Ontario family physicians’ experiences with digital self-triage patient navigator tools: Lessons from the COVID-19 pandemic for digital-first approaches in primary care
Bibliographic record
Abstract
Abstract Aim To explore Ontario primary care physician and nurse practitioner experiences with, and perceptions of, digital self-triage patient navigator tools, and to identify implications for future design and implementation. Background Purported benefits of digital self-triage tools include triage any time/place, easily updated triage logic, and reduced primary care demand. The COVID-19 pandemic provided a particularly important context for studying digital self-triage; primary care clinicians are an important group to learn from, but their perspectives have received little attention. Methods We interviewed fourteen Ontario family physicians/nurse practitioners in Spring 2021 using semi-structured interviews. We asked about experiences with, and perceptions of, digital self-triage tools, including the province’s centralized tool. Interview data were analyzed using reflexive thematic analysis. Findings Participants had limited awareness of the provincial tool. They had greater awareness of locally developed tools that were intended to mimic the provincial tool’s logic, but that were integrated into primary care. Respondents noted local tools were embedded in EMR systems, enabled scheduling, and were designed by others they knew and trusted. Respondents preferred self-assessment tools positioned as adjuncts to, rather than replacements for, clinical care, due to concerns about coordination and continuity of care. Concerns centred on tool efficacy, user input and interpretation, and access and equity. Conclusions This study highlights a gap between stand-alone digital self-triage tools (meant to reduce demand on primary care) and primary care. Locally developed tools intended to mimic that centralized tool, but integrated into primary care, are preferred. This aids primary care coordination and workflows but undermines ability to provide standardized triage anywhere, at any time, via a centralized tool, key benefits of digital self-triage. Future self-triage tools may be more effective if centrally maintained, but interoperable with local systems, developed with physician input, and customizable to support clinical workflows and continuity of care.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".