The Use of Electronic Consultations in Outpatient Surgery Clinics: Synthesized Narrative Review
Bibliographic record
Abstract
BACKGROUND: Electronic consultations (eConsults) are an increasingly used form of telemedicine that allows a nonspecialist clinician to seek specialist advice remotely without direct patient-specialist communication. Surgical clinics may see benefits from such forms of communication but face challenges with the need for intervention planning. OBJECTIVE: We aimed to use the Quadruple Aim Framework to integrate published knowledge of surgical outpatient eConsults with regard to efficacy, safety, limitations, and evolving use in the era of COVID-19. METHODS: We systematically searched for relevant studies across four databases (Ovid MEDLINE, Embase, Scopus, and Web of Science) on November 4, 2021, with the following inclusion criteria: English language, published in the past 10 years, and data on the outcomes of outpatient surgical eConsults. RESULTS: A total of 363 studies were screened for eligibility, of which 33 (9.1%) were included. Most of the included studies were from the United States (23/33, 70%) and Canada (7/33, 21%), with a predominant multidisciplinary focus (9/33, 27%). Most were retrospective audits (16/33, 48%), with 15% (5/33) of the studies having a prospective component. CONCLUSIONS: The surgical eConsult studies indicated a possible benefit for population health, promising safety results, enhanced patient and clinician experience, and cost savings compared with the traditional face-to-face surgical referral pathway. Their use appeared to be more favorable in some surgical subspecialties, and the overall efficacy was similar to that of medical subspecialties. Limited data on their long-term safety and use during the COVID-19 pandemic were identified, and this should be the focus of future research.
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".