Development and Testing of Improved Models to Predict Payment Using Centers for Medicare & Medicaid Services Claims Data

Importance: Predicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare 37. 4% aged ≤74 years), 677 044 for HF (45. 5% male; 25. 9% aged ≤74 years), and 922 889 for pneumonia (46. 4% male; 28. 2% aged ≤74 years). The mean (SD) 30-day payment was 23 103 (18 221) for AMI, 16 365 (12 527) for HF, and 17 097 (12 087) for pneumonia. Each incremental model change improved the pseudo R2 and RMSE. Incorporating all 3 changes improved the pseudo R2 of the patient-level models from 0. 077 to 0. 129 for AMI, from 0. 042 to 0. 129 for HF, and from 0. 114 to 0. 237 for pneumonia. Parallel improvements in RMSE were found for all 3 conditions. Conclusions and Relevance: Leveraging POA codes, separating index from previous diagnoses, and using single diagnostic codes improved payment models. Better models can potentially improve research, benchmarking, public reporting, and calculations for population-based programs.