 1003228156)\n", + " Submitted: $702.7K, Allowed: $4.0K, Paid: $2.4K\n", + " Submitted : Allowed = 177.83x\n", + "\n", + "- Nile City Pharmacy Inc (NPI: 1578076212)\n", + " Submitted: $106.4K, Allowed: $702, Paid: $524\n", + " Submitted : Allowed = 151.50x\n", + "\n", + "- Farmacia Julia Discount #2 Llc (NPI: 1457430274)\n", + " Submitted: $410.4K, Allowed: $3.4K, Paid: $2.1K\n", + " Submitted : Allowed = 122.31x\n", + "\n", + "- Gamer Pharmacy Inc (NPI: 1588697692)\n", + " Submitted: $9.3M, Allowed: $76.9K, Paid: $56.8K\n", + " Submitted : Allowed = 120.95x\n", + "\n", + "- Madina Pharmacy Inc (NPI: 1538525316)\n", + " Submitted: $427.3K, Allowed: $4.3K, Paid: $2.7K\n", + " Submitted : Allowed = 99.99x\n", + "\n", + "- Colonial Pharmacy Inc (NPI: 1255438198)\n", + " Submitted: $407.8K, Allowed: $4.1K, Paid: $2.5K\n", + " Submitted : Allowed = 98.28x\n", + "\n", + "- Blue Ridge Pharmacy Inc (NPI: 1538564596)\n", + " Submitted: $2.0M, Allowed: $21.0K, Paid: $15.6K\n", + " Submitted : Allowed = 92.84x\n", + "\n", + "- Welch Pharmacy Inc (NPI: 1336326792)\n", + " Submitted: $1.2M, Allowed: $13.4K, Paid: $8.3K\n", + " Submitted : Allowed = 92.37x\n", + "\n", + "\n", + "Top 10 Suppliers: Highest Submitted Charges vs. Paid Amount Ratio\n", + "\n", + "- Flatbush Rx Corp (NPI: 1669839536)\n", + " Submitted: $252.8K, Allowed: $1.1K, Paid: $616\n", + " Submitted : Paid = 410.09x\n", + "\n", + "- Mingocare Inc (NPI: 1003228156)\n", + " Submitted: $702.7K, Allowed: $4.0K, Paid: $2.4K\n", + " Submitted : Paid = 292.70x\n", + "\n", + "- Arooba Corp (NPI: 1649225152)\n", + " Submitted: $312.0K, Allowed: $1.7K, Paid: $1.1K\n", + " Submitted : Paid = 285.22x\n", + "\n", + "- Nile City Pharmacy Inc (NPI: 1578076212)\n", + " Submitted: $106.4K, Allowed: $702, Paid: $524\n", + " Submitted : Paid = 202.93x\n", + "\n", + "- Farmacia Julia Discount #2 Llc (NPI: 1457430274)\n", + " Submitted: $410.4K, Allowed: $3.4K, Paid: $2.1K\n", + " Submitted : Paid = 196.85x\n", + "\n", + "- Colonial Pharmacy Inc (NPI: 1255438198)\n", + " Submitted: $407.8K, Allowed: $4.1K, Paid: $2.5K\n", + " Submitted : Paid = 165.16x\n", + "\n", + "- Gamer Pharmacy Inc (NPI: 1588697692)\n", + " Submitted: $9.3M, Allowed: $76.9K, Paid: $56.8K\n", + " Submitted : Paid = 163.71x\n", + "\n", + "- Madina Pharmacy Inc (NPI: 1538525316)\n", + " Submitted: $427.3K, Allowed: $4.3K, Paid: $2.7K\n", + " Submitted : Paid = 155.57x\n", + "\n", + "- Welch Pharmacy Inc (NPI: 1336326792)\n", + " Submitted: $1.2M, Allowed: $13.4K, Paid: $8.3K\n", + " Submitted : Paid = 148.70x\n", + "\n", + "- Crystal Drugs Inc (NPI: 1124049184)\n", + " Submitted: $720.3K, Allowed: $8.2K, Paid: $5.2K\n", + " Submitted : Paid = 137.98x\n", + "\n" + ] + } + ], + "source": [ + "if not combined_df.empty:\n", + " supplier_totals_ap = combined_df.groupby([\n", + " 'Suplr_NPI',\n", + " 'Suplr_Prvdr_Last_Name_Org'\n", + " ], as_index=False).agg({\n", + " 'Suplr_Sbmtd_Chrgs': 'sum',\n", + " 'Suplr_Mdcr_Alowd_Amt': 'sum',\n", + " 'Suplr_Mdcr_Pymt_Amt': 'sum',\n", + " 'Tot_Suplr_Benes': 'mean',\n", + " 'Tot_Suplr_Clms': 'sum'\n", + " })\n", + "\n", + " supplier_totals_ap['submitted_allowed_ratio'] = (\n", + " supplier_totals_ap['Suplr_Sbmtd_Chrgs'] / (supplier_totals_ap['Suplr_Mdcr_Alowd_Amt'] + 1e-9)\n", " )\n", - "\n", - " # Add z-score labels\n", - " for i, bar in enumerate(bars.patches):\n", - " zscore = plot_df.iloc[i][f'{metric}_zscore']\n", - " ax.text(\n", - " bar.get_width() + 5,\n", - " bar.get_y() + bar.get_height()/2,\n", - " f\"z={zscore:.1f}\",\n", - " ha='left',\n", - " va='center',\n", - " fontweight='bold'\n", - " )\n", - "\n", - " # Format the x-axis with dollar amounts if it's a monetary metric\n", - " if 'Pymt' in metric or 'Chrg' in metric or 'Amt' in metric:\n", - " ax.xaxis.set_major_formatter(\n", - " plt.FuncFormatter(lambda x, pos: f'${x:,.0f}'))\n", - "\n", - " # Set labels and title\n", - " metric_name = metric.replace('_', ' ')\n", - " ax.set_xlabel(metric_name, fontsize=14)\n", - " ax.set_ylabel('Supplier', fontsize=14)\n", - " ax.set_title(f'Top {top_n} Suppliers with Unusually High {metric_name}',\n", - " fontsize=16, fontweight='bold')\n", - "\n", - " # Add grid\n", - " ax.grid(axis='x', linestyle='--', alpha=0.7)\n", - "\n", - " plt.tight_layout()\n", - " return fig\n", - "\n", - "\n", - "def plot_beneficiary_claim_ratio_outliers(ratio_df, top_n=20):\n", - " \"\"\"\n", - " Create a visualization of suppliers with unusual beneficiary-to-claim ratios.\n", - "\n", - " Parameters:\n", - " -----------\n", - " ratio_df : DataFrame\n", - " DataFrame from detect_unusual_beneficiary_to_claim_ratio function\n", - " top_n : int\n", - " Number of top suppliers to visualize\n", - "\n", - " Returns:\n", - " --------\n", - " fig : Figure\n", - " Matplotlib figure object containing the visualization\n", - " \"\"\"\n", - " # Take top N suppliers\n", - " plot_df = ratio_df.head(top_n)\n", - "\n", - " # Create figure\n", - " fig, ax = plt.subplots(figsize=(14, 10))\n", - "\n", - " # Plot horizontal bar chart\n", - " bars = sns.barplot(\n", - " x='Claims_Per_Beneficiary',\n", - " y='Suplr_Prvdr_Org_Name',\n", - " data=plot_df,\n", - " palette='magma',\n", - " ax=ax\n", + " supplier_totals_ap['submitted_paid_ratio'] = (\n", + " supplier_totals_ap['Suplr_Sbmtd_Chrgs'] / (supplier_totals_ap['Suplr_Mdcr_Pymt_Amt'] + 1e-9)\n", " )\n", "\n", - " # Add z-score labels\n", - " for i, bar in enumerate(bars.patches):\n", - " zscore = plot_df.iloc[i]['Claims_Per_Beneficiary_zscore']\n", - " ax.text(\n", - " bar.get_width() + 0.5,\n", - " bar.get_y() + bar.get_height()/2,\n", - " f\"z={zscore:.1f}\",\n", - " ha='left',\n", - " va='center',\n", - " fontweight='bold'\n", - " )\n", - "\n", - " # Set labels and title\n", - " ax.set_xlabel('Claims Per Beneficiary', fontsize=14)\n", - " ax.set_ylabel('Supplier', fontsize=14)\n", - " ax.set_title(f'Top {top_n} Suppliers with Unusually High Claims Per Beneficiary',\n", - " fontsize=16, fontweight='bold')\n", - "\n", - " # Add grid\n", - " ax.grid(axis='x', linestyle='--', alpha=0.7)\n", - "\n", - " plt.tight_layout()\n", - " return fig\n", - "\n", - "\n", - "def plot_combined_fraud_indicators(growth_df, outlier_df, ratio_df, top_n=20):\n", - " \"\"\"\n", - " Create a visualization of suppliers that appear in multiple fraud indicator lists.\n", - "\n", - " Parameters:\n", - " -----------\n", - " growth_df : DataFrame\n", - " DataFrame from detect_high_growth_suppliers function\n", - " outlier_df : DataFrame\n", - " DataFrame from detect_outlier_claim_amounts function\n", - " ratio_df : DataFrame\n", - " DataFrame from detect_unusual_beneficiary_to_claim_ratio function\n", - " top_n : int\n", - " Number of top suppliers to visualize\n", - "\n", - " Returns:\n", - " --------\n", - " fig : Figure\n", - " Matplotlib figure object containing the visualization\n", - " \"\"\"\n", - " print(\"Identifying suppliers with multiple fraud indicators...\")\n", - "\n", - " # Check if any of the dataframes are empty\n", - " if growth_df.empty or outlier_df.empty or ratio_df.empty:\n", - " print(\"Warning: One or more fraud indicator dataframes are empty. Cannot perform combined analysis.\")\n", - " # Return an empty plot\n", - " fig, ax = plt.subplots(figsize=(15, 12))\n", - " ax.text(0.5, 0.5, \"Insufficient data for combined fraud indicator analysis\",\n", - " ha='center', va='center', fontsize=14)\n", - " return fig, pd.DataFrame()\n", - "\n", - " # Get unique supplier NPIs from each DataFrame\n", - " growth_npis = set(growth_df['Supplier NPI'])\n", - " outlier_npis = set(outlier_df['Suplr_NPI'])\n", - " ratio_npis = set(ratio_df['Suplr_NPI'])\n", - "\n", - " # Find suppliers that appear in multiple lists\n", - " suspicious_suppliers = []\n", - "\n", - " # Check all 3 indicators\n", - " all_three = growth_npis & outlier_npis & ratio_npis\n", - " for npi in all_three:\n", - " try:\n", - " growth_row = growth_df[growth_df['Supplier NPI'] == npi].iloc[0]\n", - " outlier_row = outlier_df[outlier_df['Suplr_NPI'] == npi].iloc[0]\n", - " ratio_row = ratio_df[ratio_df['Suplr_NPI'] == npi].iloc[0]\n", - "\n", - " suspicious_suppliers.append({\n", - " 'NPI': npi,\n", - " 'Supplier Name': growth_row['Supplier Name'],\n", - " 'State': growth_row['Supplier State'],\n", - " 'Growth Rate (%)': growth_row['Growth Rate (%)'],\n", - " 'Claims Per Beneficiary': ratio_row['Claims_Per_Beneficiary'],\n", - " # Use index to get the charge column\n", - " 'Avg Charge': outlier_row[outlier_row.columns[3]],\n", - " 'Indicators': 3,\n", - " 'Flags': 'High Growth, High Charges, High Claims/Beneficiary'\n", - " })\n", - " except (IndexError, KeyError) as e:\n", - " print(\n", - " f\"Error processing supplier {npi} with all three indicators: {str(e)}\")\n", - " continue\n", - "\n", - " # Check growth + outlier\n", - " growth_outlier = (growth_npis & outlier_npis) - all_three\n", - " for npi in growth_outlier:\n", - " try:\n", - " growth_row = growth_df[growth_df['Supplier NPI'] == npi].iloc[0]\n", - " outlier_row = outlier_df[outlier_df['Suplr_NPI'] == npi].iloc[0]\n", - "\n", - " suspicious_suppliers.append({\n", - " 'NPI': npi,\n", - " 'Supplier Name': growth_row['Supplier Name'],\n", - " 'State': growth_row['Supplier State'],\n", - " 'Growth Rate (%)': growth_row['Growth Rate (%)'],\n", - " 'Claims Per Beneficiary': 0,\n", - " # Use index to get the charge column\n", - " 'Avg Charge': outlier_row[outlier_row.columns[3]],\n", - " 'Indicators': 2,\n", - " 'Flags': 'High Growth, High Charges'\n", + " # Focus on those with at least $100K submitted charges to reduce noise\n", + " significant_ap = supplier_totals_ap[supplier_totals_ap['Suplr_Sbmtd_Chrgs'] >= 100000]\n", + "\n", + " # Highest submitted-to-allowed ratio\n", + " top_allowed = significant_ap.sort_values(\n", + " 'submitted_allowed_ratio', ascending=False\n", + " ).head(10)\n", + "\n", + " print(\"Top 10 Suppliers: Highest Submitted Charges vs. Allowed Amount Ratio\\n\")\n", + " for i, row in top_allowed.iterrows():\n", + " npi = row['Suplr_NPI']\n", + " name = row['Suplr_Prvdr_Last_Name_Org']\n", + " submitted = row['Suplr_Sbmtd_Chrgs']\n", + " allowed = row['Suplr_Mdcr_Alowd_Amt']\n", + " paid = row['Suplr_Mdcr_Pymt_Amt']\n", + " ratio = row['submitted_allowed_ratio']\n", + "\n", + " print(f\"- {name} (NPI: {npi})\")\n", + " print(f\" Submitted: {format_dollar_amount(submitted)}, Allowed: {format_dollar_amount(allowed)}, Paid: {format_dollar_amount(paid)}\")\n", + " print(f\" Submitted : Allowed = {ratio:.2f}x\\n\")\n", + "\n", + " # Highest submitted-to-paid ratio\n", + " top_paid = significant_ap.sort_values(\n", + " 'submitted_paid_ratio', ascending=False\n", + " ).head(10)\n", + "\n", + " print(\"\\nTop 10 Suppliers: Highest Submitted Charges vs. Paid Amount Ratio\\n\")\n", + " for i, row in top_paid.iterrows():\n", + " npi = row['Suplr_NPI']\n", + " name = row['Suplr_Prvdr_Last_Name_Org']\n", + " submitted = row['Suplr_Sbmtd_Chrgs']\n", + " allowed = row['Suplr_Mdcr_Alowd_Amt']\n", + " paid = row['Suplr_Mdcr_Pymt_Amt']\n", + " ratio = row['submitted_paid_ratio']\n", + "\n", + " print(f\"- {name} (NPI: {npi})\")\n", + " print(f\" Submitted: {format_dollar_amount(submitted)}, Allowed: {format_dollar_amount(allowed)}, Paid: {format_dollar_amount(paid)}\")\n", + " print(f\" Submitted : Paid = {ratio:.2f}x\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "ebfae2d2", + "metadata": {}, + "source": [ + "# 7. Peer Group Analysis\n", + "Analyze suppliers in the context of their **specialty**, **state**, or combined specialty–state. \n", + "Outliers are flagged if they exceed 3× the peer group's median in more than one of these metrics:\n", + "- Total Claims\n", + "- Total Submitted Charges\n", + "- Total Payments" + ] + }, + { + "cell_type": "markdown", + "id": "9b851377", + "metadata": {}, + "source": [ + "# 8. Conclusions & Next Steps\n", + "We've combined multi-year DME data, identified year-over-year outliers, analyzed high submitted vs. allowed/paid ratios, and performed peer-group checks.\n", + "\n", + "### Potential Enhancements\n", + "1. **Additional Metrics**: Incorporate DME-specific categories (e.g., prosthetics vs. drug/nutrition) and investigate outliers in each.\n", + "2. **Machine Learning**: Replace threshold-based outlier detection with algorithms (Isolation Forest, DBSCAN, etc.).\n", + "3. **Visualization**: Plot distributions, boxplots, or time-series charts for top suspicious suppliers.