Virtual Acute Psychiatric Ward: Evaluation of Outcomes and Cost Savings.

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.

Early Learning From a Low-Resource COVID-Response Virtual Mental Health Crisis Ward: Mixed Methods Study.

BACKGROUND: The COVID-19 pandemic was accompanied by the accelerated uptake of virtual care, leading to a proliferation of virtual ward models as alternatives to facility-based care. Early in the pandemic, our program implemented a virtual mental health crisis ward (vWard) to provide options for individuals requiring intense psychiatric and/or crisis support but who preferred to remain in the community and were deemed safe to do so. OBJECTIVE: The aim of this study was to identify early learnings from the vWard, which was implemented rapidly in a resource-constrained environment, to inform the future state should it be sustained beyond the pandemic. METHODS: Mixed methods of data collection were used to evaluate provider perspectives on the vWard, develop archetypes for individuals who are a good fit for the vWard model, and create a driver diagram. Data sources included an anonymous survey of clinical and managerial staff involved in the vWard, a service planning workshop, and program discharge forms for all individuals admitted between March 2020 and April 2021. Survey responses were coded for themes under categories of "benefits" and "challenges." Discharge forms where the team indicated that the vWard was a good fit for an individual were examined for characteristics common to these admissions. These findings were reviewed in the service planning workshop and refined with input from the participants into patient archetypes. A driver diagram was created for the future state. RESULTS: Survey respondents (N=60) represented diverse roles in crisis services and the vWard team. Ten providers took part in the service planning workshop. A total of 467 discharge forms were reviewed. The vWard was felt to be a model that worked by 39 survey respondents, one respondent felt it did not work, and the remaining participants had no response. Several benefits for the individual and the system were identified alongside challenges, including certain processes and materials related to the nature of rapid implementation during the pandemic, and others due to lack of fit for certain individuals. The model was felt to be a good fit for 67.5% of admissions. Four patient archetypes representing a good fit with the model were developed. The driver diagram connected the program aim with primary drivers of (1) reduce barriers to care; (2) improve outcomes; and (3) provide collaborative, patient- and family-centered care to secondary drivers and interventions that leveraged virtual technology among other crisis care interventions. CONCLUSIONS: Despite some challenges, the vWard demonstrated high levels of provider acceptance and a range of mechanisms by which the model works for a variety of patient archetypes. These early learnings provide a foundation for growth, sustainability, and spread of this model going forward beyond the pandemic.