Preventability of 28-Day Hospital Readmissions in General Internal Medicine Patients: A Retrospective Analysis at a Quaternary Hospital
Bibliographic record
Abstract
BACKGROUND: Unplanned hospital readmissions are associated with increased patient mortality and health care costs, yet only a fraction are likely to be preventable. This study's objective was to identify preventable hospital readmissions of general internal medicine patients, and their common causes. METHODS: Patients who were discharged from the general internal medicine teaching service and readmitted to hospital within 28 days for 24 hours or more were recruited to the study; they were identified via the hospital electronic medical record system. Data were gathered via structured review of hospital charts/electronic medical records, along with standardized patient interviews. Unique to our study, a multidisciplinary panel of physicians, nurses, and hospital administrators adjudicated preventability and identified common causes of readmission. RESULTS: Fifty-five hospital readmissions were identified; 53% were adjudicated to be preventable. There was no difference in any variable analyzed between preventable and nonpreventable readmissions. The most common causes of preventable readmissions were inadequate coordination of community services upon discharge, insufficient clinical postdischarge follow-up, and suboptimal end-of-life care. CONCLUSION: This study identified a higher proportion of preventable 28-day hospital readmissions when compared with prior research. Increased involvement of palliative care during initial hospitalization for appropriate conditions and improvements in care after discharge may reduce preventable hospital readmissions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".