\n", + "4. **Interactive Dashboards**: Provide an interface for users to adjust thresholds and instantly see flagged suppliers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8f0e0b17", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "## Peer Group Analysis by Specialty\n", + "\n", + "Significant Specialty Outliers (exceeding 3× median in >=2 metrics):\n", + "- Accredo Health Group Inc (NPI: 1417915653)\n", + " Specialty: Pharmacy | State: PA\n", + " Claims: 209,938, Charges: $3464.6M, Payments: $1267.1M\n", + "\n", + "- North Coast Medical Supply, Llc (NPI: 1245259282)\n", + " Specialty: Pharmacy | State: CA\n", + " Claims: 1,236,598, Charges: $3458.1M, Payments: $245.3M\n", + "\n", + "- Lincare Pharmacy Services Inc. (NPI: 1780748939)\n", + " Specialty: Pharmacy | State: FL\n", + " Claims: 2,533,531, Charges: $2178.0M, Payments: $644.5M\n", + "\n", + "- Zoll Services Llc (NPI: 1164535274)\n", + " Specialty: Other Medical Supply Company | State: PA\n", + " Claims: 345,064, Charges: $1365.1M, Payments: $738.0M\n", + "\n", + "- Degc Enterprises (U.S.), Inc. (NPI: 1295827780)\n", + " Specialty: Pharmacy | State: FL\n", + " Claims: 1,329,923, Charges: $1291.7M, Payments: $325.9M\n", + "\n", + "- United States Medical Supply, Llc (NPI: 1700889227)\n", + " Specialty: Other Medical Supply Company | State: FL\n", + " Claims: 3,296,437, Charges: $1103.4M, Payments: $297.8M\n", + "\n", + "- 180 Medical Inc (NPI: 1639160708)\n", + " Specialty: Other Medical Supply Company | State: OK\n", + " Claims: 1,224,531, Charges: $1036.1M, Payments: $420.4M\n", + "\n", + "- Rgh Enterprises, Llc (NPI: 1609858729)\n", + " Specialty: All Other Suppliers | State: OH\n", + " Claims: 1,242,822, Charges: $965.4M, Payments: $271.6M\n", + "\n", + "- Lincare Pharmacy Services Inc. (NPI: 1003970260)\n", + " Specialty: Pharmacy | State: CA\n", + " Claims: 953,473, Charges: $817.3M, Payments: $240.2M\n", + "\n", + "- Coram Alternate Site Services Inc (NPI: 1386674067)\n", + " Specialty: All Other Suppliers | State: MN\n", + " Claims: 26,085, Charges: $786.8M, Payments: $53.1M\n", + "\n", + "\n", + "## Peer Group Analysis by State\n", + "\n", + "Significant State Outliers (>= 3× median in >=2 metrics):\n", + "- Accredo Health Group Inc (NPI: 1417915653)\n", + " State: PA | Specialty: Pharmacy\n", + " Claims: 209,938, Charges: $3464.6M, Payments: $1267.1M\n", + "\n", + "- North Coast Medical Supply, Llc (NPI: 1245259282)\n", + " State: CA | Specialty: Pharmacy\n", + " Claims: 1,236,598, Charges: $3458.1M, Payments: $245.3M\n", + "\n", + "- Lincare Pharmacy Services Inc. (NPI: 1780748939)\n", + " State: FL | Specialty: Pharmacy\n", + " Claims: 2,533,531, Charges: $2178.0M, Payments: $644.5M\n", + "\n", + "- Zoll Services Llc (NPI: 1164535274)\n", + " State: PA | Specialty: Other Medical Supply Company\n", + " Claims: 345,064, Charges: $1365.1M, Payments: $738.0M\n", + "\n", + "- Degc Enterprises (U.S.), Inc. (NPI: 1295827780)\n", + " State: FL | Specialty: Pharmacy\n", + " Claims: 1,329,923, Charges: $1291.7M, Payments: $325.9M\n", + "\n", + "- United States Medical Supply, Llc (NPI: 1700889227)\n", + " State: FL | Specialty: Other Medical Supply Company\n", + " Claims: 3,296,437, Charges: $1103.4M, Payments: $297.8M\n", + "\n", + "- 180 Medical Inc (NPI: 1639160708)\n", + " State: OK | Specialty: Other Medical Supply Company\n", + " Claims: 1,224,531, Charges: $1036.1M, Payments: $420.4M\n", + "\n", + "- Rgh Enterprises, Llc (NPI: 1609858729)\n", + " State: OH | Specialty: All Other Suppliers\n", + " Claims: 1,242,822, Charges: $965.4M, Payments: $271.6M\n", + "\n", + "- Lincare Pharmacy Services Inc. (NPI: 1003970260)\n", + " State: CA | Specialty: Pharmacy\n", + " Claims: 953,473, Charges: $817.3M, Payments: $240.2M\n", + "\n", + "- Coram Alternate Site Services Inc (NPI: 1386674067)\n", + " State: MN | Specialty: All Other Suppliers\n", + " Claims: 26,085, Charges: $786.8M, Payments: $53.1M\n", + "\n", + "\n", + "## Peer Group Analysis by Combined Specialty–State\n", + "\n", + "Significant Combined Specialty–State Outliers (>= 3× median in >=2 metrics):\n", + "- Accredo Health Group Inc (NPI: 1417915653)\n", + " Specialty: Pharmacy | State: PA\n", + " Claims: 209,938, Charges: $3464.6M, Payments: $1267.1M\n", + "\n", + "- North Coast Medical Supply, Llc (NPI: 1245259282)\n", + " Specialty: Pharmacy | State: CA\n", + " Claims: 1,236,598, Charges: $3458.1M, Payments: $245.3M\n", + "\n", + "- Lincare Pharmacy Services Inc. (NPI: 1780748939)\n", + " Specialty: Pharmacy | State: FL\n", + " Claims: 2,533,531, Charges: $2178.0M, Payments: $644.5M\n", + "\n", + "- Zoll Services Llc (NPI: 1164535274)\n", + " Specialty: Other Medical Supply Company | State: PA\n", + " Claims: 345,064, Charges: $1365.1M, Payments: $738.0M\n", + "\n", + "- Degc Enterprises (U.S.), Inc. (NPI: 1295827780)\n", + " Specialty: Pharmacy | State: FL\n", + " Claims: 1,329,923, Charges: $1291.7M, Payments: $325.9M\n", + "\n", + "- United States Medical Supply, Llc (NPI: 1700889227)\n", + " Specialty: Other Medical Supply Company | State: FL\n", + " Claims: 3,296,437, Charges: $1103.4M, Payments: $297.8M\n", + "\n", + "- 180 Medical Inc (NPI: 1639160708)\n", + " Specialty: Other Medical Supply Company | State: OK\n", + " Claims: 1,224,531, Charges: $1036.1M, Payments: $420.4M\n", + "\n", + "- Lincare Pharmacy Services Inc. (NPI: 1003970260)\n", + " Specialty: Pharmacy | State: CA\n", + " Claims: 953,473, Charges: $817.3M, Payments: $240.2M\n", + "\n", + "- Coram Alternate Site Services Inc (NPI: 1386674067)\n", + " Specialty: All Other Suppliers | State: MN\n", + " Claims: 26,085, Charges: $786.8M, Payments: $53.1M\n", + "\n", + "- Caremark, L.L.C. (NPI: 1134100134)\n", + " Specialty: All Other Suppliers | State: IL\n", + " Claims: 61,520, Charges: $737.9M, Payments: $244.4M\n", + "\n" + ] + } + ], + "source": [ + "if not combined_df.empty:\n", + " # Ensure we have columns needed for specialty/state analysis\n", + " required_cols = [\n", + " 'Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org',\n", + " 'Suplr_Prvdr_Spclty_Desc', 'Suplr_Prvdr_State_Abrvtn',\n", + " 'Suplr_Sbmtd_Chrgs', 'Suplr_Mdcr_Pymt_Amt',\n", + " 'Tot_Suplr_Clms', 'Tot_Suplr_Srvcs'\n", + " ]\n", + " missing_cols = [c for c in required_cols if c not in combined_df.columns]\n", + " if missing_cols:\n", + " print(f\"Missing columns for Peer Group Analysis: {missing_cols}\")\n", + " else:\n", + " supplier_metrics = combined_df.groupby([\n", + " 'Suplr_NPI', 'Suplr_Prvdr_Last_Name_Org',\n", + " 'Suplr_Prvdr_Spclty_Desc', 'Suplr_Prvdr_State_Abrvtn'\n", + " ], as_index=False).agg({\n", + " 'Suplr_Sbmtd_Chrgs': 'sum',\n", + " 'Suplr_Mdcr_Pymt_Amt': 'sum',\n", + " 'Tot_Suplr_Clms': 'sum',\n", + " 'Tot_Suplr_Srvcs': 'sum'\n", + " })\n", + "\n", + " # Add derived metrics\n", + " supplier_metrics['Avg_Chrg_Per_Clm'] = supplier_metrics['Suplr_Sbmtd_Chrgs'] / supplier_metrics['Tot_Suplr_Clms'].replace(0, np.nan)\n", + " supplier_metrics['Avg_Pymt_Per_Clm'] = supplier_metrics['Suplr_Mdcr_Pymt_Amt'] / supplier_metrics['Tot_Suplr_Clms'].replace(0, np.nan)\n", + " supplier_metrics['Avg_Srvcs_Per_Clm'] = supplier_metrics['Tot_Suplr_Srvcs'] / supplier_metrics['Tot_Suplr_Clms'].replace(0, np.nan)\n", + "\n", + " print(\"\\n## Peer Group Analysis by Specialty\\n\")\n", + " specialty_counts = supplier_metrics['Suplr_Prvdr_Spclty_Desc'].value_counts()\n", + " valid_specialties = specialty_counts[specialty_counts >= 5].index # at least 5 suppliers\n", + "\n", + " if len(valid_specialties) > 0:\n", + " peer_specialty_metrics = supplier_metrics[supplier_metrics['Suplr_Prvdr_Spclty_Desc'].isin(valid_specialties)].groupby('Suplr_Prvdr_Spclty_Desc').agg({\n", + " 'Suplr_Sbmtd_Chrgs': ['median'],\n", + " 'Suplr_Mdcr_Pymt_Amt': ['median'],\n", + " 'Tot_Suplr_Clms': ['median'],\n", + " 'Tot_Suplr_Srvcs': ['median']\n", " })\n", - " except (IndexError, KeyError) as e:\n", - " print(\n", - " f\"Error processing supplier {npi} with growth+outlier indicators: {str(e)}\")\n", - " continue\n", - "\n", - " # Check growth + ratio\n", - " growth_ratio = (growth_npis & ratio_npis) - all_three\n", - " for npi in growth_ratio:\n", - " try:\n", - " growth_row = growth_df[growth_df['Supplier NPI'] == npi].iloc[0]\n", - " ratio_row = ratio_df[ratio_df['Suplr_NPI'] == npi].iloc[0]\n", - "\n", - " suspicious_suppliers.append({\n", - " 'NPI': npi,\n", - " 'Supplier Name': growth_row['Supplier Name'],\n", - " 'State': growth_row['Supplier State'],\n", - " 'Growth Rate (%)': growth_row['Growth Rate (%)'],\n", - " 'Claims Per Beneficiary': ratio_row['Claims_Per_Beneficiary'],\n", - " 'Avg Charge': 0,\n", - " 'Indicators': 2,\n", - " 'Flags': 'High Growth, High Claims/Beneficiary'\n", + " peer_specialty_metrics.columns = [\"_\".join(col) for col in peer_specialty_metrics.columns]\n", + "\n", + " outliers_by_specialty = []\n", + "\n", + " for specialty in valid_specialties:\n", + " group = supplier_metrics[supplier_metrics['Suplr_Prvdr_Spclty_Desc'] == specialty]\n", + " med_clms = peer_specialty_metrics.loc[specialty, 'Tot_Suplr_Clms_median']\n", + " med_chrg = peer_specialty_metrics.loc[specialty, 'Suplr_Sbmtd_Chrgs_median']\n", + " med_pay = peer_specialty_metrics.loc[specialty, 'Suplr_Mdcr_Pymt_Amt_median']\n", + "\n", + " # Compare each supplier to 3x median\n", + " claim_outliers = group[group['Tot_Suplr_Clms'] > 3 * med_clms]\n", + " charge_outliers = group[group['Suplr_Sbmtd_Chrgs'] > 3 * med_chrg]\n", + " payment_outliers = group[group['Suplr_Mdcr_Pymt_Amt'] > 3 * med_pay]\n", + "\n", + " # Combine\n", + " all_out = pd.concat([\n", + " claim_outliers[['Suplr_NPI']].assign(flag='claims'),\n", + " charge_outliers[['Suplr_NPI']].assign(flag='charges'),\n", + " payment_outliers[['Suplr_NPI']].assign(flag='payments')\n", + " ], ignore_index=True)\n", + " # We want suppliers that appear at least in 2 out of 3 categories\n", + " outlier_counts = all_out.groupby('Suplr_NPI').size()\n", + " multi_flags = outlier_counts[outlier_counts >= 2].index\n", + " multi_outliers = group[group['Suplr_NPI'].isin(multi_flags)]\n", + "\n", + " for idx, row in multi_outliers.iterrows():\n", + " outliers_by_specialty.append({\n", + " 'NPI': row['Suplr_NPI'],\n", + " 'Name': row['Suplr_Prvdr_Last_Name_Org'],\n", + " 'Specialty': row['Suplr_Prvdr_Spclty_Desc'],\n", + " 'State': row['Suplr_Prvdr_State_Abrvtn'],\n", + " 'Total_Claims': row['Tot_Suplr_Clms'],\n", + " 'Total_Charges': row['Suplr_Sbmtd_Chrgs'],\n", + " 'Total_Payments': row['Suplr_Mdcr_Pymt_Amt']\n", + " })\n", + "\n", + " if len(outliers_by_specialty) > 0:\n", + " # Just show top 10 by total charges\n", + " outliers_by_specialty = sorted(\n", + " outliers_by_specialty,\n", + " key=lambda x: x['Total_Charges'],\n", + " reverse=True\n", + " )\n", + "\n", + " print(\"Significant Specialty Outliers (exceeding 3× median in >=2 metrics):\")\n", + " for outlier in outliers_by_specialty[:10]:\n", + " print(f\"- {outlier['Name']} (NPI: {outlier['NPI']})\")\n", + " print(f\" Specialty: {outlier['Specialty']} | State: {outlier['State']}\")\n", + " print(f\" Claims: {outlier['Total_Claims']:,}, Charges: {format_dollar_amount(outlier['Total_Charges'])}, Payments: {format_dollar_amount(outlier['Total_Payments'])}\\n\")\n", + " else:\n", + " print(\"No multi-metric outliers by specialty.\")\n", + " else:\n", + " print(\"No specialty with >=5 suppliers.\")\n", + "\n", + " print(\"\\n## Peer Group Analysis by State\\n\")\n", + " state_counts = supplier_metrics['Suplr_Prvdr_State_Abrvtn'].value_counts()\n", + " valid_states = state_counts[state_counts >= 5].index\n", + "\n", + " if len(valid_states) > 0:\n", + " peer_state_metrics = supplier_metrics[supplier_metrics['Suplr_Prvdr_State_Abrvtn'].isin(valid_states)].groupby('Suplr_Prvdr_State_Abrvtn').agg({\n", + " 'Suplr_Sbmtd_Chrgs': ['median'],\n", + " 'Suplr_Mdcr_Pymt_Amt': ['median'],\n", + " 'Tot_Suplr_Clms': ['median'],\n", + " 'Tot_Suplr_Srvcs': ['median']\n", " })\n", - " except (IndexError, KeyError) as e:\n", - " print(\n", - " f\"Error processing supplier {npi} with growth+ratio indicators: {str(e)}\")\n", - " continue\n", - "\n", - " # Check outlier + ratio\n", - " outlier_ratio = (outlier_npis & ratio_npis) - all_three\n", - " for npi in outlier_ratio:\n", - " try:\n", - " outlier_row = outlier_df[outlier_df['Suplr_NPI'] == npi].iloc[0]\n", - " ratio_row = ratio_df[ratio_df['Suplr_NPI'] == npi].iloc[0]\n", - "\n", - " suspicious_suppliers.append({\n", - " 'NPI': npi,\n", - " 'Supplier Name': outlier_row['Suplr_Prvdr_Org_Name'],\n", - " 'State': outlier_row['Suplr_Prvdr_State_Abrvtn'],\n", - " 'Growth Rate (%)': 0,\n", - " 'Claims Per Beneficiary': ratio_row['Claims_Per_Beneficiary'],\n", - " # Use index to get the charge column\n", - " 'Avg Charge': outlier_row[outlier_row.columns[3]],\n", - " 'Indicators': 2,\n", - " 'Flags': 'High Charges, High Claims/Beneficiary'\n", + " peer_state_metrics.columns = [\"_\".join(col) for col in peer_state_metrics.columns]\n", + "\n", + " outliers_by_state = []\n", + "\n", + " for st in valid_states:\n", + " group = supplier_metrics[supplier_metrics['Suplr_Prvdr_State_Abrvtn'] == st]\n", + " med_clms = peer_state_metrics.loc[st, 'Tot_Suplr_Clms_median']\n", + " med_chrg = peer_state_metrics.loc[st, 'Suplr_Sbmtd_Chrgs_median']\n", + " med_pay = peer_state_metrics.loc[st, 'Suplr_Mdcr_Pymt_Amt_median']\n", + "\n", + " # Compare to 3x\n", + " claim_outliers = group[group['Tot_Suplr_Clms'] > 3 * med_clms]\n", + " charge_outliers = group[group['Suplr_Sbmtd_Chrgs'] > 3 * med_chrg]\n", + " payment_outliers = group[group['Suplr_Mdcr_Pymt_Amt'] > 3 * med_pay]\n", + "\n", + " all_out = pd.concat([\n", + " claim_outliers[['Suplr_NPI']].assign(flag='claims'),\n", + " charge_outliers[['Suplr_NPI']].assign(flag='charges'),\n", + " payment_outliers[['Suplr_NPI']].assign(flag='payments')\n", + " ], ignore_index=True)\n", + " outlier_counts = all_out.groupby('Suplr_NPI').size()\n", + " multi_flags = outlier_counts[outlier_counts >= 2].index\n", + " multi_outliers = group[group['Suplr_NPI'].isin(multi_flags)]\n", + "\n", + " for idx, row in multi_outliers.iterrows():\n", + " outliers_by_state.append({\n", + " 'NPI': row['Suplr_NPI'],\n", + " 'Name': row['Suplr_Prvdr_Last_Name_Org'],\n", + " 'Specialty': row['Suplr_Prvdr_Spclty_Desc'],\n", + " 'State': st,\n", + " 'Total_Claims': row['Tot_Suplr_Clms'],\n", + " 'Total_Charges': row['Suplr_Sbmtd_Chrgs'],\n", + " 'Total_Payments': row['Suplr_Mdcr_Pymt_Amt']\n", + " })\n", + "\n", + " if len(outliers_by_state) > 0:\n", + " outliers_by_state = sorted(\n", + " outliers_by_state,\n", + " key=lambda x: x['Total_Charges'],\n", + " reverse=True\n", + " )\n", + " print(\"Significant State Outliers (>= 3× median in >=2 metrics):\")\n", + " for outlier in outliers_by_state[:10]:\n", + " print(f\"- {outlier['Name']} (NPI: {outlier['NPI']})\")\n", + " print(f\" State: {outlier['State']} | Specialty: {outlier['Specialty']}\")\n", + " print(f\" Claims: {outlier['Total_Claims']:,}, Charges: {format_dollar_amount(outlier['Total_Charges'])}, Payments: {format_dollar_amount(outlier['Total_Payments'])}\\n\")\n", + " else:\n", + " print(\"No multi-metric outliers by state.