Healthcare
Healthcare: Medical Coding Automation
4.4
D
4.6
U
4.7
B
4.3
E
The Problem
Medical coding errors cost the US healthcare system $36 billion annually, with 10-15% of claims denied on first submission due to incorrect ICD-10, CPT, or HCPCS codes. A healthcare revenue cycle company serving 200+ hospital systems faced a critical shortage of certified medical coders, with each coder reviewing only 50-80 charts daily while maintaining 95%+ accuracy requirements. The company needed an AI system to transform physician documentation into accurate billing codes, reducing denial rates while maintaining compliance with payer-specific guidelines and CMS regulations.
Type
Healthcare Revenue Cycle Company
Industry
Healthcare
Size
Enterprise
Region
Massachusetts, United States
Users
800+
The Analysis
The AI system required natural language processing to extract clinical concepts from physician notes, operative reports, and discharge summaries. Code suggestion models mapped extracted concepts to ICD-10-CM diagnosis codes, ICD-10-PCS procedure codes, CPT codes, and HCPCS Level II codes. Payer-specific logic applied Medicare, Medicaid, and commercial insurance guidelines for bundling, modifiers, and medical necessity. Human-in-the-loop workflows routed complex cases and low-confidence suggestions to certified coders. Complete audit trails documented reasoning from source documentation through final code selection for compliance reviews.
The Solution
Discovery
6 weeks
Development
22 weeks
Integration
10 weeks
Deployment
5 weeks
The Results
Key Outcomes
Key Learnings
Payer-specific guidelines changed frequently. Build automated update pipelines for LCD/NCD policies.
High-confidence thresholds initially rejected too many straightforward cases. Tune by code complexity tier.
Physician documentation quality was the primary accuracy driver. Consider CDI integration for upstream improvement.
About DUBEScore™
On-time, on-budget execution. Measures project management quality, milestone adherence, and resource efficiency.
Real-world usefulness. Evaluates how well the solution solves the stated problem and meets user needs.
Measurable ROI and value creation. Tracks revenue impact, cost savings, and strategic outcomes.
Long-term sustainability. Assesses maintainability, scalability, and system resilience over time.