Virtual Care, What Are We Measuring and What Should We Measure? Scoping Review of Reviews
Bibliographic record
Abstract
BACKGROUND: Virtual care is here to stay but there remains no comprehensive measurement framework to guide evaluation of its impacts, to inform policy decisions and optimization of practice. OBJECTIVE: The aim of our study was to conduct a scoping review of reviews to synthesize measures related to virtual care evaluation across clinical conditions and contexts to identify gaps in current evaluation measures, and to inform the development of recommendations for future work. METHODS: Citations published from 2015-2023 were retrieved from Medline, Cochrane Database of Systematic Reviews, Embase, Emcare, Scopus, CINAHL and Web of Science, utilizing search terms grouped by key concept (virtual care and evaluation/ quality measurement). Measures were defined as any quantitative or qualitative evaluation of performance or impact of virtual care on processes, outcomes or systems. Articles were excluded if they were not a literature review (primary results, commentaries, letters, protocols), dealt exclusively with pediatric populations, published in a language other than English, or were abstract only. Measures from retained articles (1,233) were thematically grouped against the Proctor Implementation Research Outcomes framework. The study was reported according to the PRISMA guideline extension for scoping reviews. RESULTS: There has been substantial growth in the virtual care literature, particularly since the start of the COVID-19 pandemic. The majority of articles (73.0%; 900/1233) evaluated client outcomes, including satisfaction with virtual care, usability or functionality of platforms, and/or clinical outcomes. Relative to the other domains of the Proctor framework, implementation measures were poorly defined, and many of the measures were proxy rather than direct measures. Despite the potential impacts of virtual care on health equity, most studies examining health equity were purely qualitative. Measures of safety, privacy and security of virtual care were sparse and poorly defined. Caregivers play an important role in facilitating virtual visits and providing informal technical support; however, few studies examined implementation or satisfaction with virtual care from the perspective of caregivers. Additionally, clinician experience and acceptance of virtual care has implications for availability and adoption; however, relative to patients, few articles examined the clinician perspective. CONCLUSIONS: Our study highlights gaps in current evaluations of virtual care. Work is needed to improve the quality and standardization of virtual care evaluation to ensure reproducibility, generalizability and comparability of findings. Additionally, better compliance with existing measure definitions and conventions should extend to virtual care. Finally, additional theoretical work is needed to standardize and conceptually frame future virtual care evaluations. Future studies should include both the caregiver and clinician as unique perspectives in evaluations, and should embed systematic evaluations of the impact of social determinants of health on virtual care access, adoption, and perspectives of care.
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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 | 0.150 | 0.467 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".