Concordance between Virtual Physician advice and subsequent health service utilization for callers to a nurse-managed provincial health information telephone service: a time-to-event analysis
Bibliographic record
Abstract
Abstract Background: British Columbia 8-1-1 callers who are advised by a nurse to seek urgent medical care can be referred to virtual physicians (VPs) for supplemental assessment and advice. Prior research indicates callers’ subsequent health service use may diverge from VP advice. We sought to 1) estimate concordance between VP advice and subsequent health service use, and 2) identify factors associated with concordance to understand potential drivers of discordant cases.Methods: We linked relevant provincial administrative databases to obtain inpatient, outpatient, and emergency service use by callers. We developed operational definitions of concordance collaboratively with researcher, patient, VP, and management perspectives. We used Kaplan-Meier curves to describe health service use post-VP consultation and Cox regression to estimate the association of caller factors (rurality, demography, attachment to primary care) and call factors (reason, triage level, time of day) with concordance as hazard ratios.Results: We analyzed 17,188 calls from November 16, 2020 to April 30, 2021. Callers advised to attend an emergency department (ED) immediately were the most concordant (73%) while concordance was lowest for those advised to seek Family Physician (FP) care either immediately (41%) or within 7 days (47%). Callers unattached to FPs were less likely to schedule an FP visit (hazard ratio = 0.76 [95%CI: 0.68–0.85]). Rural callers were less likely to attend an ED within 48 hours when advised to go immediately (0.53 [95%CI:0.46–0.61]) compared to urban callers. Rural callers advised to see an FP, either immediately (1.28 [95%CI:1.01–1.62]) or within 7 days (1.23 [95%CI: 1.11–1.37]), were more likely to do so than urban callers.Interpretation: Concordance between VP advice and subsequent caller health service use varies substantially by category of advice and caller rurality. Concordance with advice to “Go to ED” is high overall but to access primary care is below 50%, suggesting potential issues with timely access to FP care. Future research from a patient/caller centered perspective may reveal additional barriers and facilitators to concordance.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".