Combined Impact of Medicare’s Hospital Pay for Performance Programs on Quality and Safety Outcomes is Mixed
Bibliographic record
Abstract
Abstract BACKGROUND Three major hospital pay for performance (P4P) programs were introduced by the Affordable Care Act and intended to improve the quality, safety and efficiency of care provided to Medicare beneficiaries. The financial risk to hospitals associated with Medicare’s P4P programs is substantial. Evidence on the positive impact of these programs, however, has been mixed, and no study has assessed their combined impact. In this study, we examined the combined impact of Medicare’s P4P programs on clinical areas and populations targeted by the programs, as well as those outside their focus.METHODS We used 2007 – 2016 Healthcare Cost and Utilization Project State Inpatient Databases for 14 states to identify hospital-level inpatient quality indicators (IQIs) and patient safety indicators (PSIs), by quarter and payer (Medicare vs. non-Medicare). IQIs and PSIs are standardized, evidence-based measures that can be used to track hospital quality of care and patient safety over time using hospital administrative data. The study period of 2007 – 2016 was selected to capture multiple years before and after introduction of program metrics. Interrupted time series was used to analyze the impact of the P4P programs on study outcomes targeted and not targeted by the programs. In sensitivity analyses, we examined the impact of these programs on care for non-Medicare patients.RESULTS Medicare P4P programs were not associated with consistent improvements in targeted or non-targeted quality and safety measures. Moreover, mortality rates across targeted and untargeted conditions were generally getting worse after the introduction of Medicare’s P4P programs. Trends in PSIs were extremely mixed, with five outcomes trending in an expected (improving) direction, five trending in an unexpected (deteriorating) direction, and three with insignificant changes over time. Sensitivity analyses did not substantially alter these results.CONCLUSIONS Consistent with previous studies for individual programs, we detect minimal, if any, effect of Medicare’s hospital P4P programs on quality and safety. Given the growing evidence of limited impact, the administrative cost of monitoring and enforcing penalties, and potential increase in mortality, CMS should consider redesigning their P4P programs before continuing to expand them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".