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Medicare HCC Risk Analytics Engine

A high-performance risk adjustment scoring engine built to model CMS-HCC (Hierarchical Condition Categories) for 2.3M synthetic Medicare beneficiaries.

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## Executive Summary

Risk Adjustment is the financial operating system of Value-Based Care. This engine ingests raw CMS SynPUF claims data, calculates patient age/demographics, and computes a Risk Adjustment Factor (RAF) score to simulate capitated reimbursement revenue.

The system is engineered for performance, utilizing Polars for strictly-typed, high-speed data processing (outperforming Pandas on large datasets) and Streamlit for real-time financial simulation.

## Architecture & Stack

  • Core Logic: Python 3.11+
  • Data Engine: Polars (Rust-based DataFrame library)
  • Interface: Streamlit
  • Visualization: Plotly Express
  • Data Source: CMS DE-SynPUF (2008-2010)

## Key Features

  1. ETL Pipeline: Ingests and normalizes raw CMS CSV files into high-speed Parquet storage.
  2. Scoring Logic: Implements CMS-HCC V24 demographic scoring constraints (Age/Sex interactions).
  3. Financial Simulator: Dynamic dashboard allowing operators to adjust Base Rates and visualize revenue variance.
  4. Population Stratification: Cohort analysis by Age Band and Risk Quartile.

## Usage

1. Environment Setup

git clone [https://github.com/YOUR_USERNAME/medicare-hcc-analytics.git](https://github.com/YOUR_USERNAME/medicare-hcc-analytics.git)
cd medicare-hcc-analytics
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Data Ingestion Note: Raw CMS data is not included in the repo due to size constaints.
1. Download Sample 1 (2008 Beneficiary Summary) from CMS SynPUF
2. Place the .csv file in data/raw/.
3. Run the pipeline:
python src/ingestion.py
python src/scoring.py

3. Launch Dashboard
streamlit run src/app.py
### **Why this works:**
* **Bold Headers (`**1. ...**`):** Instead of relying on Markdown's automatic list rendering (which breaks easily with code blocks), I manually bolded the numbers. This guarantees they will always appear as "1.", "2.", "3." regardless of the code blocks in between.
* **Clarity:** This "Manual Header" style is standard in technical documentation because it is impossible to break.

**Next Step:** Once you save this file, commit and push it to GitHub:
```bash
git add README.md
git commit -m "Add documentation"
git push

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CMS-HCC Risk Adjustment Engine build with Polars and Python

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