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

Scalable event-driven architecture: The solution features a scalable, asynchronous event-driven microservices architecture orchestrated by Camunda. This architecture efficiently manages complex workflows and task assignments, resulting in improved responsiveness and operational efficiency.

Modernized frontend with enhanced customization: The frontend has been modernized using React, Chakra UI, and Tailwind CSS, leveraging atomic design principles for superior modularity and ease of customization. This approach significantly improves the overall user experience and adaptability of the interface.

Full integration with external EHR systems: The solution had to fully integrate with external EHR systems so that important physician and patient data could be imported and added without any problems. Frontend widgets visually represent this data, providing valuable clinical insights and supporting informed decision-making.

Extensible widget framework: Zemoso developed an extensible widget framework, streamlining the addition of new functionalities. This framework facilitated seamless feature integration without significant redevelopment efforts, making the platform highly adaptable to different hospital systems.

Robust cloud deployment: The solution is deployed on Amazon Web Services (AWS). It utilizes CloudFront CDN for optimized data delivery and AWS Network Load Balancer to effectively handle volatile and unpredictable traffic demands, ensuring consistent availability and performance.

Advanced analytics and secure data management: Databricks is utilized for secure and compliant storage of patient case data. This functionality is complemented by advanced analytics and reporting capabilities provided through Dundas BI. Rigorous tenancy and security protocols ensure data compliance, privacy, and security.

Conclusion

The autonomous medical coding platform developed by Zemoso Labs effectively interprets clinical documentation to assign accurate medical codes to procedures, significantly reducing manual effort and potential errors. Enhanced with machine learning capabilities, the solution integrates seamless project management tools, streamlining workflow processes from task assignments to coding reviews. Built on a flexible architecture, the platform ensures smooth integration with electronic health record (EHR) systems and supports necessary human oversight for accuracy and compliance. A robust cloud deployment further maintains performance and availability, even under variable traffic conditions. By combining automation with strategic human intervention, healthcare providers can achieve more efficient revenue cycles, improved accuracy in medical coding, and enhanced operational effectiveness.

Note: We have refrained from providing too many details about the AI engine model that drove the solution as it is proprietary. We are happy to discuss it further if you’d like to reach out to us via our Contact Us page.