Hospital-based ambulatory clinic adoption of video and telephone visits before and during the COVID-19 pandemic: a convergent mixed-methods study
Bibliographic record
Abstract
Purpose The purpose of this study is to understand virtual care use (e.g. telephone and video visits) during the COVID-19 pandemic across three hospital-based ambulatory clinics (i.e. mental health, renal and respiratory care) and to describe associated patient and provider experiences. Design/methodology/approach A mixed-methods convergent study was conducted including quantitative electronic medical records data on virtual care use, electronic surveys assessing domains of experience (e.g. satisfaction, acceptance and technology use) among patient and providers and semi-structured interviews exploring the associated barriers and facilitators of virtual care adoption. Findings Virtual care adoption rates and relative modality use (telephone vs video) varied across specialty clinics. Mental health clinics) showed the greatest use of virtual care and greater use of video over telephone, as compared to renal and respiratory care, where telephone was used almost exclusively. Patients and providers reported an overall good satisfaction and acceptance of virtual care (60–72%) across clinics, but commonly observed barriers (technical problems, behavioral adaptations needed and inequity) persisted. Good value propositions, tech support and the presence of early adopters who can support others in workflow re-design and highlight value propositions of virtual care were listed as adoption facilitators. Originality/value The study provides a unique opportunity to compare the rate of virtual care adoption before and during the COVID-19 pandemic across distinct specialties that operate within the same organizational and political setting. This study showed that the nature of the condition (e.g. mental health conditions) and the characteristics of the users (e.g. younger patients) may drive models of care with higher rate of video use. Focusing on removing common barriers, like providing tech support and ensuring equitable access to patients, continues to be important even in the context of high virtual care adoption rates during the pandemic.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".