\")\n", + " else:\n", + " print(\"No states with >=5 suppliers.\")\n", + "\n", + " print(\"\\n## Peer Group Analysis by Combined Specialty–State\\n\")\n", + " supplier_metrics['SpecState'] = supplier_metrics['Suplr_Prvdr_Spclty_Desc'].astype(str) + ' - ' + supplier_metrics['Suplr_Prvdr_State_Abrvtn'].astype(str)\n", + " combo_counts = supplier_metrics['SpecState'].value_counts()\n", + " valid_specstates = combo_counts[combo_counts >= 5].index\n", + "\n", + " if len(valid_specstates) > 0:\n", + " # Calculate medians for each group\n", + " combo_medians = supplier_metrics[supplier_metrics['SpecState'].isin(valid_specstates)].groupby('SpecState').agg({\n", + " 'Suplr_Sbmtd_Chrgs': 'median',\n", + " 'Suplr_Mdcr_Pymt_Amt': 'median',\n", + " 'Tot_Suplr_Clms': 'median',\n", + " 'Tot_Suplr_Srvcs': 'median'\n", " })\n", - " except (IndexError, KeyError) as e:\n", - " print(\n", - " f\"Error processing supplier {npi} with outlier+ratio indicators: {str(e)}\")\n", - " continue\n", - "\n", - " # Convert to DataFrame\n", - " combined_df = pd.DataFrame(suspicious_suppliers)\n", - "\n", - " # If no suppliers found with multiple indicators, return empty\n", - " if combined_df.empty:\n", - " print(\"No suppliers found with multiple fraud indicators.\")\n", - " fig, ax = plt.subplots(figsize=(15, 12))\n", - " ax.text(0.5, 0.5, \"No suppliers identified with multiple fraud indicators\",\n", - " ha='center', va='center', fontsize=14)\n", - " return fig, combined_df\n", - "\n", - " # Sort by number of indicators (descending), then by growth rate\n", - " combined_df = combined_df.sort_values(\n", - " ['Indicators', 'Growth Rate (%)'], ascending=[False, False])\n", - "\n", - " # Take top N suppliers\n", - " plot_df = combined_df.head(top_n)\n", - "\n", - " # Create figure\n", - " fig, ax = plt.subplots(figsize=(15, 12))\n", - "\n", - " # Plot horizontal bar chart, colored by number of indicators\n", - " scatter = ax.scatter(\n", - " plot_df['Growth Rate (%)'],\n", - " range(len(plot_df)),\n", - " c=plot_df['Indicators'],\n", - " cmap='RdYlBu_r',\n", - " s=300,\n", - " alpha=0.7\n", - " )\n", - "\n", - " # Set y-tick labels to supplier names\n", - " ax.set_yticks(range(len(plot_df)))\n", - " ax.set_yticklabels(plot_df['Supplier Name'])\n", - "\n", - " # Create a colorbar\n", - " cbar = plt.colorbar(scatter)\n", - " cbar.set_label('Number of Fraud Indicators', rotation=270, labelpad=20)\n", - "\n", - " # Add text labels with flag information\n", - " for i, (_, row) in enumerate(plot_df.iterrows()):\n", - " ax.text(\n", - " row['Growth Rate (%)'] + 5,\n", - " i,\n", - " row['Flags'],\n", - " va='center',\n", - " fontsize=9\n", - " )\n", - "\n", - " # Set labels and title\n", - " ax.set_xlabel('Growth Rate (%)', fontsize=14)\n", - " ax.set_ylabel('Supplier', fontsize=14)\n", - " ax.set_title('Suppliers with Multiple Fraud Indicators',\n", - " fontsize=16, fontweight='bold')\n", - "\n", - " # Add grid\n", - " ax.grid(axis='x', linestyle='--', alpha=0.7)\n", - "\n", - " plt.tight_layout()\n", - " return fig, combined_df\n", - "\n", - "\n", - "def create_fraud_detection_visualizations(df_by_year):\n", - " \"\"\"\n", - " Create all fraud detection visualizations for a Jupyter notebook.\n", - "\n", - " Parameters:\n", - " -----------\n", - " df_by_year : dict\n", - " Dictionary with yearly dataframes\n", - "\n", - " Returns:\n", - " --------\n", - " visualizations : dict\n", - " Dictionary with all visualizations\n", - " data : dict\n", - " Dictionary with all data DataFrames\n", - " \"\"\"\n", - " # Initialize results dictionaries\n", - " visualizations = {}\n", - " data = {}\n", - "\n", - " if not df_by_year:\n", - " print(\"Error: No data provided. Cannot create visualizations.\")\n", - " return {}, {}\n", - "\n", - " # Most recent year\n", - " recent_year = max(df_by_year.keys())\n", - "\n", - " # Display column names for debugging\n", - " print(\"\\nColumn names available in the most recent year's data:\")\n", - " for i, col in enumerate(sorted(df_by_year[recent_year].columns)):\n", - " print(f\" {i+1}. {col}\")\n", - "\n", - " # 1. Detect high growth suppliers\n", - " print(\"\\nDetecting high growth suppliers...\")\n", - " growth_df = detect_high_growth_suppliers(df_by_year, top_n=50)\n", - " data['high_growth_suppliers'] = growth_df\n", - "\n", - " if not growth_df.empty:\n", - " # Create high growth visualization\n", - " growth_fig = plot_high_growth_suppliers(growth_df, top_n=20)\n", - " visualizations['high_growth_suppliers'] = growth_fig\n", - " print(\"✓ High growth suppliers visualization created successfully.\")\n", - " else:\n", - " # Create empty plot\n", - " fig, ax = plt.subplots(figsize=(14, 10))\n", - " ax.text(0.5, 0.5, \"No high growth suppliers identified\",\n", - " ha='center', va='center', fontsize=14)\n", - " visualizations['high_growth_suppliers'] = fig\n", - " print(\"⚠ No high growth suppliers identified.\")\n", - "\n", - " # 2. Detect geographic fraud hotspots\n", - " print(\"\\nDetecting geographic fraud hotspots...\")\n", - " if not growth_df.empty:\n", - " hotspots_fig = plot_geographic_fraud_hotspots(df_by_year, growth_df)\n", - " visualizations['geographic_hotspots'] = hotspots_fig\n", - " print(\"✓ Geographic hotspots visualization created successfully.\")\n", - " else:\n", - " # Create empty plot\n", - " fig, ax = plt.subplots(figsize=(14, 10))\n", - " ax.text(0.5, 0.5, \"No geographic hotspots identified (no high growth suppliers)\",\n", - " ha='center', va='center', fontsize=14)\n", - " visualizations['geographic_hotspots'] = fig\n", - " print(\"⚠ No geographic hotspots identified (no high growth suppliers).\")\n", - "\n", - " # 3. Detect outlier claim amounts\n", - " print(\"\\nDetecting outlier claim amounts...\")\n", - " outlier_df = detect_outlier_claim_amounts(\n", - " df_by_year[recent_year], recent_year)\n", - " data['outlier_claims'] = outlier_df\n", - "\n", - " if not outlier_df.empty:\n", - " outlier_fig = plot_outlier_claim_patterns(outlier_df)\n", - " visualizations['outlier_claims'] = outlier_fig\n", - " print(\"✓ Outlier claim patterns visualization created successfully.\")\n", - " else:\n", - " # Create empty plot\n", - " fig, ax = plt.subplots(figsize=(14, 10))\n", - " ax.text(0.5, 0.5, \"No outlier claim amounts identified\",\n", - " ha='center', va='center', fontsize=14)\n", - " visualizations['outlier_claims'] = fig\n", - " print(\"⚠ No outlier claim amounts identified.\")\n", - "\n", - " # 4. Detect unusual beneficiary-to-claim ratios\n", - " print(\"\\nDetecting unusual beneficiary-to-claim ratios...\")\n", - " ratio_df = detect_unusual_beneficiary_to_claim_ratio(\n", - " df_by_year[recent_year], recent_year)\n", - " data['unusual_ratios'] = ratio_df\n", - "\n", - " if not ratio_df.empty:\n", - " ratio_fig = plot_beneficiary_claim_ratio_outliers(ratio_df)\n", - " visualizations['unusual_ratios'] = ratio_fig\n", - " print(\"✓ Unusual beneficiary-to-claim ratios visualization created successfully.\")\n", - " else:\n", - " # Create empty plot\n", - " fig, ax = plt.subplots(figsize=(14, 10))\n", - " ax.text(0.5, 0.5, \"No unusual beneficiary-to-claim ratios identified\",\n", - " ha='center', va='center', fontsize=14)\n", - " visualizations['unusual_ratios'] = fig\n", - " print(\"⚠ No unusual beneficiary-to-claim ratios identified.\")\n", - "\n", - " # 5. Combined fraud indicators\n", - " print(\"\\nIdentifying suppliers with multiple fraud indicators...\")\n", - " combined_fig, combined_df = plot_combined_fraud_indicators(\n", - " growth_df, outlier_df, ratio_df)\n", - " visualizations['combined_indicators'] = combined_fig\n", - " data['combined_indicators'] = combined_df\n", - "\n", - " if not combined_df.empty:\n", - " print(\"✓ Combined fraud indicators visualization created successfully.\")\n", - " else:\n", - " print(\"⚠ No suppliers with multiple fraud indicators identified.\")\n", - "\n", - " return visualizations, data\n", - "\n", - "\n", - "def main():\n", - " \"\"\"Main function to import and analyze DME data for fraud detection.\"\"\"\n", - " print(\"DME Fraud Detection Analysis\")\n", - " print(\"===========================\\n\")\n", - "\n", - " # Dictionary to store dataframes by year\n", - " df_by_year = {}\n", - "\n", - " # Import data for years 2017-2022\n", - " for year in range(2017, 2023):\n", - " csv_path = f\"data/{year}/mup_dme_ry24_p05_v10_dy{str(year)[-2:]}_supr.csv\"\n", - " if os.path.exists(csv_path):\n", - " print(f\"Importing data for {year}...\")\n", - " try:\n", - " df = pd.read_csv(csv_path, low_memory=False)\n", - "\n", - " # Convert monetary columns to numeric\n", - " money_columns = [\n", - " col for col in df.columns if 'Pymt' in col or 'Amt' in col]\n", - " for col in money_columns:\n", - " if col in df.columns:\n", - " df[col] = pd.to_numeric(df[col], errors='coerce')\n", - "\n", - " df_by_year[year] = df\n", - " print(\n", - " f\"✓ Data for {year} imported successfully. Shape: {df.shape}\")\n", - " except Exception as e:\n", - " print(f\"Error importing data for {year}: {str(e)}\")\n", + " outliers_combined = []\n", + " \n", + " for cs in valid_specstates:\n", + " group = supplier_metrics[supplier_metrics['SpecState'] == cs]\n", + " med_clms = combo_medians.loc[cs, 'Tot_Suplr_Clms']\n", + " med_chrg = combo_medians.loc[cs, 'Suplr_Sbmtd_Chrgs']\n", + " med_pay = combo_medians.loc[cs, 'Suplr_Mdcr_Pymt_Amt']\n", + "\n", + " claim_outliers = group[group['Tot_Suplr_Clms'] > 3 * med_clms]\n", + " charge_outliers = group[group['Suplr_Sbmtd_Chrgs'] > 3 * med_chrg]\n", + " payment_outliers = group[group['Suplr_Mdcr_Pymt_Amt'] > 3 * med_pay]\n", + "\n", + " all_out = pd.concat([\n", + " claim_outliers[['Suplr_NPI']].assign(flag='claims'),\n", + " charge_outliers[['Suplr_NPI']].assign(flag='charges'),\n", + " payment_outliers[['Suplr_NPI']].assign(flag='payments')\n", + " ], ignore_index=True)\n", + " outlier_counts = all_out.groupby('Suplr_NPI').size()\n", + " multi_flags = outlier_counts[outlier_counts >= 2].index\n", + "\n", + " multi_outliers = group[group['Suplr_NPI'].isin(multi_flags)]\n", + " for idx, row in multi_outliers.iterrows():\n", + " outliers_combined.append({\n", + " 'NPI': row['Suplr_NPI'],\n", + " 'Name': row['Suplr_Prvdr_Last_Name_Org'],\n", + " 'SpecState': cs,\n", + " 'Specialty': row['Suplr_Prvdr_Spclty_Desc'],\n", + " 'State': row['Suplr_Prvdr_State_Abrvtn'],\n", + " 'Total_Claims': row['Tot_Suplr_Clms'],\n", + " 'Total_Charges': row['Suplr_Sbmtd_Chrgs'],\n", + " 'Total_Payments': row['Suplr_Mdcr_Pymt_Amt']\n", + " })\n", + " \n", + " if outliers_combined:\n", + " # Sort by total charges just as a quick way to highlight big outliers\n", + " outliers_combined = sorted(\n", + " outliers_combined,\n", + " key=lambda x: x['Total_Charges'],\n", + " reverse=True\n", + " )\n", + " print(\"Significant Combined Specialty–State Outliers (>= 3× median in >=2 metrics):\")\n", + " for outlier in outliers_combined[:10]:\n", + " print(f\"- {outlier['Name']} (NPI: {outlier['NPI']})\")\n", + " print(f\" Specialty: {outlier['Specialty']} | State: {outlier['State']}\")\n", + " print(f\" Claims: {outlier['Total_Claims']:,}, Charges: {format_dollar_amount(outlier['Total_Charges'])}, Payments: {format_dollar_amount(outlier['Total_Payments'])}\\n\")\n", + " else:\n", + " print(\"No multi-metric outliers at the combined specialty–state level.\")\n", " else:\n", - " print(f\"Warning: No data file found for {year}\")\n", - "\n", - " if not df_by_year:\n", - " print(\"\\nError: No data files were successfully imported. Cannot proceed with analysis.\")\n", - " return {}, {}, {}\n", - "\n", - " print(f\"\\n{len(df_by_year)} year(s) of data imported.\")\n", - "\n", - " # Print column names from the first year to help diagnose column issues\n", - " first_year = min(df_by_year.keys())\n", - " print(f\"\\nSample column names from {first_year} data:\")\n", - " # Show first 20 columns\n", - " for i, col in enumerate(sorted(df_by_year[first_year].columns)[:20]):\n", - " print(f\" {i+1}. {col}\")\n", - "\n", - " if len(df_by_year[first_year].columns) > 20:\n", - " print(\n", - " f\" ... and {len(df_by_year[first_year].columns) - 20} more columns\")\n", - "\n", - " # ----- FRAUD DETECTION ANALYSIS -----\n", - " print(\"\\n1. High Growth Rate Analysis\")\n", - " print(\"--------------------------\\n\")\n", - "\n", - " # Detect suppliers with abnormally high growth rates\n", - " growth_df = detect_high_growth_suppliers(df_by_year, top_n=50)\n", - "\n", - " if growth_df.empty:\n", - " print(\"No suppliers with high growth rates detected. Cannot proceed with this part of the analysis.