Unique Educational Opportunities for PCPs and Specialists Arising From Electronic Consultation Services
Bibliographic record
Abstract
Health care reform should be driven by the goals of better patient experience, improved population health, lower per capita costs, and improved provider satisfaction. Electronic consultation (eConsult) services have been adopted by several jurisdictions in the United States, Canada, and Europe to improve access to specialists by primary care providers (PCPs) and are being heralded as a key component for delivery of coordinated care. The primary intent of an eConsult service is to provide PCPs with efficient, timely, direct access to specialist expertise to help guide the management of their patients, reduce the need for unnecessary face-to-face specialty consultations, and improve the quality of the initial face-to-face consultation when needed, through the preconsultative communication.In addition to improving access to care, eConsult services have been praised by PCPs and specialists for their educational value, in particular their ability to enrich practice-based learning. Less recognized, but equally important from the educational perspective, include the abilities of eConsult programs to promote reflection by PCPs and specialists, improve collegiality and professionalism between primary and specialist care, inform continuing professional development activities and maintenance of certification, and enhance training programs' teaching of effective communication and care coordination.As eConsult services become increasingly available, the medical community must leverage the educational opportunities inherent in eConsult programs to further improve the delivery of coordinated specialty care. The educational role of eConsults should be considered as a priority outcome in their evaluation and must be highlighted and optimized in next iterations of eConsult systems design.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".