Advancing a virtual home hospital: a blueprint for development and expansion
Bibliographic record
Abstract
BACKGROUND: The rising demand for hospitals has spurred increased interest in adopting virtual home hospital (VHH) care models. Development in this area often uses rigid research methods. This study describes a dynamic approach to constructing a VHH and outlines the progress over 5 years. METHODS: In 2018, a multicentre VHH was developed in Edmonton, Alberta, Canada, using an innovation lab approach, fostering collaboration among healthcare stakeholders for design, prototyping and testing. Over a 5-year period (2018-2022), the VHH underwent trial and adaptation using the Model for Improvement and the Dynamic Sustainability Framework, refining integrated care for a broader patient population. Within the VHH, patients received acute, hospital-level care at home, using technology, existing services and hospital and community personnel. Outcome measures included number of patient cohorts, staffing numbers, patients served, capacity and hospitals/health centres supported. RESULTS: Over 5 years, the VHH expanded from 2 to 15 staff members, from 14 to 25 physicians, from 45 to 870 total patients served, from 10- to 75-patient capacity and from serving 1 hospital to 6 hospitals and 1 health centre. The VHH advanced by transitioning from telehealth to digital remote patient monitoring, involving additional community partners, extending operating hours, diversifying admission and referral pathways and improving patient monitoring. CONCLUSION: A VHH has the potential to bridge the gap between hospital and community care and to become a permanent healthcare delivery model that supports continuity of patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".