Evaluation of an Electronic Consultation Service for COVID-19 Care
Bibliographic record
Abstract
PURPOSE: COVID-19 has increased the need for innovative virtual care solutions. Electronic consultation (eConsult) services allow primary care practitioners to pose clinical questions to specialists using a secure remote application. We examined eConsult cases submitted to a COVID-19 specialist group in order to assess usage patterns, impact on response times and referrals, and the content of clinical questions being asked. METHODS: This was a mixed-methods analysis of eConsult cases submitted between March and September 2020 in Ontario, Canada to 2 services. We performed a descriptive analysis of the average response time and the total time spent by the specialist for eConsults. Primary care practitioners completed a post-eConsult questionnaire that asked about the outcome of the eConsult. We performed an inductive and deductive content analysis of a subset of cases to identify common themes among the clinical questions asked. RESULTS: A total of 208 primary care practitioners submitted 289 eConsult cases. The median specialist response time was 0.6 days (range = 3 minutes to 15 days); the average time spent by specialists per case was 16 minutes (range = 5 to 59 minutes). In 69 cases (24%), the eConsult enabled avoidance of a face-to-face referral. Content analysis of 51 cases identified 5 major themes: precautions for high-risk and special populations, diagnostic clarification and/or need for COVID-19 testing, guidance on self-isolation and return to work, guidance on personal protective equipment, and management of chronic symptoms. CONCLUSIONS: This study demonstrates the considerable potential of eConsults during a pandemic as our service was quickly implemented across Ontario and resulted in primary care practitioners' rapid and low-barrier access to specialist input.
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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 | 0.022 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".