Machine Learning Versus Conventional Benchmarks for Predicting Next-Year Medicare Allowed Amounts
Main Article Content
Abstract
The Medicare Physician Fee Schedule provides an administrative reference for physician payment, but fee-schedule
values and historical averages are retrospective and may not capture nonlinear, geographic, specialty-specific, setting-specific, and
utilization-related changes that influence future observed allowed amounts. This study evaluates whether machine-learning and
conventional models can predict next-year service-weighted average Medicare allowed amounts for high-volume evaluation-andmanagement services more accurately than predetermined fee-schedule and historical benchmarks. Publicly available Centers for
Medicare & Medicaid Services (CMS) Medicare Physician and Other Practitioners data and Physician Fee Schedule files from
2018–2024 were used. The analysis was restricted to participating individual physician billing for HCPCS 99213 and 99214
across the 50 U.S. states and the District of Columbia, separated by facility and non-facility setting. Groups required at least 100
services and five unique clinicians. Models were developed on earlier years, selected using 2023 data, and evaluated on a sealed
2024 test set. Histogram gradient boosting (HGB), random forest, ridge regression, pooled-history, recent-year-history, and
fee-schedule-reference approaches were compared using service-weighted mean absolute error (WMAE). The final 2024 cohort
contained 6,829 eligible groups representing 117,969,671 services. HGB achieved the lowest WMAE at $0.829 per service,
compared with random forest at $1.173, ridge regression at $1.650, pooled history at $1.884, recent-year history at $1.907, and
the fee-schedule reference at $4.803. Relative to ridge regression, HGB reduced WMAE by 49.7%, with a state-block bootstrap
difference of -$0.821 per service (95% CI: -$1.172 to -$0.563). HGB improved WMAE in 49 of 51 jurisdictions, with at least
5% improvement in 47. These findings support temporally sealed, group-level machine-learning forecasting of Medicare allowed
amounts for the evaluated services while not establishing claims-level accuracy, causal effects, or future official CMS payment
decisions.
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