DRG overpayment accuracy models drive identification of the highest potential for medical errors and overpayments. Auditors review medical coding for diagnosis and prognosis accuracy leveraging ML to unearth opportunities for resequencing encounters enhancing patient outcomes and operational efficiencies across the healthcare system.
Rules Engine: DRG Overpayment
Approach
Our team implemented advanced machine learning algorithms to analyze historical medical coding data, uncovering patterns and discrepancies that highlight potential inaccuracies in diagnosis and prognosis. This strategic use of ML significantly enhances the reliability of medical coding by identifying errors that human auditors might overlook.
Delivered Solution
- Rules Engine: Business rules engine designed with an approximate with an approximate 17.5x ROI
- Over 2000+ DRG and Coding related rules created by users, leveraging the design and processing of custom data.
- DRG Selection Application: Custom build user interface for case review and auditing of correct coding adhering to clinical guidelines established by CMS.
- Evaluate potential changes in reimbursement
- Seamless integration with 3M or TrueCoder groupers
Value Generated
- Data-Driven Insights for Continuous Improvement: Offers analytics and insights into coding practices and trends, enabling continuous improvement in clinical documentation and DRG classification processes.
- Rules Engine Start-Up: The company was acquired for $300+ Million primarily driven by rules engine, rules engine and selection model stack produced ~$35M in annually revenue.
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