A retrospective analysis of the use of electronic consultation in general internal medicine
Bibliographic record
Abstract
BACKGROUND: General internists in Canada are subspecialty providers in the inpatient and outpatient settings. Electronic consultations (eConsult) allow primary care providers (PCPs) to virtually consult specialists to address clinical questions. There is a paucity of literature examining the utility and benefits of eConsults by general internists. AIMS: To determine how an eConsult service is used to access general internists. METHODS: A retrospective cross-sectional analysis of internal medicine cases was completed between 1 January 2016 and 31 December 2019 via the ChamplainBASE eConsult service. Two authors derived and validated a general internal medicine (GIM)-specific taxonomy using the validated: (i) Taxonomy of Generic Clinical Questions; and (ii) Internal Classification for Primary Care. Two hundred seventy-six cases were coded following taxonomy validation. ChamplainBASE utilisation summary and closeout survey data were also analysed. RESULTS: eConsults were responded to in a median of 3.1 days and took 15 min to complete. The eConsult's helpfulness and educational value were rated as 4 to 5/5 and often provided advice for a new or additional course of action. In-person referral was avoided in 40% of cases. The majority of eConsults consisted of a single question (88%) related to diagnostic clarification. The median remuneration per eConsult was $50. CONCLUSIONS: The majority of eConsults to general internists sought diagnostic clarification and confirmed the view of general internists as expert diagnosticians. eConsults cost less than an in-person consultation and were viewed favourably by PCPs. Further research can consider the eConsult provider experience and whether eConsults should become a required part of GIM ambulatory practice.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".