Health care utilization and outcomes of patients seen by virtual urgent care versus in-person emergency department care
Bibliographic record
Abstract
BACKGROUND: Virtual urgent care (VUC) is intended to support diversion of patients with low-acuity complaints and reduce the need for in-person emergency department visits. We aimed to describe subsequent health care utilization and outcomes of patients who used VUC compared with similar patients who had an in-person emergency department visit. METHODS: We used patient-level encounter data that were prospectively collected for patients using VUC services provided by 14 pilot programs in Ontario, Canada. We linked the data to provincial administrative databases to identify subsequent 30-day health care utilization and outcomes. We defined 2 subgroups of VUC users; those with a documented prompt referral to an emergency department by a VUC provider, and those without. We matched patients in each cohort to an equal number of patients presenting to an emergency department in person, based on encounter date, medical concern and the logit of a propensity score. For the subgroup of patients not promptly referred to an emergency department, we matched patients to those who were seen in an emergency department and then discharged home. RESULTS: Of the 19 595 patient VUC visits linked to administrative data, we matched 2129 patients promptly referred to the emergency department by a VUC provider to patients presenting to the emergency department in person. Index visit hospital admissions (9.4% v. 8.7%), 30-day emergency department visits (17.0% v. 17.5%), and hospital admissions (12.9% v. 11.0%) were similar between the groups. We matched 14 179 patients who were seen by a VUC provider with no documented referral to the emergency department. Patients seen by VUC were more likely to have a subsequent in-person emergency department visit within 72 hours (13.7% v. 7.0%), 7 days (16.5% v. 10.3%) and 30 days (21.9% v. 17.9%), but hospital admissions were similar within 72 hours (1.1% v. 1.3%), and higher within 30 days for patients who were discharged home from the emergency department (2.6% v. 3.4%). INTERPRETATION: The impact of the provincial VUC pilot program on subsequent health care utilization was limited. There is a need to better understand the inherent limitations of virtual care and ensure future virtual providers have timely access to in-person outpatient resources, to prevent subsequent emergency department visits.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".