Improving target price calculations in Medicare bundled payment programs
Bibliographic record
Abstract
OBJECTIVE: To compare the predictive accuracy of two approaches to target price calculations under Bundled Payments for Care Improvement-Advanced (BPCI-A): the traditional Centers for Medicare and Medicaid Services (CMS) methodology and an empirical Bayes approach designed to mitigate the effects of regression to the mean. DATA SOURCES: Medicare fee-for-service claims for beneficiaries discharged from acute care hospitals between 2010 and 2016. STUDY DESIGN: We used data from a baseline period (discharges between January 1, 2010 and September 30, 2013) to predict spending in a performance period (discharges between October 1, 2015 and June 30, 2016). For 23 clinical episode types in BPCI-A, we compared the average prediction error across hospitals associated with each statistical approach. We also calculated an average across all clinical episode types and explored differences by hospital size. DATA COLLECTION/EXTRACTION METHODS: We used a 20% sample of Medicare claims, excluding hospitals and episode types with small numbers of observations. PRINCIPAL FINDINGS: The empirical Bayes approach resulted in significantly more accurate episode spending predictions for 19 of 23 clinical episode types. Across all episode types, prediction error averaged $8456 for the CMS approach versus $7521 for the empirical Bayes approach. Greater improvements in accuracy were observed with increasing hospital size. CONCLUSIONS: CMS should consider using empirical Bayes methods to calculate target prices for BPCI-A.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".