Psychiatric Rehospitalization
Bibliographic record
Abstract
OBJECTIVE: Rehospitalization affects quality of life and health system efficiency. Although this outcome is a common quality indicator, there are few applications for linking evaluation to risk mitigation at the person level. This study examined risk factors for rehospitalization to develop an application for supporting care planning based on the interRAI Mental Health (MH), a commonly available assessment system. METHOD: A retrospective analysis was performed of 53,538 psychiatric inpatients assessed with the interRAI MH in Ontario, Canada, between January 2010 and May 2014. The interRAI MH is a clinical system for assessing demographic variables, service utilization, functional status, and clinical needs. Logistic regression models and survival analysis were used to develop the Rehospitalization Clinical Assessment Protocol by predicting 90-day rehospitalization to any inpatient mental health bed. RESULTS: Variables found to significantly predict rehospitalization included 6 or more lifetime hospitalizations (odds ratio [OR] = 1.40), positive symptoms of psychosis (OR = 1.23), a secondary substance use disorder (OR = 1.13), and being at risk of harm to self (OR = 1.11). Using these variables, the Rehospitalization Clinical Assessment Protocol was derived whereby those at level 2 (highest) were 74% more likely to be rehospitalized within 90 days than those at level 0. By 1-year postdischarge, 30% at level 2 and 18% at level 0 were rehospitalized. CONCLUSIONS: The Rehospitalization Clinical Assessment Protocol is an application supporting care planning for targeting risk of rehospitalization whenever a person is assessed with the interRAI MH. Further exploration is needed to understand how the use of this Clinical Assessment Protocol, service processes, and health system structures further mediate or moderate psychiatric rehospitalization risk.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".