Specialists accessing specialty advice: Evaluating utilization, benefits, and impact of care of an e-consultation service
Bibliographic record
Abstract
Introduction The usual referral pathway is from a primary care provider (PCP) to a specialist; however, specialists also refer to and consult with other specialists. Electronic consultation (eConsult) allows clinicians to submit questions on behalf of patients to specialists to receive timely advice. Most eConsult studies in the past have examined questions asked from PCPs to specialists. This study investigates the utilization of specialists submitting clinical questions to other specialists through the Ontario eConsult Service and identifies use-case scenarios where specialist-to-specialist eConsult may be beneficial. Methods A retrospective, descriptive, cross-sectional analysis of eConsults submitted by specialists through the Ontario eConsult Service for 24 months (March 2019 to February 2021). Utilization data is collected automatically by the service, including specialty referred to, time billed, region, and results from a closeout survey which includes the referral outcome of the eConsult and the utility to the submitting clinician. Results 4% ( n = 3285) of all eConsults sent within the study period were specialist-to-specialist, with the others being sent by a PCP. The number of specialist-to-specialist eConsults grew 120% following the onset of the COVID-19 pandemic. The top three specialties that submitted eConsults were pediatrics, internal medicine, and endocrinology. The top three specialties that specialists submitted to were dermatology, neurology, and hematology. A face-to-face referral was avoided in 69% of referrals. Conclusion Evaluating the utilization patterns of specialist-to-specialist eConsults allows us to better understand and expand the scope of eConsult services, which have traditionally been thought of as a workflow between a PCP and a specialist.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".