Effect of a Postdischarge Virtual Ward on Readmission or Death for High-Risk Patients
Bibliographic record
Abstract
IMPORTANCE: Hospital readmissions are common and costly, and no single intervention or bundle of interventions has reliably reduced readmissions. Virtual wards, which use elements of hospital care in the community, have the potential to reduce readmissions, but have not yet been rigorously evaluated. OBJECTIVE: To determine whether a virtual ward-a model of care that uses some of the systems of a hospital ward to provide interprofessional care for community-dwelling patients-can reduce the risk of readmission in patients at high risk of readmission or death when being discharged from hospital. DESIGN, SETTING, AND PATIENTS: High-risk adult hospital discharge patients in Toronto were randomly assigned to either the virtual ward or usual care. A total of 1923 patients were randomized during the course of the study: 960 to the usual care group and 963 to the virtual ward group. The first patient was enrolled on June 29, 2010, and follow-up was completed on June 2, 2014. INTERVENTIONS: Patients assigned to the virtual ward received care coordination plus direct care provision (via a combination of telephone, home visits, or clinic visits) from an interprofessional team for several weeks after hospital discharge. The interprofessional team met daily at a central site to design and implement individualized management plans. Patients assigned to usual care typically received a typed, structured discharge summary, prescription for new medications if indicated, counseling from the resident physician, arrangements for home care as needed, and recommendations, appointments, or both for follow-up care with physicians as indicated. MAIN OUTCOMES AND MEASURES: The primary outcome was a composite of hospital readmission or death within 30 days of discharge. Secondary outcomes included nursing home admission and emergency department visits, each of the components of the primary outcome at 30 days, as well as each of the outcomes (including the composite primary outcome) at 90 days, 6 months, and 1 year. RESULTS: There were no statistically significant between-group differences in the primary or secondary outcomes at 30 or 90 days, 6 months, or 1 year. The primary outcome occurred in 203 of 959 (21.2%) of the virtual ward patients and 235 of 956 (24.6%) of the usual care patients (absolute difference, 3.4%; 95% CI, -0.3% to 7.2%; P = .09). There were no statistically significant interactions to indicate that the virtual ward model of care was more or less effective in any of the prespecified subgroups. CONCLUSIONS AND RELEVANCE: In a diverse group of high-risk patients being discharged from the hospital, we found no statistically significant effect of a virtual ward model of care on readmissions or death at either 30 days or 90 days, 6 months, or 1 year after hospital discharge. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01108172.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".