Is audit and feedback associated with increased hospital adherence to standards for communication during patient care transitions?
Bibliographic record
Abstract
Guidelines to enhance communication during patient care transitions between healthcare settings have the potential to improve patient outcomes and satisfaction, as well as to decrease overall costs. In 2009, Healthcentric Advisors, the Medicare Quality Improvement Organization (QIO) for New England, collaborated with Rhode Island providers and stakeholders to develop communitywide standards for hospitals, the Safe Transitions Best Practice Measures for Hospitals and then implemented a hospital quality improvement intervention. As part of this intervention, 10 of the state’s 11 acute-care hospitals collected quarterly data and Healthcentric Advisors provided audit and feedback reports showing each facility’s progress and the state’s average performance. Using hospital-reported data on four best practice measures and Medicare claims data for Q2 2011-Q1 2013, we performed descriptive analyses of (1) inpatient-to-outpatient communication for all patients at the 10 participating hospitals, as measured by four best practice measures, and (2) state-wide all-cause, 30-day readmission rates per 1,000 fee-for-service (FFS) beneficiaries. Aggregate performance for the four best practice process measures increased by 5.5% to 217.6% for all patients who met eligibility criteria (p ≤ .001 for each measure) and the readmission rate decreased by 18.4% from 14.12 to 11.52 per 1,000 eligible FFS Medicare beneficiaries (p ≤ .001). These findings suggest that communitywide standards, such as the Safe Transitions Best Practice Measures for Hospitals, and audit and feedback interventions can successfully improve hospitals’ communication during patient care transitions between healthcare settings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".