Inefficient billing practices are costing U.S. doctors $125B per year. An integral part of managing the revenue lifecycle management for healthcare service providers is medical coding. Medical coding has to be done following the strict guidelines and protocols established in ICD-10, and requires diligent oversight. Delays impact everyone in the lifecycle: the hospital, the service providers like the doctors, and the patient. Zemoso’s customer wanted to launch an autonomous medical coding solution that would leverage artificial intelligence (AI) and machine learning (ML) more effectively with an autonomous medical coding solution. This would enable medical coders to be more accurate and accelerate the overall process without compromising the integrity of the entire process.
Inefficient billing practices cost U.S. doctors $125B a year, and medical coding - bound by strict ICD-10 guidelines - remains a manual, expertise-dependent bottleneck that delays hospitals, providers, and patients alike. The client wanted an autonomous coding solution that could automate 100% of procedures, including complex ones requiring nuanced judgment, while integrating cleanly with electronic health record and physician systems.
Zemoso Labs collaborated with the company's product, engineering, and data science teams to automate the medical coding process through robotic process automation (RPA). The self-organized pod delivered an initial design prototype, continuing design services, and product engineering and development on an accelerated timeline.
The solution
The solution, an autonomous medical coding platform, was built leveraging advanced artificial intelligence (AI) and machine learning (ML) technologies to interpret clinical documentation precisely and autonomously assign medical codes to procedures. The platform's proprietary ML models and natural language processing (NLP) capabilities significantly enhance coding accuracy, automate routine coding tasks, and intelligently handle complex procedures that traditionally require nuanced human judgment.
Additionally, the solution integrates sophisticated project management capabilities, streamlining workflows from coding job assignments to review stages. It also proactively identifies when human intervention is needed, ensuring accuracy and compliance at every step. The AI-driven recommendations include clearly assigned confidence levels, enabling reviewers to efficiently focus their attention on higher-risk or lower-confidence cases. This strategic integration of AI and human oversight optimizes overall efficiency and accuracy in the medical coding lifecycle.
Solution highlights
The autonomous medical coding platform built by Zemoso interprets clinical documentation to assign accurate codes, significantly reducing manual effort and errors. Built on a flexible architecture with human oversight built in, it gives healthcare providers more efficient revenue cycles, improved coding accuracy, and stronger operational effectiveness even under variable traffic conditions.
