The Utilization of Electronic Consultations (eConsults) to Address Emerging Questions Related to Long COVID-19 in Ontario, Canada: Mixed Methods Analysis
Bibliographic record
Abstract
Background: Long COVID is an often debilitating condition affecting millions of people. Its diverse clinical presentations make effective diagnosis and management at the primary care level difficult, while specialist services for long COVID face extensive wait times. An electronic consultation (eConsult) program in Ontario developed a long COVID specialist group to allow primary care providers (PCPs) prompt access to specialist advice for patients with long COVID. Objective: This study aims to assess patterns of service use, response times, impact, and clinical content of eConsult cases submitted to an eConsult long COVID specialist group in Ontario. Methods: This study is a mixed methods analysis of eConsults submitted by PCPs to the long COVID specialist group of 2 eConsult services (Champlain eConsult BASE and Ontario eConsult) between June 1, 2021, and July 31, 2022. Data sources included the use data collected automatically by the services, responses to a mandatory closeout survey, and the content of PCP questions and specialist responses (Champlain eConsult BASE service only). Clinical questions or responses were analyzed using 2 validated taxonomies. Descriptive statistics were used for survey responses and use data. Results: A total of 40 PCPs submitted 47 eConsults through Champlain eConsult BASE and 197 PCPs submitted 228 cases through Ontario eConsult. The median specialist response time was 0.6 (IQR 0.19-2.36; mean 1.7, SD 2.29) days. The 5 most common symptoms of long COVID were fatigue (14/47, 30%), dyspnea (7/47, 15%), cough (6/47, 13%), altered sense of smell (ie, anosmia and parosmia; 6/47, 13%), and cognitive changes (6/47, 13%). The five main question categories asked by PCPs were: (1) management of chronic symptoms of COVID-19, (2) need for additional work-up or follow-up testing, (3) community resources to support or manage patients with long COVID, (4) diagnostic clarification, and (5) guidance regarding COVID-19 vaccination. Conclusions: The long COVID groups provided rapid access to a multispecialty service that facilitated the avoidance of unnecessary face-to-face referrals. An assessment of eConsults highlighted 5 common question types, providing insight into potential gaps in knowledge among PCPs that could help guide medical education and policy.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".