Utility of eConsults for COVID-19 vaccine-related concerns in Ontario: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: The Champlain BASE™ and Ontario eConsult services are virtual platforms that serve to facilitate contact between primary care providers and specialists across Ontario, relaying patient-specific questions to relevant specialists via a secure web-based platform. Despite ample evidence regarding the general effectiveness of these platforms, their utility as it pertains to clinical concerns regarding COVID-19 vaccines has not yet been explored. METHODS: We performed a cross-sectional descriptive analysis of COVID-19 vaccine related eConsults on Ontario patients completed by five allergy specialists between February and October of 2021. 4318 COVID-19 vaccine-related eConsults were completed in total during this time; with 1857 completed by the five allergists participating in this analysis. Question types/content were categorized using a taxonomy developed through consensus on a weighted monthly sample of 499 total cases. Data regarding whether external resources were required to answer each eConsult, impact on primary care provider referral decisions, and allergy consultant response times were collected. A 2-question survey was completed by primary care providers following eConsultation and results were collected. RESULTS: 41.08% of eConsults received involved safety concerns regarding COVID-19 vaccine administration in the setting of prior allergic disease and another 36.1% involved a potential reaction the first dose of a COVID-19 vaccine. 72.1% of eConsults were answered by specialist without needing external resources, and only 9.8% of all eConsults received resulted in a recommendation for formal in-person referral to Clinical Immunology & Allergy specialist or another subspecialty. Average time to complete eConsult was 16.4 min, and 79.7% of PCP eConsult queries which would have traditionally resulted in formal consultation were resolved based on advice provided in the eConsult without need for in-person assessment. CONCLUSIONS: Our study demonstrates the utility of the eConsult service as it pertains to COVID-19 vaccine-related concerns. The eConsult platform proved an effective tool in diverting the need for in-person assessment by an Allergist or other medical specialty. This is significant given the large volume of eConsults completed by Allergists, and demonstrates the impact of an effective electronic delivery of care model during a time of strained resources and public health efforts directed at mass vaccination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".