Multidisciplinary Case Management for Patients at High Risk of Hospitalization: Comparison of Virtual Ward Models in the United Kingdom, United States, and Canada
Bibliographic record
Abstract
Virtual wards are a model for delivering multidisciplinary case management to people who are at high predicted risk of unplanned acute care hospitalization. First introduced in Croydon, England, in 2006, this concept has since been adopted and adapted by health care organizations in other parts of the United Kingdom and internationally. In this article, the authors review the model of virtual wards as originally described-with its twin pillars of (1) using a predictive model to identify people who are at high risk of future emergency hospitalization, and (2) offering these individuals a period of intensive, multidisciplinary preventive care at home using the systems, staffing, and daily routines of a hospital ward. The authors then describe how virtual wards have been modified and implemented in 6 sites in the United Kingdom, United States, and Canada where they are subject to formal evaluation. Like hospital wards, virtual wards vary in terms of patient selection, ward configuration, staff composition, and ward processes. Policy makers and researchers should be aware of these differences when considering the evaluation results of studies investigating the cost-effectiveness of virtual wards.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".