Uses of Virtual Care in Primary Care: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic catalyzed an uptake in virtual care. However, the rapid shift left unanswered questions about the impact of virtual care on the quality of primary care and its appropriateness and effectiveness. Moving forward, health care providers require guidance on how best to use virtual care to support high-quality primary care. OBJECTIVE: This study aims to identify and summarize clinical studies and systematic reviews comparing virtual care and in-person care in primary care, with a focus on how virtual care can support key clinical functions such as triage, medical assessment and treatment, counseling, and rehabilitation in addition to the management of particular conditions. METHODS: We conducted a scoping review following an established framework. Comprehensive searches were performed across the following databases: Embase, MEDLINE, PsycInfo, Emcare, and Cochrane Database of Systematic Reviews. Other well-known websites were also searched. PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines were followed. Articles were selected by considering article type, language, care provided, intervention, mode of care delivery, and sample size. RESULTS: A total of 13,667 articles were screened, and 219 (1.6%) articles representing 170 studies were included in the review. Of the 170 studies included, 142 (83.5%) were primary studies, and 28 (16.5%) were systematic reviews. The studies were grouped by functions of primary care, including triage (16/170, 9.4%), medical assessment and treatment of particular conditions (63/170, 37.1%), rehabilitation (17/170, 10%), and counseling (74/170, 43.5%). The studies suggested that many primary care functions could appropriately be conducted virtually. Virtual rehabilitation was comparable to in-person care and virtual counseling was found to be equally effective as in-person counseling in several contexts. Some of the studies indicated that many general primary care issues could be resolved virtually without the need for any additional follow-up, but data on diagnostic accuracy were limited. Virtual triage is clinically appropriate and led to fewer in-person visits, but overall impact on efficiency was unclear. Many studies found that virtual care was more convenient for many patients and provided care equivalent to in-person care for a range of conditions. Studies comparing appropriate antibiotic prescription between virtual and in-person care found variable impact by clinical condition. Studies on virtual chronic disease management observed variability in impact on overall disease control and clinical outcomes. CONCLUSIONS: Virtual care can be safe and appropriate for triage and seems equivalent to in-person care for counseling and some rehabilitation services; however, further studies are needed to determine specific contexts or medical conditions where virtual care is appropriate for diagnosis, management outcomes, and other functions of primary care. Virtual care needs to be adapted to fit a new set of patient and provider workflows to demonstrate positive impacts on experience, outcomes, and costs 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 | 0.028 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".