Patient safety indicators for virtual consultations in primary care: A systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Virtual consultations are being increasingly incorporated into routine primary care, as they offer better time and geographical flexibility for patients while also being cost-effective for both patients and service providers. At the same time, concerns have been raised about the extent to which virtual care is safe for patients. As of now, there is no validated methodology for evaluating the safety nuances and implications of virtual care. This study aims to identify patient safety indicators that could be used to evaluate the safety of virtual consultations in primary care. METHODS: A literature search will be performed in Ovid MEDLINE/PubMed, Embase, and Cochrane Library for relevant articles published over the last 10 years (2014-2024). The systematic review will include randomized and non-randomized controlled trials and observational studies with adult populations that compare synchronous patient-provider virtual consultations (telephone or video) or multicomponent interventions involving synchronous remote consultations with face-to-face consultations. The outcome of interest will be patient safety indicators extracted from the studies. The quality of randomized controlled trials will be assessed with the Cochrane Risk of Bias Tool, and the Newcastle-Ottawa Scale will be used to analyze risk of bias in observational studies. DISCUSSION: Considering the growing adoption of virtual medical care worldwide, a robust and comprehensive evaluation of its safety and quality is now a system-wide priority. Therefore, one of the primary strengths of this proposed systematic review is its focus on a topic of great importance and timeliness, specifically addressing the existing knowledge gap in this area. By publishing this protocol, we demonstrate the transparency and reliability of our research strategy and aim to minimize the risk of selection bias. Potential limitations include the heterogeneity of measures and outcomes, as well as a lower-than-expected number of studies in subgroup analyses, which may negatively influence the statistical significance in data synthesis. TRIAL REGISTRATION: PROSPERO registration number: CRD42023464878.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.080 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.018 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.006 |
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".