HIPAA TRANSACTION SET AUTOMATION WITH AI-ASSISTED EDI 837 CLAIM GENERATION FROM STRUCTURED CLINICAL DATA

Healthcare revenue cycle management (RCM) loses an estimated $262 billion annually in the United States due
to claim denials, billing errors, and manual coding inefficiencies. The ANSI X12 837 electronic data interchange
(EDI) transaction set - the mandatory HIPAA standard for professional, institutional, and dental claim submission
- demands precise adherence to thousands of segment-level rules, ICD-10-CM diagnosis codes, CPT/HCPCS
procedure codes, and payer-specific policy overlays that exceed the practical capacity of rule-based automation.
This paper presents a production-validated architecture for automating 837 claim generation from structured
clinical data using a GPT-4 language model deployed on Azure OpenAI Service, integrated with an ASP.NET
Core 8 backend and a four-source Retrieval-Augmented Generation (RAG) knowledge base. The system achieves
a first-pass claim acceptance rate of 95.1%, reduces denial rates from 18.8% to 3.8%, and cuts mean claim build
time from 12.4 minutes to 1.6 minutes across a 10-month production evaluation (January–October 2024, n =
487,800 claims).
Key contributions include a seven-stage clinical-to-837 prompt engineering pipeline, a FHIR R4 to X12 field
mapping specification covering ten critical element translations, a comprehensive HIPAA security control
architecture operating within Azure's Business Associate Agreement boundary, and a 24-week phased
implementation roadmap validated across three regional health systems.