\")\n", - " else:\n", - " # Print summary of top 15 high-growth suppliers\n", - " print(\"Top 15 suppliers with highest growth rates:\")\n", - "\n", - " # Format the output for display\n", - " formatted_growth_df = growth_df.head(15).copy()\n", - " formatted_growth_df['Growth Rate (%)'] = formatted_growth_df['Growth Rate (%)'].apply(\n", - " lambda x: f\"{x:.2f}%\")\n", - " formatted_growth_df['Start Year Value'] = formatted_growth_df['Start Year Value'].apply(\n", - " lambda x: f\"${x:,.2f}\")\n", - " formatted_growth_df['End Year Value'] = formatted_growth_df['End Year Value'].apply(\n", - " lambda x: f\"${x:,.2f}\")\n", - " formatted_growth_df['Absolute Growth'] = formatted_growth_df['Absolute Growth'].apply(\n", - " lambda x: f\"${x:,.2f}\")\n", - "\n", - " print(formatted_growth_df.to_string(index=False))\n", - "\n", - " # ----- GEOGRAPHIC ANALYSIS -----\n", - " print(\"\\n2. Geographic Fraud Hotspots\")\n", - " print(\"-------------------------\\n\")\n", - "\n", - " # Group high growth suppliers by state\n", - " high_growth_states = growth_df.groupby('Supplier State').agg({\n", - " 'Supplier NPI': 'nunique',\n", - " 'Growth Rate (%)': 'mean',\n", - " 'Absolute Growth': 'sum'\n", - " }).reset_index()\n", - "\n", - " high_growth_states.columns = [\n", - " 'State', 'Suspicious Suppliers', 'Average Growth Rate (%)', 'Total Growth ($)']\n", - " high_growth_states = high_growth_states.sort_values(\n", - " 'Suspicious Suppliers', ascending=False)\n", - "\n", - " print(\"States with highest number of suspicious suppliers:\")\n", - "\n", - " # Format for display\n", - " formatted_states = high_growth_states.head(10).copy()\n", - " formatted_states['Average Growth Rate (%)'] = formatted_states['Average Growth Rate (%)'].apply(\n", - " lambda x: f\"{x:.2f}%\")\n", - " formatted_states['Total Growth ($)'] = formatted_states['Total Growth ($)'].apply(\n", - " lambda x: f\"${x:,.2f}\")\n", - "\n", - " print(formatted_states.to_string(index=False))\n", - "\n", - " # ----- OUTLIER CLAIM ANALYSIS -----\n", - " print(\"\\n3. Outlier Claim Amount Analysis\")\n", - " print(\"-----------------------------\\n\")\n", - "\n", - " # Most recent year\n", - " recent_year = max(df_by_year.keys())\n", - "\n", - " # Detect suppliers with outlier claim amounts\n", - " outlier_df = detect_outlier_claim_amounts(\n", - " df_by_year[recent_year], recent_year)\n", - "\n", - " if outlier_df.empty:\n", - " print(\n", - " f\"No suppliers with outlier claim amounts detected in {recent_year}.\")\n", - " else:\n", - " print(f\"Top 10 suppliers with outlier claim amounts in {recent_year}:\")\n", - "\n", - " # Format for display\n", - " try:\n", - " formatted_outliers = outlier_df.head(10).copy()\n", - "\n", - " # Use the metric column name (4th column)\n", - " metric_col = outlier_df.columns[3]\n", - " zscore_col = outlier_df.columns[4]\n", - "\n", - " # Format monetary values with dollar signs\n", - " if 'Pymt' in metric_col or 'Chrg' in metric_col or 'Amt' in metric_col:\n", - " formatted_outliers[metric_col] = formatted_outliers[metric_col].apply(\n", - " lambda x: f\"${x:,.2f}\")\n", - "\n", - " formatted_outliers[zscore_col] = formatted_outliers[zscore_col].apply(\n", - " lambda x: f\"{x:.2f}\")\n", - "\n", - " print(formatted_outliers.to_string(index=False))\n", - " except Exception as e:\n", - " print(f\"Error formatting outlier data: {str(e)}\")\n", - " print(\"Raw data:\")\n", - " print(outlier_df.head(10))\n", - "\n", - " # ----- BENEFICIARY-CLAIM RATIO ANALYSIS -----\n", - " print(\"\\n4. Unusual Beneficiary-to-Claim Ratio Analysis\")\n", - " print(\"-----------------------------------------\\n\")\n", - "\n", - " # Detect suppliers with unusual beneficiary-to-claim ratios\n", - " ratio_df = detect_unusual_beneficiary_to_claim_ratio(\n", - " df_by_year[recent_year], recent_year)\n", - "\n", - " if ratio_df.empty:\n", - " print(\n", - " f\"No suppliers with unusual claims per beneficiary detected in {recent_year}.\")\n", - " else:\n", - " print(\n", - " f\"Top 10 suppliers with unusual claims per beneficiary in {recent_year}:\")\n", - "\n", - " # Format for display\n", - " try:\n", - " formatted_ratios = ratio_df.head(10).copy()\n", - " formatted_ratios['Claims_Per_Beneficiary'] = formatted_ratios['Claims_Per_Beneficiary'].apply(\n", - " lambda x: f\"{x:.2f}\")\n", - " formatted_ratios['Claims_Per_Beneficiary_zscore'] = formatted_ratios['Claims_Per_Beneficiary_zscore'].apply(\n", - " lambda x: f\"{x:.2f}\")\n", - "\n", - " print(formatted_ratios.to_string(index=False))\n", - " except Exception as e:\n", - " print(f\"Error formatting ratio data: {str(e)}\")\n", - " print(\"Raw data:\")\n", - " print(ratio_df.head(10))\n", - "\n", - " # ----- COMBINED FRAUD INDICATORS -----\n", - " print(\"\\n5. Combined Fraud Indicators\")\n", - " print(\"-------------------------\\n\")\n", - "\n", - " # Identify suppliers that appear in multiple fraud indicator lists\n", - " combined_fig, combined_df = plot_combined_fraud_indicators(\n", - " growth_df, outlier_df, ratio_df)\n", - "\n", - " if combined_df.empty:\n", - " print(\"No suppliers with multiple fraud indicators identified.\")\n", - " else:\n", - " print(\"Top 10 suppliers with multiple fraud indicators:\")\n", - "\n", - " # Format for display\n", - " try:\n", - " formatted_combined = combined_df.head(10).copy()\n", - " formatted_combined['Growth Rate (%)'] = formatted_combined['Growth Rate (%)'].apply(\n", - " lambda x: f\"{x:.2f}%\" if x > 0 else \"N/A\")\n", - " formatted_combined['Claims Per Beneficiary'] = formatted_combined['Claims Per Beneficiary'].apply(\n", - " lambda x: f\"{x:.2f}\" if x > 0 else \"N/A\")\n", - " formatted_combined['Avg Charge'] = formatted_combined['Avg Charge'].apply(\n", - " lambda x: f\"${x:,.2f}\" if x > 0 else \"N/A\")\n", - "\n", - " print(formatted_combined.to_string(index=False))\n", - " except Exception as e:\n", - " print(f\"Error formatting combined data: {str(e)}\")\n", - " print(\"Raw data:\")\n", - " print(combined_df.head(10))\n", - "\n", - " # ----- VISUALIZATIONS -----\n", - " print(\"\\n\\n6. Generating Fraud Detection Visualizations\")\n", - " print(\"------------------------------------------\\n\")\n", - "\n", - " # Setting plot style\n", - " sns.set_style('whitegrid')\n", - " plt.rcParams['figure.figsize'] = [14, 9]\n", - "\n", - " # Generate all visualizations\n", - " visualizations, data = create_fraud_detection_visualizations(df_by_year)\n", - "\n", - " # Save visualizations to files if not in a notebook environment\n", - " try:\n", - " # Check if we're in a notebook environment\n", - " if 'ipykernel' not in sys.modules:\n", - " print(\"\\nSaving visualizations to files...\")\n", - " os.makedirs('fraud_visualizations', exist_ok=True)\n", - " for name, fig in visualizations.items():\n", - " fig.savefig(\n", - " f'fraud_visualizations/{name}.png', dpi=300, bbox_inches='tight')\n", - " print(f\"Saved: fraud_visualizations/{name}.png\")\n", - " except Exception as e:\n", - " print(f\"Error saving visualizations: {str(e)}\")\n", - " print(\"Note: Visualizations will be displayed if run in a Jupyter notebook\")\n", - "\n", - " # When run in Jupyter, the figures will be displayed inline\n", - " return df_by_year, visualizations, data\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " main()\n" + " print(\"No combined specialty–state groups with >=5 suppliers.\")" ] } ], diff --git a/dme_analysis/__init__.py b/dme_analysis/__init__.py deleted file mode 100644 index 28843f9..0000000 --- a/dme_analysis/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -""" -DME Analysis -=========== - -This package contains tools for analyzing Medicare DME data. -""" - -__version__ = "0.1.0" diff --git a/dme_analysis/utils/__init__.py b/dme_analysis/utils/__init__.py deleted file mode 100644 index 62b2f88..0000000 --- a/dme_analysis/utils/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -""" -DME Analysis Utilities -===================== - -This package contains utilities for DME data analysis. -""" - -from .data_dictionary import DATA_DICTIONARY, get_column_category -from .data_import import import_dme_data, get_column_mapping, import_data_for_years - -__all__ = [ - 'DATA_DICTIONARY', - 'get_column_category', - 'import_dme_data', - 'get_column_mapping', - 'import_data_for_years' -] diff --git a/dme_analysis/utils/data_dictionary.py b/dme_analysis/utils/data_dictionary.py deleted file mode 100644 index a601e37..0000000 --- a/dme_analysis/utils/data_dictionary.py +++ /dev/null @@ -1,153 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -DME Data Dictionary -This module contains the data dictionary for the DME dataset. -""" - -# Data dictionary mapping variable names to their descriptions -DATA_DICTIONARY = { - # Supplier Information - 'Suplr_NPI': "National Provider Identifier for the DME supplier", - 'Suplr_Prvdr_Last_Name_Org': "Organization name of the DME supplier", - 'Suplr_Prvdr_First_Name': "First name of the DME supplier (if individual)", - 'Suplr_Prvdr_MI': "Middle initial of the DME supplier (if individual)", - 'Suplr_Prvdr_Crdntls': "Credentials of the DME supplier", - 'Suplr_Prvdr_Gndr': "Gender of the DME supplier (if individual)", - 'Suplr_Prvdr_Ent_Cd': "Entity code of the DME supplier", - 'Suplr_Prvdr_St1': "Street address line 1 of the DME supplier", - 'Suplr_Prvdr_St2': "Street address line 2 of the DME supplier", - 'Suplr_Prvdr_City': "City where the DME supplier is located", - 'Suplr_Prvdr_State_Abrvtn': "State abbreviation where the DME supplier is located", - 'Suplr_Prvdr_State_FIPS': "FIPS code for the state where the DME supplier is located", - 'Suplr_Prvdr_Zip5': "5-digit ZIP code where the DME supplier is located", - 'Suplr_Prvdr_Cntry': "Country where the DME supplier is located", - 'Suplr_Prvdr_RUCA': "Rural-Urban Commuting Area code for the DME supplier", - 'Suplr_Prvdr_RUCA_Desc': "Description of the Rural-Urban Commuting Area for the DME supplier", - 'Suplr_Prvdr_Spclty_Desc': "Specialty description of the DME supplier", - 'Suplr_Prvdr_Spclty_Srce': "Source of the specialty information", - - # DME-specific fields - 'DME_Sprsn_Ind': "Indicator for suppression of DME data (Y/N)", - 'DME_Tot_Suplr_Benes': "Total number of beneficiaries served by the supplier for DME", - 'DME_Tot_Suplr_Clms': "Total number of claims submitted by the supplier for DME", - 'DME_Tot_Suplr_Srvcs': "Total number of services provided by the supplier for DME", - 'DME_Tot_Suplr_HCPCS_Cds': "Total number of unique HCPCS codes billed by the supplier for DME", - 'DME_Suplr_Sbmtd_Chrgs': "Total submitted charges by the supplier for DME", - 'DME_Suplr_Mdcr_Alowd_Amt': "Total Medicare allowed amount for the supplier for DME", - 'DME_Suplr_Mdcr_Pymt_Amt': "Total Medicare payment amount to the supplier for DME", - 'DME_Suplr_Mdcr_Stdzd_Pymt_Amt': "Total Medicare standardized payment amount to the supplier for DME", - - # Prosthetic and Orthotic fields - 'POS_Sprsn_Ind': "Indicator for suppression of POS data (Y/N)", - 'POS_Tot_Suplr_Benes': "Total number of beneficiaries served by the supplier for POS", - 'POS_Tot_Suplr_Clms': "Total number of claims submitted by the supplier for POS", - 'POS_Tot_Suplr_Srvcs': "Total number of services provided by the supplier for POS", - 'POS_Tot_Suplr_HCPCS_Cds': "Total number of unique HCPCS codes billed by the supplier for POS", - 'POS_Suplr_Sbmtd_Chrgs': "Total submitted charges by the supplier for POS", - 'POS_Suplr_Mdcr_Alowd_Amt': "Total Medicare allowed amount for the supplier for POS", - 'POS_Suplr_Mdcr_Pymt_Amt': "Total Medicare payment amount to the supplier for POS", - 'POS_Suplr_Mdcr_Stdzd_Pymt_Amt': "Total Medicare standardized payment amount to the supplier for POS", - - # Drug and Nutritional fields - 'Drug_Sprsn_Ind': "Indicator for suppression of Drug data (Y/N)", - 'Drug_Tot_Suplr_Benes': "Total number of beneficiaries served by the supplier for Drug", - 'Drug_Tot_Suplr_Clms': "Total number of claims submitted by the supplier for Drug", - 'Drug_Tot_Suplr_Srvcs': "Total number of services provided by the supplier for Drug", - 'Drug_Tot_Suplr_HCPCS_Cds': "Total number of unique HCPCS codes billed by the supplier for Drug", - 'Drug_Suplr_Sbmtd_Chrgs': "Total submitted charges by the supplier for Drug", - 'Drug_Suplr_Mdcr_Alowd_Amt': "Total Medicare allowed amount for the supplier for Drug", - 'Drug_Suplr_Mdcr_Pymt_Amt': "Total Medicare payment amount to the supplier for Drug", - 'Drug_Suplr_Mdcr_Stdzd_Pymt_Amt': "Total Medicare standardized payment amount to the supplier for Drug", - - # Overall supplier fields - 'Tot_Suplr_Benes': "Total number of beneficiaries served by the supplier overall", - 'Tot_Suplr_Clms': "Total number of claims submitted by the supplier overall", - 'Tot_Suplr_Srvcs': "Total number of services provided by the supplier overall", - 'Tot_Suplr_HCPCS_Cds': "Total number of unique HCPCS codes billed by the supplier overall", - 'Suplr_Sbmtd_Chrgs': "Total submitted charges by the supplier overall", - 'Suplr_Mdcr_Alowd_Amt': "Total Medicare allowed amount for the supplier overall", - 'Suplr_Mdcr_Pymt_Amt': "Total Medicare payment amount to the supplier overall", - 'Suplr_Mdcr_Stdzd_Pymt_Amt': "Total Medicare standardized payment amount to the supplier overall", - - # Beneficiary Demographics - 'Bene_Avg_Age': "Average age of beneficiaries served by this supplier", - 'Bene_Age_LT_65_Cnt': "Count of beneficiaries under 65 years of age", - 'Bene_Age_65_74_Cnt': "Count of beneficiaries 65-74 years of age", - 'Bene_Age_75_84_Cnt': "Count of beneficiaries 75-84 years of age", - 'Bene_Age_GT_84_Cnt': "Count of beneficiaries greater than 84 years of age", - 'Bene_Male_Cnt': "Count of male beneficiaries", - 'Bene_Feml_Cnt': "Count of female beneficiaries", - 'Bene_Race_Wht_Cnt': "Count of white beneficiaries", - 'Bene_Race_Black_Cnt': "Count of Black or African American beneficiaries", - 'Bene_Race_Api_Cnt': "Count of Asian/Pacific Islander beneficiaries", - 'Bene_Race_Hspnc_Cnt': "Count of Hispanic beneficiaries", - 'Bene_Race_Natind_Cnt': "Count of Native American/Alaska Native beneficiaries", - 'Bene_Race_Othr_Cnt': "Count of beneficiaries of other races", - 'Bene_Dual_Cnt': "Count of dual-eligible beneficiaries (Medicare and Medicaid)", - 'Bene_Ndual_Cnt': "Count of non-dual-eligible beneficiaries", - 'Bene_Avg_Risk_Scre': "Average risk score of beneficiaries", - - # Health Conditions - 'Bene_CC_PH_Hypertension_V2_Pct': "Percentage of beneficiaries with hypertension", - 'Bene_CC_PH_Hyperlipidemia_V2_Pct': "Percentage of beneficiaries with hyperlipidemia", - 'Bene_CC_PH_Diabetes_V2_Pct': "Percentage of beneficiaries with diabetes", - 'Bene_CC_PH_Arthritis_V2_Pct': "Percentage of beneficiaries with