Family Physician–to–Hospital Specialist Electronic Consultation and Access to Hospital Care
Bibliographic record
Abstract
Importance: Globally, health care systems face challenges in managing health care costs while maintaining access to hospital care, quality of care, and a good work balance for caregivers. Electronic consultations (e-consultations)-defined as asynchronous, consultative communication between family physicians and hospital specialists-may offer advantages to face these challenges. Objective: To provide a quantitative synthesis of the association of e-consultation with access to hospital care and the avoidance of hospital referrals. Evidence Review: A systematic search through PubMed, MEDLINE, and Embase was conducted. Eligible studies included original research studies published from January 2010 to March 2023 in English, Dutch, or German that reported on outcomes associated with access to hospital care and the avoidance of hospital referrals. Reference lists of included articles were searched for additional studies. Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) scores were assigned to assess quality of evidence. Findings: The search strategy resulted in 583 records, of which 72 studies were eligible for data extraction after applying exclusion criteria. Most studies were observational, focused on multispecialty services, and were performed in either Canada or the US. Outcomes on access to hospital care and the avoidance of referrals indicated that e-consultation was associated with improved access to hospital care and an increase in avoided referrals to the hospital specialist, although outcomes greatly differed across studies. GRADE scores were low or very low across studies. Conclusions and Relevance: In this systematic review of the association of e-consultation with access to hospital care and the avoidance of hospital referrals, results indicated that the use of e-consultation has greatly increased over the years. Although e-consultation was associated with improved access to hospital care and avoidance of hospital referrals, it was hard to draw a conclusion about these outcomes due to heterogeneity and lack of high-quality evidence (eg, from randomized clinical trials). Nevertheless, these results suggest that e-consultation seems to be a promising digital health care implementation, but more rigorous studies are needed; nonrandomized trial designs should be used, and appropriate outcomes should be chosen in future research on this topic.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".