Electronic referral systems in health care: a scoping review
Bibliographic record
Abstract
Amilcar Azamar-Alonso,1 Andrew P Costa,1–3 Lori-Anne Huebner,4,5 Jean-Eric Tarride1–2,61Center for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Ontario, Canada; 2Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada; 3Department of Medicine, McMaster University, Hamilton, Ontario, Canada; 4The eHealth Centre of Excellence, Waterloo, Ontario, Canada; 5Department of Family Medicine, McMaster University, Hamilton, Ontario, Canada; 6Programs for Assessment of Technology in Health (PATH), The Research Institute of St. Joe’s, St Joseph’s Healthcare Hamilton, Hamilton, Ontario, CanadaIntroduction: Electronic referral (eReferral) systems have been designed with the dual purpose of decreasing wait times and improving workflow efficiency. Evidence about the clinical and economic value enabled through the use of eReferral systems is limited. Our objective was to review the evidence base for the effectiveness and cost-effectiveness of eReferral systems. This review is part of a bigger project to inform the economic benefits of a regional eReferral implementation program.Material and methods: A systematic search was conducted to capture the available literature on the effectiveness and cost-effectiveness of eReferral system interventions. Evaluation of eReferral system for cost or outcome(s) were included. Strictly e-consultation systems were excluded. We only included publications in English.Results: We found 274 citations. After removing duplicates and conducting levels one and two screenings, nine publications qualified. Results were divided into four categories: cost or cost-effectiveness analysis, changes in workflow efficiency, the quantity of referrals, and the quality of referrals. A full economic evaluation, conducted in Denmark, found that an eReferral system was cost-effective compared with a paper-based referral system. Of the other eight studies, three demonstrated positive changes in referral processing; two evaluated changes in the quality of the referrals, and three evaluated if the eReferral system increased the quantity of referrals.Discussion: The evidence base on the effectiveness of eReferral systems to improve communication between primary care and specialists and to decrease wait times is positive but limited. Economic evaluations are needed to examine the clinical and economic value of eReferral systems in health care.Keywords: electronic referrals, eHealth systems, review, costs, cost-effectiveness
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".