Virtual Acute Psychiatric Ward: Evaluation of Outcomes and Cost Savings
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic motivated rapid expansion of virtual care. In Winnipeg, Canada, the authors launched a virtual psychiatric acute care ward (vWARD) to divert patients from hospitalization through daily remote treatment by a psychiatry team using telephone or videoconferencing. This study examined vWARD patient characteristics, predictors of transfer to a hospital, use of acute care postdischarge, and costs of the vWARD compared with in-person hospitalization. METHODS: Data for all vWARD admissions from March 23, 2020, to April 30, 2021, were retrieved from program documents and electronic records. Emergency department visits and hospitalizations in the 6 months before admission and the 30 days after discharge were documented. Logistic regression identified factors associated with transfer to a hospital. Thirty-day acute care use after discharge was modeled with Kaplan-Meier curves. A break-even cost analysis was generated with data for usual hospital-based care. RESULTS: The 132 vWARD admissions represented a diverse demographic and clinical population. Overall, 57% involved suicidal behavior, and 29% involved psychosis or mania. Seventeen admissions (13%) were transferred to a hospital. Only presence of psychosis or mania significantly predicted transfer (OR=34.2, 95% CI=3.3-354.6). Eight individuals were hospitalized in the 30 days postdischarge (cumulative survival=0.93). vWARD costs were lower than usual care across several scenarios. CONCLUSIONS: A virtual ward is a feasible, effective, and potentially cost-saving intervention to manage acute psychiatric crises in the community and avoid hospitalization. It has benefits for both the health system and the individual who prefers to receive care at home.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".