arthritis", - 'Bene_CC_PH_IschemicHeart_V2_Pct': "Percentage of beneficiaries with ischemic heart disease", - 'Bene_CC_PH_COPD_V2_Pct': "Percentage of beneficiaries with COPD", - 'Bene_CC_PH_CKD_V2_Pct': "Percentage of beneficiaries with chronic kidney disease", - 'Bene_CC_PH_Cancer6_V2_Pct': "Percentage of beneficiaries with cancer", - 'Bene_CC_PH_Asthma_V2_Pct': "Percentage of beneficiaries with asthma", - 'Bene_CC_PH_Afib_V2_Pct': "Percentage of beneficiaries with atrial fibrillation", - 'Bene_CC_PH_HF_NonIHD_V2_Pct': "Percentage of beneficiaries with heart failure", - 'Bene_CC_PH_Stroke_TIA_V2_Pct': "Percentage of beneficiaries with stroke/TIA", - 'Bene_CC_PH_Osteoporosis_V2_Pct': "Percentage of beneficiaries with osteoporosis", - 'Bene_CC_PH_Parkinson_V2_Pct': "Percentage of beneficiaries with Parkinson's disease", - 'Bene_CC_BH_Mood_V2_Pct': "Percentage of beneficiaries with mood disorders", - 'Bene_CC_BH_Depress_V1_Pct': "Percentage of beneficiaries with depression", - 'Bene_CC_BH_Anxiety_V1_Pct': "Percentage of beneficiaries with anxiety", - 'Bene_CC_BH_Tobacco_V1_Pct': "Percentage of beneficiaries with tobacco use disorder", - 'Bene_CC_BH_Alz_NonAlzdem_V2_Pct': "Percentage of beneficiaries with Alzheimer's/dementia", - 'Bene_CC_BH_Schizo_OthPsy_V1_Pct': "Percentage of beneficiaries with schizophrenia or other psychotic disorders", - 'Bene_CC_BH_Alcohol_Drug_V1_Pct': "Percentage of beneficiaries with alcohol/drug use disorders", - 'Bene_CC_BH_ADHD_OthCD_V1_Pct': "Percentage of beneficiaries with ADHD", - 'Bene_CC_BH_Bipolar_V1_Pct': "Percentage of beneficiaries with bipolar disorder", - 'Bene_CC_BH_PD_V1_Pct': "Percentage of beneficiaries with personality disorders", - 'Bene_CC_BH_PTSD_V1_Pct': "Percentage of beneficiaries with PTSD" -} - - -def get_column_category(column_name): - """ - Determine the category of a column based on its name. - - Parameters: - ----------- - column_name : str - The name of the column - - Returns: - -------- - category : str - The category of the column - """ - if column_name.startswith('Suplr_Prvdr_'): - return 'Supplier Information' - elif column_name.startswith('Suplr_') and not column_name.startswith('Suplr_Prvdr_'): - return 'Overall Supplier Metrics' - elif column_name.startswith('DME_'): - return 'Durable Medical Equipment' - elif column_name.startswith('POS_'): - return 'Prosthetics and Orthotics' - elif column_name.startswith('Drug_'): - return 'Drug and Nutritional Products' - elif column_name.startswith('Bene_'): - if any(x in column_name for x in ['_CC_', 'Risk']): - return 'Health Conditions' - else: - return 'Beneficiary Demographics' - elif column_name.startswith('Tot_'): - return 'Overall Provider Metrics' - else: - return 'Other' diff --git a/dme_analysis/utils/data_import.py b/dme_analysis/utils/data_import.py deleted file mode 100644 index 46f48fb..0000000 --- a/dme_analysis/utils/data_import.py +++ /dev/null @@ -1,155 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -DME Data Import Utilities -This module contains functions for importing DME data. -""" - -import pandas as pd -import numpy as np -import os -from collections import defaultdict - - -def import_dme_data(file_path): - """ - Import and preprocess DME data from a CSV file. - - Parameters: - ----------- - file_path : str - Path to the CSV file containing DME data - - Returns: - -------- - df : DataFrame - Processed DataFrame containing DME data - """ - print(f"Importing data from {file_path}...") - - try: - # Import data with appropriate dtypes to handle monetary values correctly - df = pd.read_csv(file_path, low_memory=False) - - # Convert monetary columns to numeric - money_columns = [col for col in df.columns if any( - x in col for x in ['Pymt', 'Amt', 'Chrgs'])] - for col in money_columns: - if col in df.columns: - df[col] = pd.to_numeric(df[col], errors='coerce') - - print(f"Successfully imported data with shape: {df.shape}") - return df - - except Exception as e: - print(f"Error importing data: {str(e)}") - return None - - -def get_column_mapping(df): - """ - Get a mapping of expected column names to actual column names in the DataFrame. - This helps handle variations in column names across different datasets. - - Parameters: - ----------- - df : DataFrame - DataFrame to inspect for column names - - Returns: - -------- - column_map : dict - Dictionary mapping expected column names to actual column names - """ - column_map = {} - - # Map for supplier organization name - if 'Suplr_Prvdr_Last_Name_Org' in df.columns: - column_map['supplier_name'] = 'Suplr_Prvdr_Last_Name_Org' - elif 'Suplr_Prvdr_Org_Name' in df.columns: - column_map['supplier_name'] = 'Suplr_Prvdr_Org_Name' - elif 'Suplr_Name' in df.columns: - column_map['supplier_name'] = 'Suplr_Name' - elif 'Supplier_Name' in df.columns: - column_map['supplier_name'] = 'Supplier_Name' - else: - # If no suitable column exists, create a placeholder - print("Warning: No supplier name column found. Using placeholder names.") - column_map['supplier_name'] = None - - # Map for supplier state - if 'Suplr_Prvdr_State_Abrvtn' in df.columns: - column_map['supplier_state'] = 'Suplr_Prvdr_State_Abrvtn' - elif 'Suplr_State' in df.columns: - column_map['supplier_state'] = 'Suplr_State' - elif 'State' in df.columns: - column_map['supplier_state'] = 'State' - else: - print("Warning: No supplier state column found. Using placeholder.") - column_map['supplier_state'] = None - - # Map for supplier NPI - if 'Suplr_NPI' in df.columns: - column_map['supplier_npi'] = 'Suplr_NPI' - elif 'NPI' in df.columns: - column_map['supplier_npi'] = 'NPI' - elif 'Provider_NPI' in df.columns: - column_map['supplier_npi'] = 'Provider_NPI' - else: - print("Warning: No NPI column found. Using index as placeholder.") - column_map['supplier_npi'] = None - - # Check if key columns are missing - if None in column_map.values(): - print("\nAvailable columns in the dataset:") - # Show first 20 columns - for i, col in enumerate(sorted(df.columns)[:20]): - print(f" {i+1}. {col}") - - if len(df.columns) > 20: - print(f" ... and {len(df.columns) - 20} more columns") - - return column_map - - -def import_data_for_years(years_range, base_path="data"): - """ - Import data for multiple years. - - Parameters: - ----------- - years_range : range or list - Range or list of years to import (e.g., range(2017, 2023)) - base_path : str - Base path where data files are stored - - Returns: - -------- - df_by_year : dict - Dictionary with years as keys and DataFrames as values - """ - df_by_year = {} - - for year in years_range: - # Try different file name patterns - file_patterns = [ - f"{base_path}/{year}/mup_dme_ry24_p05_v10_dy{str(year)[-2:]}_supr.csv", - f"{base_path}/dme_data_{year}.csv", - f"{base_path}/{year}/dme_data_{year}.csv", - f"dme_data_{year}.csv" - ] - - file_found = False - for file_path in file_patterns: - if os.path.exists(file_path): - df = import_dme_data(file_path) - if df is not None: - df_by_year[year] = df - file_found = True - break - - if not file_found: - print(f"Warning: No data file found for {year}") - - return df_by_year diff --git a/dme_data_analysis.py b/dme_data_analysis.py deleted file mode 100644 index c17bce4..0000000 --- a/dme_data_analysis.py +++ /dev/null @@ -1,781 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -DME Data Analysis Script -This script imports and analyzes the DME data files by year. -""" - -import pandas as pd -import numpy as np -import os -from pprint import pprint -from collections import defaultdict, Counter -import matplotlib.pyplot as plt -import seaborn as sns -import sys - -# Import from the new module structure -from dme_analysis.utils import ( - DATA_DICTIONARY, - get_column_category, - import_data_for_years -) - - -def get_top_suppliers(df, top_n=10): - """Return the top suppliers by number of beneficiaries.""" - top_suppliers = df.sort_values( - 'DME_Tot_Suplr_Benes', ascending=False).head(top_n) - - # Format results for better readability - results = [] - for _, row in top_suppliers.iterrows(): - supplier_name = row['Suplr_Prvdr_Last_Name_Org'] - beneficiaries = row['DME_Tot_Suplr_Benes'] - claims = row['DME_Tot_Suplr_Clms'] - payments = row['DME_Suplr_Mdcr_Pymt_Amt'] - - results.append({ - 'Supplier': supplier_name, - 'Beneficiaries': beneficiaries, - 'Claims': claims, - 'Medicare Payments': f"${payments:,.2f}" - }) - - return pd.DataFrame(results) - - -def get_beneficiary_demographics(df): - """Analyze beneficiary demographics from the data.""" - # Extract age distribution - age_cols = ['Bene_Age_LT_65_Cnt', 'Bene_Age_65_74_Cnt', - 'Bene_Age_75_84_Cnt', 'Bene_Age_GT_84_Cnt'] - age_totals = df[age_cols].sum() - total_benes = age_totals.sum() - age_pcts = (age_totals / total_benes * 100).round(2) - - # Extract gender distribution - gender_cols = ['Bene_Feml_Cnt', 'Bene_Male_Cnt'] - gender_totals = df[gender_cols].sum() - gender_pcts = (gender_totals / gender_totals.sum() * 100).round(2) - - # Extract race distribution - race_cols = ['Bene_Race_Wht_Cnt', 'Bene_Race_Black_Cnt', 'Bene_Race_Api_Cnt', - 'Bene_Race_Hspnc_Cnt', 'Bene_Race_Natind_Cnt', 'Bene_Race_Othr_Cnt'] - race_totals = df[race_cols].sum() - race_pcts = (race_totals / race_totals.sum() * 100).round(2) - - # Format results with readable labels from data dictionary - age_results = {DATA_DICTIONARY[col].split( - ' - ')[0]: pct for col, pct in zip(age_cols, age_pcts)} - gender_results = {DATA_DICTIONARY[col].split( - ' - ')[0]: pct for col, pct in zip(gender_cols, gender_pcts)} - race_results = {DATA_DICTIONARY[col].split( - ' - ')[0]: pct for col, pct in zip(race_cols, race_pcts)} - - return { - 'Age Distribution': age_results, - 'Gender Distribution': gender_results, - 'Race Distribution': race_results - } - - -def get_common_health_conditions(df): - """Extract the most common health conditions among beneficiaries.""" - # Physical health conditions - ph_cols = [col for col in df.columns if col.startswith( - 'Bene_CC_PH_') and col.endswith('_Pct')] - ph_values = [] - - for col in ph_cols: - # Calculate weighted average (weighted by number of beneficiaries) - weighted_avg = (df[col] * df['DME_Tot_Suplr_Benes'] - ).sum() / df['DME_Tot_Suplr_Benes'].sum() - ph_values.append((DATA_DICTIONARY[col], weighted_avg)) - - # Behavioral health conditions - bh_cols = [col for col in df.columns if col.startswith( - 'Bene_CC_BH_') and col.endswith('_Pct')] - bh_values = [] - - for col in bh_cols: - # Calculate weighted average (weighted by number of beneficiaries) - weighted_avg = (df[col] * df['DME_Tot_Suplr_Benes'] - ).sum() / df['DME_Tot_Suplr_Benes'].sum() - bh_values.append((DATA_DICTIONARY[col], weighted_avg)) - - # Sort by prevalence - ph_values.sort(key=lambda x: x[1], reverse=True) - bh_values.sort(key=lambda x: x[1], reverse=True) - - return { - 'Physical Health Conditions': ph_values, - 'Behavioral Health Conditions': bh_values - } - - -def analyze_spending_patterns(df_by_year): - """Analyze spending patterns across years.""" - year_data = [] - - for year, df in df_by_year.items(): - # Calculate total beneficiaries and spending - total_benes = df['DME_Tot_Suplr_Benes'].sum() - total_spend = df['DME_Suplr_Mdcr_Pymt_Amt'].sum() - - # Calculate spending per beneficiary - spend_per_bene = total_spend / total_benes if total_benes > 0 else 0 - - # Calculate distribution of spending by DME, POS, and Drug categories - dme_spend = df['DME_Suplr_Mdcr_Pymt_Amt'].sum() - pos_spend = df['POS_Suplr_Mdcr_Pymt_Amt'].sum() - drug_spend = df['Drug_Suplr_Mdcr_Pymt_Amt'].sum() - - # Add to results - year_data.append({ - 'Year': year, - 'Total Beneficiaries': total_benes, - 'Total Spending': total_spend, - 'Spending Per Beneficiary': spend_per_bene, - 'DME Spending': dme_spend, - 'Prosthetic/Orthotic Spending': pos_spend, - 'Drug Spending': drug_spend - }) - - return pd.DataFrame(year_data) - - -# -------------------- Visualization Functions -------------------- - -def plot_spending_trends(spend_df): - """ - Create visualizations for spending trends over time. - - Parameters: - ----------- - spend_df : DataFrame - DataFrame with yearly spending data, as returned by analyze_spending_patterns - - Returns: - -------- - fig : matplotlib Figure - The figure containing the visualizations - """ - # Set the style - sns.set_style('whitegrid') - - # Create a figure with 2x2 subplots - fig, axes = plt.subplots(2, 2, figsize=(16, 14)) - - # Total beneficiaries by year - sns.lineplot(x='Year', y='Total Beneficiaries', data=spend_df, - marker='o', linewidth=3, markersize=10, ax=axes[0, 0], color='#1f77b4') - axes[0, 0].set_title('Total Beneficiaries by Year', fontsize=16) - axes[0, 0].ticklabel_format(style='plain', axis='y') - axes[0, 0].grid(True) - - # Total spending by year - sns.lineplot(x='Year', y='Total Spending', data=spend_df, - marker='o', linewidth=3, markersize=10, ax=axes[0, 1], color='#ff7f0e') - axes[0, 1].set_title('Total Medicare DME Spending by Year', fontsize=16) - axes[0, 1].ticklabel_format(style='plain', axis='y') - axes[0, 1].yaxis.set_major_formatter( - plt.FuncFormatter(lambda x, pos: f'${x/1e9:.1f}B')) - axes[0, 1].grid(True) - - # Spending per beneficiary by year - sns.lineplot(x='Year', y='Spending Per Beneficiary', data=spend_df, - marker='o', linewidth=3, markersize=10, ax=axes[1, 0], color='#2ca02c') - axes[1, 0].set_title('Average Spending Per Beneficiary', fontsize=16) - axes[1, 0].yaxis.set_major_formatter( - plt.FuncFormatter(lambda x, pos: f'${x:.0f}')) - axes[1, 0].grid(True) - - # Spending by category stacked area chart - category_data = spend_df[['Year', 'DME Spending', - 'Prosthetic/Orthotic Spending', 'Drug Spending']] - category_data_stacked = category_data.set_index('Year') - - # Convert to billions