Integrating research evidence into virtual healthcare service programming: a quality improvement analysis of healthcare utilization and series of rapid umbrella reviews
Bibliographic record
Abstract
Abstract Background The integration of virtual solutions in healthcare has shown promise in improving access and reducing strain on hospital services. To maximize impact, healthcare authorities should understand what populations to prioritize in virtual healthcare service deployment as well as the research evidence for virtual care services for those populations. This study aims to support the Fraser Health (FH) Authority in prioritizing the implementation of virtual health, focusing on patient populations that would benefit most. “Patient profiles” were created by analyzing admission, readmission rates, and length of stay based on chronic conditions across FH sites. Using the Pabon Lasso Model for visualization, chronic conditions were categorized into zones to identify those with the greatest acute load. Rapid umbrella reviews were conducted for heart failure, COPD, and diabetes to identify evidence-based virtual care solutions for these high-utilization populations. The resulting knowledge products offered user-friendly, high-level overviews of the evidence for decision-making. Results Heart failure, COPD, diabetes, schizophrenia, and anxiety disorders were identified as top chronic conditions with highest acute loads. Rapid umbrella reviews indicated potential benefits of the following virtual care interventions for heart failure, COPD, and diabetes: remote patient monitoring (RPM), eLearning, virtual support (via phone calls or video conferencing), tele-rehabilitation, and text messaging. Conclusion Integration of virtual care services has the potential to revolutionize healthcare but requires careful planning and consideration of barriers. Patient profiles and rapid umbrella reviews offer a comprehensive approach to inform prioritization and implementation. RPM, eLearning, virtual support, tele-rehab, and text messaging showed promise for specific chronic conditions.
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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 | 0.202 | 0.577 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.086 | 0.071 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".