THE HEDIS INSURGENCY: BUILDING AN AI AGENT ARMY TO HACK HEALTHCARE QUALITY MEASUREMENT

TABLE OF CONTENTS

1. Introduction

2. The HEDIS Measurement Problem

3. Market Opportunity and Economics

4. Technical Architecture and AI Agent Design

5. Agent Army Operations and Coordination

6. Clinical Workflows and Integration Points

7. Business Model and Go-to-Market Strategy

8. Competitive Landscape and Differentiation

9. Implementation Roadmap and Scaling Strategy

10. Risks, Challenges, and Mitigation

11. Conclusion

ABSTRACT

Disclaimer: The views and opinions expressed in this essay are my own and do not reflect the positions, strategies, or opinions of my employer or any affiliated organizations.

This dossier presents a technical and business framework for a health technology company built around deploying specialized AI agents to systematically improve Healthcare Effectiveness Data and Information Set (HEDIS) measures across health plan populations. The core innovation involves creating an "agent army" where each agent specializes in a specific HEDIS measure or clinical pathway, operating semi-autonomously while coordinating through a central orchestration layer. The company would generate revenue through performance-based contracts tied to quality measure improvement, targeting the approximately $60 billion annual flow between CMS and Medicare Advantage plans based on Star Ratings. Key technical components include: multi-modal data ingestion from claims, EHR, and pharmacy systems; specialized large language models fine-tuned for clinical workflows; real-time decisioning engines; and integration with existing care management platforms. The business model leverages risk-sharing arrangements where the company captures 30-50% of incremental quality bonus payments generated through measurable HEDIS improvements. Primary risks include regulatory scrutiny of AI in clinical workflows, integration complexity with legacy health IT systems, and the challenge of maintaining AI model performance across diverse health plan populations.

---

Introduction