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A Multi-Schedule Machine Learning Pipeline for Medicare Reimbursement Change Prediction and Operational Risk
Ishan H Patel1, Alejandro Leyva1, Muhammad Khalid Khan Niazi1
1AI4Path Lab, Department of Pathology, The Ohio State University Wexner Medical Center, The Ohio State University, Columbus, OH, USA.
Abstract:
FeePredict is a three-stage random forest machine learning framework to simultaneously predict whether Medicare reimbursement rates for specific procedures will change, in which direction they will change, and by how much. FeePredict was applied to the four major Medicare fee schedules: the Clinical Laboratory Fee Schedule (CLFS), the Physician Fee Schedule (PFS), the Ambulance Fee Schedule (AFS), and the Durable Medical Equipment, Prosthetics, Orthotics, and Supplies (DMEPOS) fee schedule. Each of these fee schedules contains publicly available data from the Centers for Medicare & Medicaid Services (CMS) for the years 2024, 2025, and 2026, with the number of procedures represented in the data ranging from 3,264 to 2,952,842 observations. FeePredict utilizes lag-1 feature engineering and train-only preprocessing steps to ensure that there is no data leakage into the model. Chronological out-of-time validation was performed on three of the four fee schedules to determine the generalizability of the model over time. FeePredict significantly outperformed the assumption that there would be no changes to Medicare reimbursement rates for procedures (p < 0.001), achieving concordance indices between 0.815 and 0.998, and reducing the mean absolute error for predicting changes to reimbursement rates by 29% to 85%. Permutation testing of the model with shuffled reimbursement rate labels indicates that there is no evidence of data leakage (AUC values: 0.467-0.515). The model achieved concordance indices of 0.854 and 0.972 for the CLFS and DMEPOS fee schedules, respectively, outside of its training period, but performed less well outside of its training period for the PFS, indicating that it generalizes less well to changes to the Medicare policy regime that existed after its training period. Overall, though, these results indicate that it is possible to accurately predict whether Medicare reimbursement rates for medical procedures will change using only data from the historical versions of those fee schedules.
Insights
FeePredict, a machine learning model, accurately forecasts changes in Medicare reimbursement rates across major fee schedules. The framework predicts rate changes, direction, and magnitude, outperforming baseline assumptions.
Area of Science:
- Health Informatics
- Machine Learning Applications
- Healthcare Policy Analysis
Background:
- Medicare reimbursement rates significantly impact healthcare provider revenue and patient access to services.
- Predicting changes in these rates is crucial for financial planning and operational strategy within the healthcare sector.
- Existing methods for forecasting reimbursement rates are limited in scope and accuracy.
Purpose of the Study:
- To develop and validate a machine learning framework, FeePredict, for simultaneously predicting the occurrence, direction, and magnitude of changes in Medicare reimbursement rates.
- To apply FeePredict to four major Medicare fee schedules: Clinical Laboratory Fee Schedule (CLFS), Physician Fee Schedule (PFS), Ambulance Fee Schedule (AFS), and Durable Medical Equipment, Prosthetics, Orthotics, and Supplies (DMEPOS).
- To assess the generalizability and accuracy of FeePredict using historical data and out-of-time validation.
Main Methods:
- A three-stage random forest machine learning framework (FeePredict) was employed.
- Lag-1 feature engineering and train-only preprocessing were utilized to prevent data leakage.
- Chronological out-of-time validation was conducted on CLFS, PFS, and AFS to evaluate model generalizability.
- Permutation testing was performed to further validate against data leakage concerns.
Main Results:
- FeePredict significantly outperformed the null hypothesis of no change (p < 0.001), with concordance indices ranging from 0.815 to 0.998.
- The model reduced the mean absolute error for predicting reimbursement rate changes by 29% to 85%.
- Out-of-time validation showed strong performance for CLFS (0.854) and DMEPOS (0.972), but less generalizability for PFS, indicating sensitivity to policy regime shifts.
Conclusions:
- It is feasible to accurately predict changes in Medicare reimbursement rates using historical fee schedule data.
- FeePredict demonstrates a robust capability for forecasting reimbursement rate dynamics, offering valuable insights for healthcare financial management.
- Further refinement may be needed for models to adapt to significant shifts in Medicare policy regimes.