AI takes the complexity out of medical coding

Medical coding sits at the heart of healthcare revenue cycle management. For this client, the process was almost entirely manual: coders hand-assigned codes to medical procedures, diagnoses, and patient records, a task that was time-consuming, expensive, and prone to human error. Anesthesia procedures posed a particular challenge, with their complexity making accurate code assignment especially difficult. The client needed an AI-driven solution that could automate coding, lift accuracy across the board, and reduce the administrative burden on their coding teams.

Svitla worked closely with the client's subject matter experts, including an anaesthesiologist leading the automation initiative, to design a multi-layered AI system tailored to the specific demands of medical coding.

The solution automates the assignment of CPT, ASA, and ICD-10 codes by analysing patient data, diagnoses, and procedure notes. A multi-layer validation framework checks assigned codes against the requirements of different insurance companies, catching errors before they reach final output. The system was designed to require minimal retraining when new data is introduced, keeping it relevant as coding standards evolve.

Additional capabilities include:

• Automated report generation for specific case subsets, giving coding teams detailed performance insights
• Advanced input preprocessing to reduce document complexity and improve AI analysis speed
• A modular architecture built on AWS and Azure, with caching strategies to manage the cost of running large language models

The platform was deployed across multiple healthcare facilities and coding teams. Accuracy improved significantly, particularly for complex anaesthesia cases. Operational costs dropped through intelligent caching and by exploring locally hosted alternatives to cloud-based language models. Processing times fell, manual workloads reduced, and the modular design means the system can absorb new coding rules and datasets without significant rework.