Within-Hospital Readmission: An Indicator of Readmission after Discharge from Psychiatric Hospitalization
Bibliographic record
Abstract
OBJECTIVE: Readmission after psychiatric hospitalization is widely used as a quality of care indicator by government funding agencies, policy-makers, and hospitals deciding on clinical priorities. Readmission rates are calculated accurately to allow these varied groups to correctly translate the knowledge into appropriate, tangible outcomes. We aimed to assess how well hospital readmission rates, calculated using only readmissions to the discharging institution, can approximate actual readmission rates. METHOD: We used administrative data sources to identify patients with a mental health discharge in the province of Ontario (2008-2011). We identified mental health readmissions within 30 and 90 days of discharge occurring to the hospital from which the patient was discharged (within-hospital readmissions), and compared readmission rates using only within-hospital admissions with actual readmission rates. RESULTS: The percentage of readmissions occurring to the discharging institution ranged from 39% to 89% (median 73%) and from 37% to 86% (median 70%) for 30- and 90-day readmissions, respectively. Using only within-hospital readmissions to rank hospitals by their readmission rates, only 56% of hospitals for 30-day readmissions and 50% for 90-day readmissions were ranked in the same quartile as when actual readmission rates were used. CONCLUSIONS: These findings highlight the importance of measuring psychiatric readmissions at the system level, particularly for hospitals with lower discharge volumes. As well, the high likelihood that multiple hospitals are involved in the hospital-based care of people who require readmission requires consideration at clinical and policy levels.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".