Public Reporting of Mortality Rates for Hospitalized Medicare Patients and Trends in Mortality for Reported Conditions
Bibliographic record
Abstract
BACKGROUND: Public reporting is seen as a powerful quality improvement tool, but data to support its efficacy are limited. The Centers for Medicare & Medicaid Services' Hospital Compare program initially reported process metrics only but started reporting mortality rates for acute myocardial infarction, heart failure, and pneumonia in 2008. OBJECTIVE: To determine whether public reporting of mortality rates was associated with lower mortality rates for these conditions among Medicare beneficiaries. DESIGN: For 2005 to 2007, process-only reporting was considered; for 2008 to 2012, process and mortality reporting was considered. Changes in mortality trends before and during reporting periods were estimated by using patient-level hierarchical modeling. Nonreported medical conditions were used as a secular control. SETTING: U.S. acute care hospitals. PARTICIPANTS: 20 707 266 fee-for-service Medicare beneficiaries hospitalized from January 2005 through November 2012. MEASUREMENTS: 30-day risk-adjusted mortality rates. RESULTS: Mortality rates for the 3 publicly reported conditions were changing at an absolute rate of -0.23% per quarter during process-only reporting, but this change slowed to a rate of -0.09% per quarter during process and mortality reporting (change, 0.13% per quarter; 95% CI, 0.12% to 0.14%). Mortality for nonreported conditions was changing at -0.17% per quarter during process-only reporting and slowed slightly to -0.11% per quarter during process and mortality reporting (change, 0.06% per quarter; CI, 0.05% to 0.07%). LIMITATION: Administrative data may have limited ability to account for changes in patient complexity over time. CONCLUSION: Changes in mortality trends suggest that reporting in Hospital Compare was associated with a slowing, rather than an improvement, in the ongoing decline in mortality among Medicare patients. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".