What makes a high-quality electronic consultation (eConsult)? A nominal group study
Bibliographic record
Abstract
INTRODUCTION: Poor communication between health professionals can compromise patient safety, yet specialists rarely receive feedback on their written communication. Although worldwide implementation of electronic consultation (eConsult) services is rising rapidly, little is known about the features of effective communication when specialists provide online advice to primary care providers (PCP). To inform efforts to ensure and maintain high-quality communication via eConsult, we aim to identify features of high-quality eConsult advice to incorporate into an assessment tool that can provide specialists with feedback on their correspondence. METHODS: Initial items for the tool were generated by PCPs and specialists using the nominal group technique (NGT). Invited PCPs were above-median eConsult users between July 2016 and June 2017. Specialists were purposively recruited to represent the range of available specialties. Participants individually wrote down items they felt should be included in the tool. A moderator with consensus group expertise then led a round-robin discussion for each item. Items were ranked anonymously and included if highly-ranked by over 70% of participants. RESULTS: Eight PCPs (six family physicians, two nurse practitioners) and three specialists (dermatology, hematology, pediatric orthopedics) produced 49 items that were refined to 14 after group discussion and two rounds of ranking. Highly-ranked items encompassed specific, up-to-date, patient-individualized, and practical advice that the PCP could implement. DISCUSSION: Features of high-quality eConsult correspondence derived from consensus methods highlight similarities and differences between face-to-face consultation letters and eConsult. Our findings could be used to inform feedback and education for eConsult specialists on their advice to PCPs.
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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 | 0.104 | 0.369 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".