AI-Driven Medical Coding: 5× Speed, 98% Precision
An established healthcare revenue analytics firm slashed coding times and error rates using our AI medical coding solution, boosting efficiency, accuracy, and claims recovery.
Executive Summary
A large, US-based healthcare revenue analytics organization faced significant challenges in medical coding with efficiency, accuracy, and bringing new disease classification experts up to speed. The company, which works with both healthcare providers and payers, changed the way it works with the AI-based medical coding solution.
As with any healthcare IT solution, keeping protected health information (PHI) secure was non-negotiable. The AI solution was fully integrated into the organization's coding and revenue cycle management (RCM) systems.
New coding operations staff take significantly less than the 30-45 minutes they took earlier, with more accurate coding.
About the Client
The client is a 25-year-old healthcare revenue analytics organization serving providers and payers. Specializing in end-to-end claim lifecycle management for both healthcare providers and payers, the company is technology-driven and innovative, leveraging deep expertise across the entire claims lifecycle.
The company offers services such as medical coding audits, revenue enhancement, and compliance analytics. Their expertise encompasses areas like Diagnosis-Related Group (DRG) validations, underpayment recovery, and predictive denial management, aiming to optimize healthcare reimbursement processes.
The Challenge
With increasing business, the organization constantly added new staff to keep up. Qualified medical coding staff were not easy to hire, and even more difficult to train. The coding from the less experienced staff took longer and was often of lower quality.
Healthcare coding was a human-intensive, 15–30 minute process per case, rife with consistency issues and claim denials. Analysts juggled complex surgical and anesthesia reports without standardized tools, resulting in:
The Solution
A custom-trained AI model transformed coding workflows end-to-end.
We implemented a purpose-built AI engine that ingests surgery and anesthesia reports, applying advanced NLP and medical ontology matching to automatically assign.
The Impact
Coding throughput accelerated, and accuracy soared.
Post-deployment, the AI outpaced manual coders, cutting per-case processing time by over 75% and achieving a sustained 99%+ coding accuracy. The system’s proactive data-gap alerts and “assume-when-forced” logic minimized claim denials and freed analysts for higher-value tasks.