A Systematic Review of Asynchronous, Provider-to-Provider, Electronic Consultation Services to Improve Access to Specialty Care Available Worldwide
Bibliographic record
Abstract
BACKGROUND: Electronic consultation (eConsult) is an asynchronous electronic communication tool allowing primary care providers to obtain a specialist consultant's expert opinion in a timely manner, thereby offering a potential solution to excessive wait times for specialist care, which remain a serious concern in many countries. INTRODUCTION: Our 2014 review of eConsult services demonstrated feasibility and high acceptability among patients and providers. However, gaps remain in knowledge regarding eConsult's impact on system costs and patient outcomes. MATERIALS AND METHODS: Following the PRISMA guidelines, we conducted a systematic review in May 2017 of English and French literature on OVID Medline, EMBASE, ERIC, and CINAHL databases, examining all studies on eConsult services published since our previous review. The Quadruple Aim Framework was used to synthesize outcomes. Articles reporting on the impact of eConsult on access, patient safety and satisfaction, utilization rates, clinical workflow, and continuing medical education were analyzed using a narrative synthesis approach. RESULTS: The initial search yielded 1,021 results, 50 of which were included on abstract and received a quality assessment and full text review. Of these, 43 were included in our final analysis. Results demonstrated the worldwide presence of eConsult services in North America and countries beyond, including Brazil, Australia, Spain, and The Netherlands. The breadth of specialty services offered has greatly expanded beyond dermatology and includes cardiology, nephrology, and hematology among others. Overall impact on access measures, acceptability, cost, and provider satisfaction remain positive. There is limited research on population health outcomes of morbidity and mortality. CONCLUSIONS: The availability of eConsult services has spread both geographically and in terms of specialty services offered. By allowing for a greater population to be served, access to care is being improved; however, long-term impact should continue to be assessed with a focus on patient safety, morbidity, mortality, and cost effectiveness metrics.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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, 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".