Electronic Consultation in Primary Care Between Providers and Patients: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Governments and health care providers are keen to find innovative ways to deliver care more efficiently. Interest in electronic consultation (e-consultation) has grown, but the evidence of benefit is uncertain. OBJECTIVE: This study aimed to assess the evidence of delivering e-consultation using secure email and messaging or video links in primary care. METHODS: A systematic review was conducted on the use and application of e-consultations in primary care. We searched 7 international databases (MEDLINE, EMBASE, CINAHL, Cochrane Library, PsycINFO, EconLit, and Web of Science; 1999-2017), identifying 52 relevant studies. Papers were screened against a detailed inclusion and exclusion criteria. Independent dual data extraction was conducted and assessed for quality. The resulting evidence was synthesized using thematic analysis. RESULTS: This review included 57 studies from a range of countries, mainly the United States (n=30) and the United Kingdom (n=13). There were disparities in uptake and utilization toward more use by younger, employed adults. Patient responses to e-consultation were mixed. Patients reported satisfaction with services and improved self-care, communication, and engagement with clinicians. Evidence for the acceptability and ease of use was strong, especially for those with long-term conditions and patients located in remote regions. However, patients were concerned about the privacy and security of their data. For primary health care staff, e-consultation delivers challenges around time management, having the correct technological infrastructure, whether it offers a comparable standard of clinical quality, and whether it improves health outcomes. CONCLUSIONS: E-consultations may improve aspects of care delivery, but the small scale of many of the studies and low adoption rates leave unanswered questions about usage, quality, cost, and sustainability. We need to improve e-consultation implementation, demonstrate how e-consultations will not increase disparities in access, provide better reassurance to patients about privacy, and incorporate e-consultation as part of a manageable clinical workflow.
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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 | 0.012 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".