ClaimShield

GenAI Assisted Healthcare Claim Denial Prevention Agent

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Presented By: Sagar Kurhekar

Sagar Kurhekar

sagar_kurhekar@persistent.com

“ClaimShield” -Health Claim Denial Prevention Agent

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Problem statement and proposed solution

Problem

• Healthcare providers and revenue cycle teams face significant financial losses and operational inefficiencies due to medical claim denials. A substantial proportion of these denials are preventable, often resulting from issues like incorrect coding, or non-compliance with payer-specific rules/policies. Manually identifying and addressing such risks before claims are submitted is time-consuming, error-prone, and lacks scalability.

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• Many organizations currently lacks an automated, proactive system to analyze claims for denial risks prior to submission. As a result, preventable denials increase administrative costs, delay reimbursements, and strain resources required for appeals and rework.

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• Although existing solutions use statistical ML models to predict potential denials, they lack in utilising unstructured payer policies

Solution

• ClaimShield agent uses payer specific guidelines ,exclusion policies & rules against CPT codes as knowledgebase (context grounding) for validating incoming claims and predict potential denials.

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• It also uses historical claim denial data as additional input for making the prediction

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• It compares unstructured patient chart , treatment details as well as structured information like patient DOB, treatment date etc. against payer rules , identifies patterns from historical denials and predicts if claim will be denied / accepted

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• It sends a report to billing team for review

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Benefits and technologies used

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Benefits, impact and outcomes

• ClaimShield Denial Prevention Agent helps reduce preventable claim denials, cutting administrative costs and accelerating reimbursements
• It improves coding accuracy and compliance with payer rules, minimizing rework and appeals.
• Acts as an intellgent checker checker of claims before they are submitted to payers and eases the workload for billing and coding teams.

Details

Solution architecture

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LLM

UiPath Orchestrator

UiPath Robot

ClaimShield Agent

Context Grounding

Verdict & Reasoning

Prompt + Context

Verdict , Reasoning & Suggestions for claim

Payer,Treatment details etc.

Update Output

Read Input

Excel

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** Will be replaced with EMR system like EPIC **

Billng Team

Assessment Report for Review

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Live Demo

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https://drive.google.com/file/d/1gTtvVvyblB1vBfO95LWAhw1K9Wfn0Wmh/view?usp=sharing

Future Enhancements

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1. Integration with atleatst one EMR like OpenEMR is planned. It will enable to pickup patient chart & treatment details directly from EMR using EMR API. Current static excel input source will be replaced.

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• This solution can be adapted to integrate with any downstream application including EMR where agent verdict can be updated against claim. This will improve user experience for billing teams.

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• Instead of static knowledge base of payer guidlines, an integration with repositories of payer guidelines can be considered.

ClaimShield denial prevention agent uses UiPath Agentic AI to predict likely claim denials and recommendations to improve acceptance by grounding responses in payer guidelines and historical claim denials. This approach has certainly enhance medical billing and coding team’s efficiency and accuracy