for better readability - category_data_stacked = category_data_stacked / 1e9 - - # Plot stacked area chart - category_data_stacked.plot.area(stacked=True, ax=axes[1, 1], - color=['#1f77b4', '#ff7f0e', '#2ca02c'], - alpha=0.7) - axes[1, 1].set_title('Spending by Category', fontsize=16) - axes[1, 1].set_ylabel('Spending (Billions $)') - axes[1, 1].yaxis.set_major_formatter( - plt.FuncFormatter(lambda x, pos: f'${x:.1f}B')) - axes[1, 1].legend(loc='upper left') - axes[1, 1].grid(True) - - plt.tight_layout() - return fig - - -def plot_demographics(df, year=None): - """ - Create visualizations for beneficiary demographics. - - Parameters: - ----------- - df : DataFrame or dict - Either a DataFrame for a specific year or the df_by_year dictionary - year : int, optional - If df is a dictionary, specify which year to visualize - - Returns: - -------- - fig : matplotlib Figure - The figure containing the visualizations - """ - # If we have multiple years, extract the specified year - if isinstance(df, dict) and year is not None: - if year in df: - df = df[year] - else: - raise ValueError(f"Year {year} not found in data") - - # Get demographics data - demo_results = get_beneficiary_demographics(df) - - # Create a figure with 3 subplots for age, gender, and race - fig, axes = plt.subplots(1, 3, figsize=(18, 6)) - - # Age distribution - age_data = demo_results['Age Distribution'] - age_labels = list(age_data.keys()) - age_values = list(age_data.values()) - - axes[0].pie(age_values, labels=age_labels, autopct='%1.1f%%', - startangle=90, colors=sns.color_palette("Blues", len(age_labels))) - axes[0].set_title('Age Distribution', fontsize=16) - - # Gender distribution - gender_data = demo_results['Gender Distribution'] - gender_labels = list(gender_data.keys()) - gender_values = list(gender_data.values()) - - axes[1].pie(gender_values, labels=gender_labels, autopct='%1.1f%%', - startangle=90, colors=sns.color_palette("Set2", len(gender_labels))) - axes[1].set_title('Gender Distribution', fontsize=16) - - # Race distribution - race_data = demo_results['Race Distribution'] - race_labels = list(race_data.keys()) - race_values = list(race_data.values()) - - # Sort by percentage (descending) - sorted_race = sorted(zip(race_labels, race_values), - key=lambda x: x[1], reverse=True) - race_labels, race_values = zip(*sorted_race) - - axes[2].pie(race_values, labels=race_labels, autopct='%1.1f%%', - startangle=90, colors=sns.color_palette("Set3", len(race_labels))) - axes[2].set_title('Race Distribution', fontsize=16) - - plt.tight_layout() - return fig - - -def plot_health_conditions(df, year=None, top_n=10): - """ - Create visualizations for health conditions prevalence. - - Parameters: - ----------- - df : DataFrame or dict - Either a DataFrame for a specific year or the df_by_year dictionary - year : int, optional - If df is a dictionary, specify which year to visualize - top_n : int, optional - Number of top conditions to display (default: 10) - - Returns: - -------- - fig : matplotlib Figure - The figure containing the visualizations - """ - # If we have multiple years, extract the specified year - if isinstance(df, dict) and year is not None: - if year in df: - df = df[year] - else: - raise ValueError(f"Year {year} not found in data") - - # Get health conditions data - conditions = get_common_health_conditions(df) - - # Create a figure with 2 subplots for physical and behavioral health - fig, axes = plt.subplots(1, 2, figsize=(20, 10)) - - # Physical health conditions - ph_data = conditions['Physical Health Conditions'][:top_n] - ph_labels = [cond for cond, _ in ph_data] - ph_values = [val for _, val in ph_data] - - # Horizontal bar chart for physical health - sns.barplot(x=ph_values, y=ph_labels, palette="Blues_d", ax=axes[0]) - axes[0].set_title('Top Physical Health Conditions', fontsize=16) - axes[0].set_xlabel('Percentage of Beneficiaries', fontsize=12) - axes[0].xaxis.set_major_formatter( - plt.FuncFormatter(lambda x, pos: f'{x:.2f}%')) - axes[0].grid(axis='x') - - # Behavioral health conditions - bh_data = conditions['Behavioral Health Conditions'][:top_n] - bh_labels = [cond for cond, _ in bh_data] - bh_values = [val for _, val in bh_data] - - # Horizontal bar chart for behavioral health - sns.barplot(x=bh_values, y=bh_labels, palette="Oranges_d", ax=axes[1]) - axes[1].set_title('Top Behavioral Health Conditions', fontsize=16) - axes[1].set_xlabel('Percentage of Beneficiaries', fontsize=12) - axes[1].xaxis.set_major_formatter( - plt.FuncFormatter(lambda x, pos: f'{x:.2f}%')) - axes[1].grid(axis='x') - - plt.tight_layout() - return fig - - -def plot_top_suppliers(df, year=None, top_n=10): - """ - Create visualizations for top suppliers. - - Parameters: - ----------- - df : DataFrame or dict - Either a DataFrame for a specific year or the df_by_year dictionary - year : int, optional - If df is a dictionary, specify which year to visualize - top_n : int, optional - Number of top suppliers to display (default: 10) - - Returns: - -------- - fig : matplotlib Figure - The figure containing the visualizations - """ - # If we have multiple years, extract the specified year - if isinstance(df, dict) and year is not None: - if year in df: - df = df[year] - else: - raise ValueError(f"Year {year} not found in data") - - # Get top suppliers data - top_suppliers_df = get_top_suppliers(df, top_n=top_n) - - # Convert payments string to numeric values - top_suppliers_df['Medicare Payments (Numeric)'] = top_suppliers_df['Medicare Payments'].str.replace( - '$', '').str.replace(',', '').astype(float) - - # Sort by payment amount - top_suppliers_df = top_suppliers_df.sort_values( - 'Medicare Payments (Numeric)', ascending=True) - - # Create a figure with 2 subplots - fig, axes = plt.subplots(1, 2, figsize=(20, 10)) - - # Payments bar chart - sns.barplot(x='Medicare Payments (Numeric)', y='Supplier', data=top_suppliers_df, - palette="viridis", ax=axes[0]) - axes[0].set_title( - f'Top {top_n} Suppliers by Medicare Payments', fontsize=16) - axes[0].set_xlabel('Medicare Payments ($)', fontsize=12) - axes[0].xaxis.set_major_formatter( - plt.FuncFormatter(lambda x, pos: f'${x/1e6:.1f}M')) - axes[0].grid(axis='x') - - # Beneficiaries bar chart - top_suppliers_df = top_suppliers_df.sort_values( - 'Beneficiaries', ascending=True) - sns.barplot(x='Beneficiaries', y='Supplier', data=top_suppliers_df, - palette="plasma", ax=axes[1]) - axes[1].set_title( - f'Top {top_n} Suppliers by Number of Beneficiaries', fontsize=16) - axes[1].set_xlabel('Number of Beneficiaries', fontsize=12) - axes[1].xaxis.set_major_formatter( - plt.FuncFormatter(lambda x, pos: f'{x:.0f}')) - axes[1].grid(axis='x') - - plt.tight_layout() - return fig - - -def plot_geographical_distribution(df, year=None): - """ - Create visualizations for the geographical distribution of suppliers. - - Parameters: - ----------- - df : DataFrame or dict - Either a DataFrame for a specific year or the df_by_year dictionary - year : int, optional - If df is a dictionary, specify which year to visualize - - Returns: - -------- - fig : matplotlib Figure - The figure containing the visualizations - """ - # If we have multiple years, extract the specified year - if isinstance(df, dict) and year is not None: - if year in df: - df = df[year] - else: - raise ValueError(f"Year {year} not found in data") - - # Create a figure with 2 subplots - fig, axes = plt.subplots(1, 2, figsize=(20, 8)) - - # State distribution - state_counts = df['Suplr_Prvdr_State_Abrvtn'].value_counts().reset_index() - state_counts.columns = ['State', 'Suppliers'] - - # Sort by count (descending) and get top 15 - state_counts = state_counts.sort_values( - 'Suppliers', ascending=False).head(15) - - sns.barplot(x='Suppliers', y='State', data=state_counts, - palette="viridis", ax=axes[0]) - axes[0].set_title('Top 15 States by Number of Suppliers', fontsize=16) - axes[0].set_xlabel('Number of Suppliers', fontsize=12) - axes[0].grid(axis='x') - - # Rural vs Urban distribution - if 'Suplr_Prvdr_RUCA_Desc' in df.columns: - ruca_counts = df['Suplr_Prvdr_RUCA_Desc'].value_counts().reset_index() - ruca_counts.columns = ['RUCA Description', 'Suppliers'] - - explode = [0.1] * len(ruca_counts) # Explode all slices - - # Plot pie chart for RUCA distribution - axes[1].pie(ruca_counts['Suppliers'], labels=ruca_counts['RUCA Description'], - autopct='%1.1f%%', startangle=90, - colors=sns.color_palette("Set2", len(ruca_counts)), - explode=explode) - axes[1].set_title( - 'Supplier Distribution by Rural-Urban Classification', fontsize=16) - else: - axes[1].text(0.5, 0.5, 'RUCA Description not available', - ha='center', va='center', fontsize=14) - axes[1].set_title( - 'Rural-Urban Distribution (Not Available)', fontsize=16) - - plt.tight_layout() - return fig - - -def create_notebook_visualizations(df_by_year): - """ - Create all visualizations for a Jupyter notebook. - - This is a convenience function that calls all visualization functions - and returns them for display in a Jupyter notebook. - - Parameters: - ----------- - df_by_year : dict - Dictionary with yearly dataframes, as created in main() - - Returns: - -------- - visualizations : dict - Dictionary with all visualizations - """ - import matplotlib.pyplot as plt - - # Most recent year - recent_year = max(df_by_year.keys()) - - # Create spending trend visualizations - spend_df = analyze_spending_patterns(df_by_year) - spending_fig = plot_spending_trends(spend_df) - - # Create demographics visualizations for most recent year - demographics_fig = plot_demographics(df_by_year[recent_year]) - - # Create health conditions visualizations for most recent year - health_conditions_fig = plot_health_conditions(df_by_year[recent_year]) - - # Create top suppliers visualizations for most recent year - suppliers_fig = plot_top_suppliers(df_by_year[recent_year]) - - # Create geographical distribution visualizations for most recent year - geo_fig = plot_geographical_distribution(df_by_year[recent_year]) - - # Return all visualizations - return { - 'spending_trends': spending_fig, - 'demographics': demographics_fig, - 'health_conditions': health_conditions_fig, - 'top_suppliers': suppliers_fig, - 'geographical_distribution': geo_fig - } - - -def main(): - """Main function to import and analyze DME data files.""" - print("DME Data Analysis") - print("================\n") - - # Import data for years 2017-2022 using the utility function - df_by_year = import_data_for_years(range(2017, 2023)) - - if not df_by_year: - print("Error: No data files were found. Cannot proceed with analysis.") - return {}, {} - - print("\nAll available data files have been imported.") - - # Data Overview - print("\n1. Data Overview") - print("---------------\n") - - # Create a summary table - summary_data = { - 'Year': [], - 'Suppliers': [], - 'Total Beneficiaries': [], - 'Total Claims': [], - 'Total Payments ($)': [] - } - - for year, df in df_by_year.items(): - summary_data['Year'].append(year) - summary_data['Suppliers'].append(df.shape[0]) - summary_data['Total Beneficiaries'].append( - df['DME_Tot_Suplr_Benes'].sum()) - summary_data['Total Claims'].append(df['DME_Tot_Suplr_Clms'].sum()) - summary_data['Total Payments ($)'].append( - df['DME_Suplr_Mdcr_Pymt_Amt'].sum()) - - summary_df = pd.DataFrame(summary_data) - print("Summary statistics across years:") - print(summary_df.to_string(index=False, - float_format=lambda x: f"{x:,.0f}" if isinstance(x, (int, float)) else x)) - - # Calculate year-over-year changes - if len(summary_df) > 1: - yoy_data = { - 'Metric': ['Suppliers', 'Beneficiaries', 'Claims', 'Payments'], - 'Change 2021-2022 (%)': [0, 0, 0, 0] - } - - # Calculate year-over-year changes for the most recent years - if 2021 in df_by_year and 2022 in df_by_year: - suppliers_2021 = summary_df[summary_df['Year'] - == 2021]['Suppliers'].values[0] - suppliers_2022 = summary_df[summary_df['Year'] - == 2022]['Suppliers'].values[0] - bene_2021 = summary_df[summary_df['Year'] == - 2021]['Total Beneficiaries'].values[0] - bene_2022 = summary_df[summary_df['Year'] == - 2022]['Total Beneficiaries'].values[0] - claims_2021 = summary_df[summary_df['Year'] - == 2021]['Total Claims'].values[0] - claims_2022 = summary_df[summary_df['Year'] - == 2022]['Total Claims'].values[0] - payments_2021 = summary_df[summary_df['Year'] - == 2021]['Total Payments ($)'].values[0] - payments_2022 = summary_df[summary_df['Year'] - == 2022]['Total Payments ($)'].values[0] - - # Calculate percentage changes - yoy_data['Change 2021-2022 (%)'][0] = ( - (suppliers_2022 - suppliers_2021) / suppliers_2021) * 100 - yoy_data['Change 2021-2022 (%)'][1] = ( - (bene_2022 - bene_2021) / bene_2021) * 100 - yoy_data['Change 2021-2022 (%)'][2] = ( - (claims_2022 - claims_2021) / claims_2021) * 100 - yoy_data['Change 2021-2022 (%)'][3] = ( - (payments_2022 - payments_2021) / payments_2021) * 100 - - yoy_df = pd.DataFrame(yoy_data) - print("\nYear-over-year changes (2021-2022):") - print(yoy_df.to_string( - index=False, float_format=lambda x: f"{x:.2f}%")) - - # Column categories - print("\nColumn Categories:") - recent_year = max(df_by_year.keys()) - df = df_by_year[recent_year] - - categories = set() - for col in df.columns: - categories.add(get_column_category(col)) - - for category in sorted(categories): - # Print a few example columns for each category - example_cols = [ - col for col in df.columns if get_column_category(col) == category][:3] - print( - f" - {category}: {len([col for col in df.columns if get_column_category(col) == category])} columns") - print(f" Examples: {', '.join(example_cols)}") - for col in example_cols: - if col in DATA_DICTIONARY: - print(f" {col}: {DATA_DICTIONARY[col]}") - - # Top