Designing for Patient-Centered Care and Equity in Virtual Hospital-at-Home Models: Quality Improvement Initiative Using Experience-Based Co-Design
Bibliographic record
Abstract
Background: The rapid expansion of virtual care during COVID-19 accelerated the development of virtual hospital-at-home models, which deliver hospital-level care in patients' homes through remote monitoring, virtual communication, and in-person support when required. While the virtual hospital-at-home model offers the potential to improve patient-centered care and health equity, rapid implementation often overlooks culturally diverse and underserved populations, including South Asian communities who experience disproportionate chronic disease burden and barriers to accessing culturally relevant services. Strategies are needed to ensure equitable design and adoption of virtual hospital-at-home models. Objective: This study used an experience-based co-design (EBCD) approach to engage patients, caregivers, clinicians, and community organizations in shaping a regional virtual hospital-at-home strategy within the Fraser Health Authority, British Columbia, Canada. The aim was to identify barriers, facilitators, and equity-focused solutions to inform future implementation. Methods: We conducted a five-stage EBCD quality improvement process in the Fraser Health Authority, British Columbia, including (1) forming a multidisciplinary steering committee, (2) reviewing health care provider experiences, (3) interviewing South Asian patients and caregivers, (4) hosting a co-design workshop to develop solutions, and (5) sharing back findings. Results: Participants identified barriers, including digital literacy, language, and trust in virtual care. The co-designed solutions focused on culturally tailored education, hybrid digital training, caregiver inclusion, and community-driven engagement strategies. Conclusions: EBCD enabled the development of inclusive and actionable strategies to improve virtual hospital-at-home services. The findings highlight the importance of ongoing community collaboration to ensure equity in virtual care innovation.
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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 | 0.049 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".