Physician-Led Synchronous Telemedicine Compared to Face-To-Face Care in Primary Care: A Systematic Review
Bibliographic record
Abstract
The COVID-19 crisis rapidly introduced telemedicine as the predominate modality to deliver healthcare however this change has not received attention in primary care settings and the health-related impacts are unknown. The study's objective was to explore the effects of physician-led synchronous telemedicine compared to face-to-face care delivered in the primary care setting on healthcare system use and attributes of primary care as reported in recent studies. We performed a comprehensive literature search in five databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, PsycInfo) and critical appraisal using the Joanna Briggs Institute tools. Of 6,247 studies identified, 157 studies underwent full text review, and 19 studies were included. Most studies were conducted in the U.S. (78.9%) and used video and telephone telemedicine (57.9%). An outcome-based qualitative description and narrative synthesis showed similar or fewer emergency department visits, hospital visits, and prescribing, and fewer diagnostic tests and imaging for telemedicine visits compared to face-to-face care. Our systematic review fills a gap in the literature on telemedicine in primary care settings however our results need to be interpreted cautiously given studies' susceptibility to selection bias, confounding, and limited applicability to other health systems and time periods.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".