Self-reflection as a Tool to Increase Hospitalist Participation in Readmission Quality Improvement
Bibliographic record
Abstract
BACKGROUND: Reducing 30-day readmissions is a national priority. Although multipronged programs have been shown to reduce readmissions, the role of the individual hospitalist physician in reducing readmissions is not clear. OBJECTIVES: We evaluated the effect of physicians' self-review of their own readmission cases on the 30-day readmission rate. METHODS: Over a 1-year period, hospitalists were sent their individual readmission rates and cases on a weekly basis. They reviewed their cases and completed a data abstraction tool. In addition, a facilitator led small group discussion about common causes of readmission and ways to prevent such readmissions. RESULTS: Our preintervention readmission rate was 16.16% and postintervention was 14.99% (P = .76). Among hospitalists on duty, nearly all participated in scheduled facilitated discussions. Self-review was completed in 67% of the cases. CONCLUSIONS: A facilitated reflective practice intervention increased hospitalist participation and awareness in the mission to reduce readmissions and this intervention resulted in a nonsignificant trend in readmission reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".