README.md

metadata

title: ClaimSense
emoji: 🏥
colorFrom: purple
colorTo: indigo
sdk: streamlit
sdk_version: 1.38.0
app_file: app.py
pinned: false

ClaimSense — Agentic Clinical Claims Intelligence

Built for the Cotiviti AI/Healthcare Informatics Internship Assessment Author: Kshamaa Suresh | Columbia University MS Data Science

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What This Is

ClaimSense is a proof-of-concept agentic clinical claims intelligence platform that demonstrates how AI can close the gap between anomaly detection and clinical action — not just flagging suspicious claims, but reasoning about why they are suspicious and recommending what to do next.

This mirrors the core challenge in healthcare payment integrity: existing tools surface signals. What they rarely do is explain those signals in actionable terms, or recommend the specific next step a reviewer should take. ClaimSense demonstrates that gap being closed.

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The Three-Stage Pipeline

Stage 1 — Synthetic Claims Generation

120 realistic medical claims are generated with injected anomalies across six categories:

• Diagnosis-procedure mismatch (e.g., URI diagnosis + knee replacement procedure)
• Duplicate claims (same patient, procedure, and date of service)
• High-frequency billing (provider billing high-cost procedures at unusual frequency)
• Amount outliers (billed amount > 2.5 standard deviations above procedure mean)
• Future dates of service (impossible claim dates)
• Unbundling patterns (separately billing components that should be bundled)

Stage 2 — Rule-Based Anomaly Detection

A multi-rule detection engine flags claims and assigns:

• A confidence score (0–1)
• The specific rules triggered
• A risk level (Normal / Low / Medium / High / Critical)

Stage 3 — Agentic Reasoning (Groq LLM)

For each flagged claim, a Groq-powered LLM agent provides:

• Chain-of-reasoning explanation grounded in the actual claim data
• Recommended action (auto-deny, escalate, request documentation, approve)
• Risk assessment summary
• Faithfulness score — a RAGAS-style check verifying the explanation is grounded in the claim data rather than hallucinated

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Setup

Environment Variables

Add your Groq API key as a HuggingFace Space secret:

GROQ_API_KEY = your_key_here

Get a free key at console.groq.com

Local Development

pip install -r requirements.txt
export GROQ_API_KEY=your_key_here
streamlit run app.py

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Technical Stack

Component	Technology
Synthetic data	Python (pandas, numpy)
Anomaly detection	Rule-based engine (6 detectors)
LLM reasoning	Groq API (llama3-70b-8192)
Faithfulness evaluation	RAGAS-style claim-level verification
Frontend	Streamlit + Plotly
Hosting	HuggingFace Spaces (CPU free tier)

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Why This Matters for Cotiviti

The CMS estimated $31 billion in improper Medicare payments in FY2023. The bottleneck is rarely detection — it is the gap between a flagged signal and a reviewed, actioned decision. ClaimSense demonstrates how agentic AI can compress that gap from days to seconds, while maintaining a full audit trail of reasoning for every decision.

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Disclaimer

This is a research and engineering demonstration prototype. All patient data is fully synthetic. No real PHI is used or generated. This system is not intended for clinical or payment decision use without appropriate validation, regulatory review, and human oversight.