Virtual Mental Health Crisis Ward: Evaluating Patient Outcomes and Cost Effectiveness
Bibliographic record
Abstract
A virtual psychiatric reassessment and observation Unit (vROU) was launched in March 2020 at the Winnipeg Crisis Response Center (CRC) in response to the COVID-19 pandemic. It is based on a hospital-at-home model where patients receive monitoring and interventions from home with the goal of reducing hospitalizations. This study's aim was to assess the vROU user characteristics, patient-level predictors of hospitalization, acute care use in the 30 days post discharge, and cost-effectiveness of the model in comparison to usual hospital care using a break-even analysis. A retrospective chart review of all admissions from the first two years of the program (March 23rd, 2020 to May 31st, 2022) was completed. Pre and post-program health care utilization from electronic patient records were retrieved including emergency department visits and hospitalizations. A logistic regression was completed to identify predictors of hospitalization from the vROU. Thirty-day cumulative survival for acute care use post-discharge was calculated. A break-even cost analysis was done using data from the Canadian Institute for Health Information to create cost models for the program compared to usual hospital-based care. During the study period, the vROU had 197 admissions; 59.7% of which exhibited suicidal behaviour and 26.9% that presented with psychosis and/or mania. Suicidal planning (OR = 14.50, 95% CI 1.19-176.34, P=.036) and psychosis and/or mania (OR = 45.30, 95% CI 5.26-389.93, P=<.001) were significantly associated with hospitalization. Twelve patients (Cumulative survival=.93) were hospitalized in the 30 days post-discharge. The vROU was cost-saving compared to usual care.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".