Utilization, Benefits, and Impact of an e-Consultation Service Across Diverse Specialties and Primary Care Providers
Bibliographic record
Abstract
BACKGROUND: Access to specialist advice remains a barrier for primary care providers (PCPs) and their patients. Virtual consultations have been used to expedite access. There are few studies demonstrating the utilization and impact of such services. We established a regional e-consultation service that was used across a wide range of specialty services and PCPs. MATERIALS AND METHODS: We prospectively collected all e-consultations submitted from April 1, 2011 to June 30, 2012. Utilization data collected included number of e-consultations submitted, specialist response, and time required for the specialist to complete the e-consultation. Perceived benefit to the PCPs and their patients and the impact on care delivery were determined from a close-out survey. RESULTS: Fifty-nine PCPs submitted 406 e-consultations to 16 specialty services. The specialist provided an answer without requesting further information in 89% of cases, with >90% of cases taking <15 min for the specialist to complete. Seventy-five percent of cases were answered in <3 days. The service was perceived as highly beneficial to providers and patients in>90% of cases. In 43% of submitted cases a traditional referral was originally contemplated but was now avoided. CONCLUSIONS: We successfully implemented an e-consultation service across diverse PCPs and specialty services that was highly valued. Almost half of referrals submitted would have required a face-to-face consultation if the service had not been available. Thus e-consultation has tremendous potential for improving access to specialist advice in a much more timely manner than the traditional referral-consultation process.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".