Health system utilization following medical advice from Alberta’s Virtual MD: a descriptive analysis
Bibliographic record
Abstract
OBJECTIVES: Alberta's Virtual MD program was established to enhance nurse tele-triage and divert low-acuity patients from the emergency department (ED). This study describes the use of Virtual MD and its impact on healthcare utilization. METHODS: Demographic and clinical characteristics of Virtual MD patients were compared with Health Link 811 callers and the overall Alberta population between April 1, 2022, and March 31, 2023. Virtual MD recommendations included seeing a primary care provider, going to ED/urgent care, and self-management at home. Concordance with recommendations was determined using linked health administrative data. RESULTS: Virtual MD patients (n = 19,312) had a mean age of 34.8 years and were mostly female (62.3%). Compared to Health Link 811 callers, Virtual MD patients were slightly older (≥ 55 years) (20.8% vs. 25.0%). Of patients called within 4 h, 55.7% visited primary care within 14 days as advised, 60.0% visited ED within 2 days as advised and 52.5% of those advised to self-manage care at home did not use any healthcare within 14 days. Those advised to seek primary care had a higher odds [OR = 1.65 (95%CI: 1.24-2.21)] of family practice-sensitive conditions when they presented at ED compared to those advised to seek ED care. Hospitalization within 2 weeks was lower for patients advised to see primary care compared to those advised to see ED [4 h callback: OR = 0.33 (95%CI: 0.26 - 0.43), 24 h callback: OR = 0.15 (95%CI 0.08 - 0.28)]. CONCLUSION: Virtual MD effectively triaged patients, with over half following through on recommendations to see primary care, see ED, or self-manage care at home. Patients referred to primary care, but instead choosing to visit ED, were more likely to present with family practice-sensitive conditions, demonstrating appropriateness of the initial primary care advice. Overall, the Virtual MD service enables patients to access more appropriate levels of care for their healthcare needs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".