Impact of virtual visits on primary care physician work flows
Bibliographic record
Abstract
OBJECTIVE: To understand the impact of virtual visits on primary care physician (PCP) work flows. DESIGN: Qualitative semistructured interviews. SETTING: Primary care practices within 5 regions in southern Ontario. PARTICIPANTS: Physicians representing primary care practices of various sizes and remuneration models (eg, capitation and fee-for-service models). METHODS: Interviews were conducted with PCPs involved in a large-scale pilot project implementing virtual visits (via a Web-based application) into clinical practices. Convenience and purposive sampling were used to recruit PCPs between January 2018 and March 2019. To obtain a representative sample, participants were sought from a variety of practice types and geographic regions. High and low users of virtual visits were included. Interviews were audiorecorded and transcribed. An inductive thematic analysis was used to identify prominent themes and subthemes. MAIN FINDINGS: Twenty-six physicians were interviewed (n=15 using convenience sampling and n=11 through purposive sampling). Four themes were identified: PCPs employ diverse approaches to integrate virtual care into their work flow; PCPs recognize that implementing virtual visits requires upfront time and effort but have variable perceptions regarding long-term impact of virtual care on processes; asynchronous messaging is preferable to synchronous audio or video visits; and strategies were identified to improve the integration of virtual visits. CONCLUSION: The potential of virtual care to improve work flow is dependent on the way these visits are implemented and used. Dedicated time for implementation, emphasis on using asynchronous secure messaging, and access to clinical champions and structured change management support were associated with more seamless integration of virtual visits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".