Suppliers - print("\n2. Top Suppliers") - print("--------------\n") - recent_year = max(df_by_year.keys()) - top_suppliers = get_top_suppliers(df_by_year[recent_year]) - print(f"Top suppliers for {recent_year}:") - print(top_suppliers.to_string(index=False)) - - # Beneficiary Demographics - print("\n3. Beneficiary Demographics") - print("--------------------------\n") - demographics = get_beneficiary_demographics(df_by_year[recent_year]) - print(f"Demographics for {recent_year}:") - - # Print age distribution - print("\nAge Distribution:") - for age_group, percentage in demographics['Age Distribution'].items(): - print(f" - {age_group}: {percentage:.2f}%") - - # Print gender distribution - print("\nGender Distribution:") - for gender, percentage in demographics['Gender Distribution'].items(): - print(f" - {gender}: {percentage:.2f}%") - - # Print race distribution - print("\nRace Distribution:") - for race, percentage in demographics['Race Distribution'].items(): - print(f" - {race}: {percentage:.2f}%") - - # Health Conditions - print("\n4. Common Health Conditions") - print("-------------------------\n") - conditions = get_common_health_conditions(df_by_year[recent_year]) - print(f"Health conditions for {recent_year}:") - - # Print physical health conditions - print("\nPhysical Health Conditions:") - for condition, percentage in conditions['Physical Health Conditions'][:10]: - print(f" - {condition}: {percentage:.2f}%") - - # Print behavioral health conditions - print("\nBehavioral Health Conditions:") - for condition, percentage in conditions['Behavioral Health Conditions'][:10]: - print(f" - {condition}: {percentage:.2f}%") - - # Spending Patterns - print("\n5. Medicare Spending Patterns") - print("---------------------------\n") - spending_df = analyze_spending_patterns(df_by_year) - - # Format the DataFrame for display with appropriate formatting - formatted_spending_df = spending_df.copy() - - # Format monetary columns with dollar signs - monetary_cols = ['Total Spending', 'Spending Per Beneficiary', 'DME Spending', - 'Prosthetic/Orthotic Spending', 'Drug Spending'] - for col in monetary_cols: - if col in formatted_spending_df.columns: - formatted_spending_df[col] = formatted_spending_df[col].apply( - lambda x: f"${x:,.2f}") - - # Format count columns with commas - count_cols = ['Year', 'Total Beneficiaries'] - for col in count_cols: - if col in formatted_spending_df.columns: - formatted_spending_df[col] = formatted_spending_df[col].apply( - lambda x: f"{x:,.0f}") - - print("Medicare spending patterns across years:") - print(formatted_spending_df.to_string(index=False)) - - # ----- VISUALIZATIONS ----- - print("\n\n6. Generating Visualizations") - print("---------------------------\n") - - # Setting plot style - sns.set_style('whitegrid') - plt.rcParams['figure.figsize'] = [14, 9] - - # Generate all visualizations - visualizations = {} - - # 1. Spending Trends - print("Generating spending trends visualization...") - spending_trends_fig = plot_spending_trends(spending_df) - visualizations['spending_trends'] = spending_trends_fig - - # 2. Demographics - print("Generating demographics visualization...") - demographics_fig = plot_demographics(df_by_year[recent_year]) - visualizations['demographics'] = demographics_fig - - # 3. Health Conditions - print("Generating health conditions visualization...") - health_conditions_fig = plot_health_conditions(df_by_year[recent_year]) - visualizations['health_conditions'] = health_conditions_fig - - # 4. Top Suppliers - print("Generating top suppliers visualization...") - suppliers_fig = plot_top_suppliers(df_by_year[recent_year]) - visualizations['top_suppliers'] = suppliers_fig - - # 5. Geographical Distribution - print("Generating geographical distribution visualization...") - geo_fig = plot_geographical_distribution(df_by_year[recent_year]) - visualizations['geographical_distribution'] = geo_fig - - # 6. Custom visualization: YoY percentage changes - print("Generating year-over-year changes visualization...") - - # Calculate YoY percentage changes - spending_df['Beneficiaries % Change'] = spending_df['Total Beneficiaries'].pct_change() * \ - 100 - spending_df['Spending % Change'] = spending_df['Total Spending'].pct_change() * \ - 100 - spending_df['Per Beneficiary % Change'] = spending_df['Spending Per Beneficiary'].pct_change() * \ - 100 - - # Create plot - yoy_fig, ax = plt.subplots(figsize=(14, 8)) - metrics = ['Beneficiaries % Change', - 'Spending % Change', 'Per Beneficiary % Change'] - colors = ['#1f77b4', '#ff7f0e', '#2ca02c'] - - for i, metric in enumerate(metrics): - ax.plot(spending_df['Year'][1:], spending_df[metric][1:], - marker='o', linewidth=3, markersize=10, - label=metric.replace(' % Change', ''), - color=colors[i]) - - ax.axhline(y=0, color='r', linestyle='--', alpha=0.5) - ax.set_title( - 'Year-over-Year Percentage Changes in Key Metrics', fontsize=16) - ax.legend(fontsize=12) - ax.grid(True) - ax.set_xlabel('Year', fontsize=14) - ax.set_ylabel('Percentage Change (%)', fontsize=14) - visualizations['yoy_changes'] = yoy_fig - - # Save visualizations to files if not in a notebook environment - try: - # Check if we're in a notebook environment - if 'ipykernel' not in sys.modules: - print("\nSaving visualizations to files...") - os.makedirs('visualizations', exist_ok=True) - for name, fig in visualizations.items(): - fig.savefig( - f'visualizations/{name}.png', dpi=300, bbox_inches='tight') - print(f"Saved: visualizations/{name}.png") - except: - print("Note: Visualizations will be displayed if run in a Jupyter notebook") - - # When run in Jupyter, the figures will be displayed inline - return df_by_year, visualizations - - -if __name__ == "__main__": - import sys - main() diff --git a/dme_dictionary.py b/dme_dictionary.py new file mode 100644 index 0000000..f5cd696 --- /dev/null +++ b/dme_dictionary.py @@ -0,0 +1,116 @@ +DATA_DICTIONARY = { + # Supplier Information + "Suplr_NPI": "Supplier NPI - NPI for the Supplier on the DMEPOS claim", + "Suplr_Prvdr_Last_Name_Org": "Supplier Last Name/Organization Name - When registered as individual, the Supplier's last name. When registered as organization, this is the organization name", + "Suplr_Prvdr_First_Name": "Supplier First Name - When registered as individual, the Supplier's first name", + "Suplr_Prvdr_MI": "Supplier Middle Initial - When registered as individual, the Supplier's middle initial", + "Suplr_Prvdr_Crdntls": "Supplier Credentials - When registered as individual, these are the Supplier's credentials", + "Suplr_Prvdr_Gndr": "Supplier Gender - When registered as individual, this is the Supplier's gender", + "Suplr_Prvdr_Ent_Cd": "Supplier Entity Code - 'I' identifies Suppliers registered as individuals, 'O' identifies Suppliers registered as organizations", + "Suplr_Prvdr_St1": "Supplier Street 1 - First line of the Supplier's street address", + "Suplr_Prvdr_St2": "Supplier Street 2 - Second line of the Supplier's street address", + "Suplr_Prvdr_City": "Supplier City - The city where the Supplier is located", + "Suplr_Prvdr_State_Abrvtn": "Supplier State - State postal abbreviation where the Supplier is located", + "Suplr_Prvdr_State_FIPS": "Supplier State FIPS Code - FIPS code for Supplier's state", + "Suplr_Prvdr_Zip5": "Supplier ZIP - The Supplier's ZIP code", + "Suplr_Prvdr_RUCA": "Supplier RUCA - Rural-Urban Commuting Area Code for the Supplier ZIP code", + "Suplr_Prvdr_RUCA_Desc": "Supplier RUCA Description - Description of Rural-Urban Commuting Area (RUCA) Code", + "Suplr_Prvdr_Cntry": "Supplier Country - Country where the Supplier is located", + "Suplr_Prvdr_Spclty_Desc": "Supplier Provider Specialty Description - Derived from Medicare provider/supplier specialty code", + "Suplr_Prvdr_Spclty_Srce": "Supplier Provider Specialty Source - Source of the Supplier Specialty (claims-specialty or NPPES-specialty)", + + # Total Supplier Claims/Services + "Tot_Suplr_HCPCS_Cds": "Number of Supplier HCPCS - Total unique DMEPOS product/service HCPCS codes", + "Tot_Suplr_Benes": "Number of Supplier Beneficiaries - Total unique beneficiaries (<11 are suppressed)", + "Tot_Suplr_Clms": "Number of Supplier Claims - Total DMEPOS claims submitted", + "Tot_Suplr_Srvcs": "Number of Supplier Services - Total DMEPOS products/services rendered", + "Suplr_Sbmtd_Chrgs": "Supplier Submitted Charges - Total charges submitted for DMEPOS products/services", + "Suplr_Mdcr_Alowd_Amt": "Supplier Medicare Allowed Amount - Total Medicare allowed amount", + "Suplr_Mdcr_Pymt_Amt": "Supplier Medicare Payment Amount - Amount Medicare paid after deductible/coinsurance", + "Suplr_Mdcr_Stdzd_Pymt_Amt": "Supplier Medicare Standard Payment Amount - Standardized Medicare payments", + + # DME-specific Fields + "DME_Sprsn_Ind": "Durable Medical Equipment Suppression Indicator - '*'=suppressed (1-10 claims), '#'=counter-suppressed", + "DME_Tot_Suplr_HCPCS_Cds": "Number of DME HCPCS - Total unique DME HCPCS codes", + "DME_Tot_Suplr_Benes": "Number of DME Beneficiaries - Total unique beneficiaries with DME claims (<11 are suppressed)", + "DME_Tot_Suplr_Clms": "Number of DME Claims - Total DME claims submitted", + "DME_Tot_Suplr_Srvcs": "Number of DME Services - Total DME products/services rendered", + "DME_Suplr_Sbmtd_Chrgs": "DME Submitted Charges - Total charges submitted for DME products/services", + "DME_Suplr_Mdcr_Alowd_Amt": "DME Medicare Allowed Amount - Total Medicare allowed amount for DME", + "DME_Suplr_Mdcr_Pymt_Amt": "DME Medicare Payment Amount - Amount Medicare paid for DME after deductible/coinsurance", + "DME_Suplr_Mdcr_Stdzd_Pymt_Amt": "DME Medicare Standard Payment Amount - Standardized Medicare payments for DME", + + # Prosthetic and Orthotic Fields + "POS_Sprsn_Ind": "Prosthetic and Orthotic Suppression Indicator - '*'=suppressed (1-10 claims), '#'=counter-suppressed", + "POS_Tot_Suplr_HCPCS_Cds": "Number of Prosthetic/Orthotic HCPCS - Total unique prosthetic/orthotic HCPCS codes", + "POS_Tot_Suplr_Benes": "Number of Prosthetic/Orthotic Beneficiaries - Total unique beneficiaries", + "POS_Tot_Suplr_Clms": "Number of Prosthetic/Orthotic Claims - Total prosthetic/orthotic claims submitted", + "POS_Tot_Suplr_Srvcs": "Number of Prosthetic/Orthotic Services - Total prosthetic/orthotic products/services", + "POS_Suplr_Sbmtd_Chrgs": "Prosthetic/Orthotic Submitted Charges - Total charges submitted for prosthetic/orthotic", + "POS_Suplr_Mdcr_Alowd_Amt": "Prosthetic/Orthotic Medicare Allowed Amount - Total Medicare allowed amount", + "POS_Suplr_Mdcr_Pymt_Amt": "Prosthetic/Orthotic Medicare Payment Amount - Amount Medicare paid after deductible/coinsurance", + "POS_Suplr_Mdcr_Stdzd_Pymt_Amt": "Prosthetic/Orthotic Medicare Standard Payment Amount - Standardized Medicare payments", + + # Drug and Nutritional Fields + "Drug_Sprsn_Ind": "Drug and Nutritional Suppression Indicator - '*'=suppressed (1-10 claims), '#'=counter-suppressed", + "Drug_Tot_Suplr_HCPCS_Cds": "Number of Drug/Nutritional HCPCS - Total unique drug/nutritional HCPCS codes", + "Drug_Tot_Suplr_Benes": "Number of Drug/Nutritional Beneficiaries - Total unique beneficiaries", + "Drug_Tot_Suplr_Clms": "Number of Drug/Nutritional Claims - Total drug/nutritional claims submitted", + "Drug_Tot_Suplr_Srvcs": "Number of Drug/Nutritional Services - Total drug/nutritional products/services", + "Drug_Suplr_Sbmtd_Chrgs": "Drug/Nutritional Submitted Charges - Total charges submitted for drug/nutritional", + "Drug_Suplr_Mdcr_Alowd_Amt": "Drug/Nutritional Medicare Allowed Amount - Total Medicare allowed amount", + "Drug_Suplr_Mdcr_Pymt_Amt": "Drug/Nutritional Medicare Payment Amount - Amount Medicare paid after deductible/coinsurance", + "Drug_Suplr_Mdcr_Stdzd_Pymt_Amt": "Drug/Nutritional Medicare Standard Payment Amount - Standardized Medicare payments", + + # Beneficiary Demographics + "Bene_Avg_Age": "Average Age of Beneficiaries - Average age at end of calendar year or time of death", + "Bene_Age_LT_65_Cnt": "Number of Beneficiaries <65 - Count of beneficiaries under 65 years old", + "Bene_Age_65_74_Cnt": "Number of Beneficiaries 65-74 - Count of beneficiaries between 65-74 years old", + "Bene_Age_75_84_Cnt": "Number of Beneficiaries 75-84 - Count of beneficiaries between 75-84 years old", + "Bene_Age_GT_84_Cnt": "Number of Beneficiaries >84 - Count of beneficiaries over 84 years old", + "Bene_Feml_Cnt": "Number of Female Beneficiaries - Count of female beneficiaries", + "Bene_Male_Cnt": "Number of Male Beneficiaries - Count of male beneficiaries", + "Bene_Race_Wht_Cnt": "Number of White Beneficiaries - Count of non-Hispanic white beneficiaries", + "Bene_Race_Black_Cnt": "Number of Black Beneficiaries - Count of non-Hispanic Black/African American beneficiaries", + "Bene_Race_Api_Cnt": "Number of Asian/PI Beneficiaries - Count of Asian Pacific Islander beneficiaries", + "Bene_Race_Hspnc_Cnt": "Number of Hispanic Beneficiaries - Count of Hispanic beneficiaries", + "Bene_Race_Natind_Cnt": "Number of Native American/Alaska Native Beneficiaries - Count of American Indian/Alaska Native beneficiaries", + "Bene_Race_Othr_Cnt": "Number of Other Race Beneficiaries - Count of beneficiaries with race not elsewhere classified", + "Bene_Ndual_Cnt": "Number of Medicare & Medicaid Beneficiaries - Count of dual-eligible beneficiaries", + "Bene_Dual_Cnt": "Number of Medicare-Only Beneficiaries - Count of Medicare-only beneficiaries", + + # Beneficiary Health Conditions (Mental/Behavioral Health) + "Bene_CC_BH_ADHD_OthCD_V1_Pct": "Percent with ADHD and Other Conduct Disorders", + "Bene_CC_BH_Alcohol_Drug_V1_Pct": "Percent with Alcohol and Drug Use Disorders", + "Bene_CC_BH_Tobacco_V1_Pct": "Percent with Tobacco Use