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CodaMetrix
Re-imagining the future of revenue cycle with AI-powered autonomous coding.

What is CodaMetrix?

CodaMetrix leverages artificial intelligence to automate the complex process of medical coding within the healthcare revenue cycle. Its platform is designed to seamlessly integrate into existing workflows, reducing the manual burden on coding staff and physicians. By processing clinical documentation, it accurately assigns the necessary medical codes required for billing and patient care records.

This automation leads to significant improvements in operational efficiency, including drastically reduced turnaround times for coding and substantial cost savings. The system enhances coding accuracy, which minimizes claim denials and optimizes revenue capture for health systems. It supports various care models, from fee-for-service to value-based care, ensuring compliance and financial stability for provider organizations.

Features

  • Autonomous Medical Coding: Automates over 96% of cases across specialties into accurate medical codes.
  • Seamless Workflow Integration: Operates without requiring changes to existing healthcare provider workflows.
  • Denial Reduction: Significantly lowers coding-related claim denials.
  • Cost Reduction: Achieves over 30% savings on coding costs and reduces manual coding effort by 60-70%.
  • Increased Speed: Delivers coding results up to 5 times faster than manual processes.
  • Transparency and Traceability: Provides visibility into the reasoning behind every coding decision.
  • Multi-Specialty Support: Applicable across various medical specialties.

Use Cases

  • Automating routine medical coding tasks for hospitals and health systems.
  • Reducing coding backlogs and improving turnaround times.
  • Lowering operational costs associated with manual coding.
  • Minimizing claim denials due to coding errors.
  • Improving revenue cycle efficiency and financial performance.
  • Freeing up physician and coder time for more complex tasks.
  • Supporting transition to value-based care models through accurate coding.

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