Disorders", + "Bene_CC_BH_Alz_NonAlzdem_V2_Pct": "Percent with Alzheimer's and Non-Alzheimer's Dementia", + "Bene_CC_BH_Anxiety_V1_Pct": "Percent with Anxiety Disorders", + "Bene_CC_BH_Bipolar_V1_Pct": "Percent with Bipolar Disorder", + "Bene_CC_BH_Mood_V2_Pct": "Percent with Depression, Bipolar or Other Mood Disorders", + "Bene_CC_BH_Depress_V1_Pct": "Percent with Major Depressive Affective Disorder", + "Bene_CC_BH_PD_V1_Pct": "Percent with Personality Disorders", + "Bene_CC_BH_PTSD_V1_Pct": "Percent with Post-Traumatic Stress Disorder", + "Bene_CC_BH_Schizo_OthPsy_V1_Pct": "Percent with Schizophrenia and Other Psychotic Disorders", + + # Beneficiary Health Conditions (Physical Health) + "Bene_CC_PH_Asthma_V2_Pct": "Percent with Asthma", + "Bene_CC_PH_Afib_V2_Pct": "Percent with Atrial Fibrillation and Flutter", + "Bene_CC_PH_Cancer6_V2_Pct": "Percent with Cancer (combined 6 cancer indicators)", + "Bene_CC_PH_CKD_V2_Pct": "Percent with Chronic Kidney Disease", + "Bene_CC_PH_COPD_V2_Pct": "Percent with Chronic Obstructive Pulmonary Disease", + "Bene_CC_PH_Diabetes_V2_Pct": "Percent with Diabetes", + "Bene_CC_PH_HF_NonIHD_V2_Pct": "Percent with Heart Failure and Non-Ischemic Heart Disease", + "Bene_CC_PH_Hyperlipidemia_V2_Pct": "Percent with Hyperlipidemia", + "Bene_CC_PH_Hypertension_V2_Pct": "Percent with Hypertension", + "Bene_CC_PH_IschemicHeart_V2_Pct": "Percent with Ischemic Heart Disease", + "Bene_CC_PH_Osteoporosis_V2_Pct": "Percent with Osteoporosis", + "Bene_CC_PH_Parkinson_V2_Pct": "Percent with Parkinson's Disease", + "Bene_CC_PH_Arthritis_V2_Pct": "Percent with Rheumatoid Arthritis/Osteoarthritis", + "Bene_CC_PH_Stroke_TIA_V2_Pct": "Percent with Stroke/Transient Ischemic Attack", + + # Risk Score + "Bene_Avg_Risk_Scre": "Average HCC Risk Score of Beneficiaries", + + # Year column (added by our script) + "year": "Year of the data" +} diff --git a/fraud_detector.py b/fraud_detector.py deleted file mode 100644 index 160ce92..0000000 --- a/fraud_detector.py +++ /dev/null @@ -1,311 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -DME Fraud Detection Script -This script analyzes Medicare DME supplier data to identify potential fraud indicators, -with a focus on suspicious growth patterns similar to credit card fraud detection techniques. -""" - -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import seaborn as sns -from collections import defaultdict -import os -import sys - -# Import from the new module structure -from dme_analysis.utils import ( - DATA_DICTIONARY, - import_dme_data, - get_column_mapping, - get_column_category, - import_data_for_years -) - - -def detect_high_growth_suppliers(df_by_year, metric='DME_Suplr_Mdcr_Pymt_Amt', top_n=50): - """ - Identify suppliers with abnormally high growth rates year over year. - - Parameters: - ----------- - df_by_year : dict - Dictionary containing DataFrames by year - metric : str - The metric to analyze for growth (default: Medicare payments) - top_n : int - Number of top growth suppliers to identify - - Returns: - -------- - growth_df : DataFrame - DataFrame containing suppliers with their growth rates - """ - print(f"Identifying suppliers with highest year-over-year growth rates...") - - # Check if metric exists in all dataframes - for year, df in df_by_year.items(): - if metric not in df.columns: - available_metrics = [ - col for col in df.columns if 'Pymt' in col or 'Amt' in col] - if not available_metrics: - print( - f"Error: No payment metrics found in data for year {year}.") - return pd.DataFrame() - - # Use the first available payment metric - metric = available_metrics[0] - print(f"Using alternate metric: {metric}") - break - - # Get all available years - years = sorted(df_by_year.keys()) - - if len(years) < 2: - print("Error: Need at least two years of data to calculate growth rates") - return pd.DataFrame() - - # Get column mappings from the most recent year's data - recent_year = max(years) - column_map = get_column_mapping(df_by_year[recent_year]) - - # Create a dictionary to store supplier data across years - supplier_data = {} - supplier_info = {} - - # Process each supplier's data for each year - for year in years: - df = df_by_year[year] - - # Get NPI column name - npi_col = column_map['supplier_npi'] - if npi_col is None: - # Create a synthetic NPI using index - df['synthetic_npi'] = 'NPI' + df.index.astype(str) - npi_col = 'synthetic_npi' - - # Group by supplier NPI and sum the metric - supplier_metric = df.groupby(npi_col)[metric].sum().reset_index() - - # Store in dictionary - for _, row in supplier_metric.iterrows(): - npi = row[npi_col] - value = row[metric] - - if npi not in supplier_data: - supplier_data[npi] = {} - - # Store supplier info for later use - supplier_row = df[df[npi_col] == npi].iloc[0] if len( - df[df[npi_col] == npi]) > 0 else None - if supplier_row is not None: - supplier_info[npi] = { - 'name': supplier_row[column_map['supplier_name']] if column_map['supplier_name'] is not None else f"Supplier {npi}", - 'state': supplier_row[column_map['supplier_state']] if column_map['supplier_state'] is not None else 'Unknown' - } - else: - supplier_info[npi] = { - 'name': f"Supplier {npi}", - 'state': 'Unknown' - } - - supplier_data[npi][year] = value - - # Calculate year-over-year growth rates - growth_data = [] - - for npi, year_values in supplier_data.items(): - # Need at least two years of data for this supplier - if len(year_values) < 2: - continue - - for i in range(len(years) - 1): - current_year = years[i] - next_year = years[i + 1] - - # Skip if supplier doesn't have data for both years - if current_year not in year_values or next_year not in year_values: - continue - - current_value = year_values[current_year] - next_value = year_values[next_year] - - # Skip if current value is zero (would result in infinity growth) - if current_value == 0: - continue - - # Calculate growth rate - growth_rate = ((next_value - current_value) / current_value) * 100 - - growth_data.append({ - 'Supplier NPI': npi, - 'Supplier Name': supplier_info[npi]['name'], - 'Supplier State': supplier_info[npi]['state'], - 'Year Period': f"{current_year}-{next_year}", - 'Start Year Value': current_value, - 'End Year Value': next_value, - 'Growth Rate (%)': growth_rate, - 'Absolute Growth': next_value - current_value - }) - - # Convert to DataFrame - growth_df = pd.DataFrame(growth_data) - - # Sort by growth rate (descending) - growth_df = growth_df.sort_values('Growth Rate (%)', ascending=False) - - return growth_df.head(top_n) - - -def plot_high_growth_suppliers(growth_df, top_n=20): - """ - Create a visualization of suppliers with highest growth rates. - - Parameters: - ----------- - growth_df : DataFrame - DataFrame from detect_high_growth_suppliers function - top_n : int - Number of top suppliers to visualize - - Returns: - -------- - fig : Figure - Matplotlib figure object containing the visualization - """ - # Take top N suppliers - plot_df = growth_df.head(top_n) - - # Create figure - fig, ax = plt.subplots(figsize=(14, 10)) - - # Plot horizontal bar chart - bars = sns.barplot( - x='Growth Rate (%)', - y='Supplier Name', - data=plot_df, - palette='viridis', - ax=ax - ) - - # Add value labels - for i, bar in enumerate(bars.patches): - value = plot_df.iloc[i]['Growth Rate (%)'] - ax.text( - bar.get_width() + 10, - bar.get_y() + bar.get_height()/2, - f"{value:,.1f}%", - ha='left', - va='center', - fontweight='bold' - ) - - # Add a second x-axis for absolute growth - ax2 = ax.twiny() - ax2.set_xlabel('Absolute Growth ($)', color='red') - ax2.tick_params(axis='x', colors='red') - - # Plot absolute growth as scatter points - for i, (_, row) in enumerate(plot_df.iterrows()): - ax2.scatter(row['Absolute Growth'], i, color='red', s=100, alpha=0.7) - - # Format the x-axis for absolute growth with dollar amounts - ax2.xaxis.set_major_formatter( - plt.FuncFormatter(lambda x, pos: f'${x:,.0f}')) - - # Set labels and title - ax.set_xlabel('Growth Rate (%)', fontsize=14) - ax.set_ylabel('Supplier', fontsize=14) - ax.set_title(f'Top {top_n} Suppliers by Growth Rate', - fontsize=16, fontweight='bold') - - # Add year period information - if not plot_df.empty: - year_periods = plot_df['Year Period'].unique() - period_str = ', '.join(year_periods) - ax.text( - 0.5, 1.05, f"Year Period(s): {period_str}", transform=ax.transAxes, ha='center') - - # Add grid - ax.grid(axis='x', linestyle='--', alpha=0.7) - - plt.tight_layout() - return fig - - -def main(): - """Main function to import and analyze DME data for fraud detection.""" - print("DME Fraud Detection Analysis") - print("===========================\n") - - # Import data for years 2017-2022 using the utility function - df_by_year = import_data_for_years(range(2017, 2023)) - - if not df_by_year: - print("\nError: No data files were successfully imported. Cannot proceed with analysis.") - return {}, {} - - print(f"\n{len(df_by_year)} year(s) of data imported.") - - # ----- FRAUD DETECTION ANALYSIS ----- - print("\n1. High Growth Rate Analysis") - print("--------------------------\n") - - # Detect suppliers with abnormally high growth rates - growth_df = detect_high_growth_suppliers(df_by_year, top_n=50) - - if growth_df.empty: - print("No suppliers with high growth rates detected.") - return df_by_year, {}, {} - - # Print summary of top 15 high-growth suppliers - print("Top 15 suppliers with highest growth rates:") - - # Format the output for display - formatted_growth_df = growth_df.head(15).copy() - formatted_growth_df['Growth Rate (%)'] = formatted_growth_df['Growth Rate (%)'].apply( - lambda x: f"{x:.2f}%") - formatted_growth_df['Start Year Value'] = formatted_growth_df['Start Year Value'].apply( - lambda x: f"${x:,.2f}") - formatted_growth_df['End Year Value'] = formatted_growth_df['End Year Value'].apply( - lambda x: f"${x:,.2f}") - formatted_growth_df['Absolute Growth'] = formatted_growth_df['Absolute Growth'].apply( - lambda x: f"${x:,.2f}") - - print(formatted_growth_df.to_string(index=False)) - - # ----- VISUALIZATIONS ----- - print("\n2. Generating Fraud Detection Visualizations") - print("------------------------------------------\n") - - # Setting plot style - sns.set_style('whitegrid') - plt.rcParams['figure.figsize'] = [14, 9] - - # Generate visualization - growth_fig = plot_high_growth_suppliers(growth_df, top_n=20) - visualizations = {'high_growth_suppliers': growth_fig} - data = {'high_growth_suppliers': growth_df} - - # Save visualizations to files if not in a notebook environment - try: - # Check if we're in a notebook environment - if 'ipykernel' not in sys.modules: - print("\nSaving visualizations to files...") - os.makedirs('fraud_visualizations', exist_ok=True) - for name, fig in visualizations.items(): - fig.savefig( - f'fraud_visualizations/{name}.png', dpi=300, bbox_inches='tight') - print(f"Saved: fraud_visualizations/{name}.png") - except Exception as e: - print(f"Error saving visualizations: {str(e)}") - print("Note: Visualizations will be displayed if run in a Jupyter notebook") - - # When run in Jupyter, the figures will be displayed inline - return df_by_year, visualizations, data - - -if __name__ == "__main__": - main() diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index f40278b..0000000 --- a/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -jupyter==1.0.0 -notebook==7.3.2 -pandas==2.2.3 -numpy==1.26.0 -matplotlib==3.9.2 -seaborn==0.13.2 -scikit-learn==1.6.1 \ No newline at end of file From 241e4a59bfeb14317ae9f3af4a6052e05aac24c6 Mon Sep 17 00:00:00 2001 From: Khanan Grauer Date: Tue, 11 Mar 2025 08:57:41 -0400 Subject: [PATCH 3/3] Moving cell --- dme_analysis.ipynb | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/dme_analysis.ipynb b/dme_analysis.ipynb index 4a2ba84..0432eb2 100644 --- a/dme_analysis.ipynb +++ b/dme_analysis.ipynb @@ -2409,21 +2409,6 @@ "- Total Payments" ] }, - { - "cell_type": "markdown", - "id": "9b851377", - "metadata": {}, - "source": [ - "# 8. Conclusions & Next Steps\n", - "We've combined multi-year DME data, identified year-over-year outliers, analyzed high submitted vs. allowed/paid ratios, and performed peer-group checks.\n", - "\n", - "### Potential Enhancements\n", - "1. **Additional Metrics**: Incorporate DME-specific categories (e.g., prosthetics vs. drug/nutrition) and investigate outliers in each.\n", - "2. **Machine Learning**: Replace threshold-based outlier detection with algorithms (Isolation Forest, DBSCAN, etc.).\n", - "3. **Visualization**: Plot distributions, boxplots, or time-series charts for top suspicious suppliers.\n", - "4. **Interactive Dashboards**: Provide an interface for users to adjust thresholds and instantly see flagged suppliers.\n" - ] - }, { "cell_type": "code", "execution_count": 11, @@ -2788,6 +2773,21 @@ " else:\n", " print(\"No combined specialty–state groups with >=5 suppliers.\")" ] + }, + { + "cell_type": "markdown", + "id": "9b851377", + "metadata": {}, + "source": [ + "# 8. Conclusions & Next Steps\n", + "We've combined multi-year DME data, identified year-over-year outliers, analyzed high submitted vs. allowed/paid ratios, and performed peer-group checks.\n", + "\n", + "### Potential Enhancements\n", + "1. **Additional Metrics**: Incorporate DME-specific categories (e.g., prosthetics vs. drug/nutrition) and investigate outliers in each.\n", + "2. **Machine Learning**: Replace threshold-based outlier detection with algorithms (Isolation Forest, DBSCAN, etc.).\n", + "3. **Visualization**: Plot distributions, boxplots, or time-series charts for top suspicious suppliers.\n", + "4. **Interactive Dashboards**: Provide an interface for users to adjust thresholds and instantly see flagged suppliers.\n" + ] } ], "metadata": {