Exploring the use and challenges of implementing virtual visits during COVID-19 in primary care and lessons for sustained use
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has rapidly transformed how healthcare is delivered to limit the transmission of the virus. This descriptive cross-sectional study explored the current use of virtual visits in providing care among primary care providers in southwestern Ontario during the first wave of the COVID-19 pandemic and the anticipated level of utilization post-pandemic. It also explored clinicians' perceptions of the available support tools and resources and challenges to incorporating virtual visits within primary care practices. METHODS: Primary care physicians and nurse practitioners currently practicing in the southwestern part of Ontario were invited to participate in an online survey. The survey invite was distributed via email, different social media platforms, and newsletters. The survey questions gathered clinicians' demographic information and assessed their experience with virtual visits, including the proportion of visits conducted virtually (before, during the pandemic, and expected volume post-pandemic), overall satisfaction and comfort level with offering virtual visits using modalities, challenges experienced, as well as useful resources and tools to support them in using virtual visits in their practice. RESULTS: We received 207 responses, with 96.6% of respondents offering virtual visits in their practice. Participants used different modalities to conduct virtual visits, with the vast majority offering visits via phone calls (99.5%). Since the COVID-19 pandemic, clinicians who offered virtual visits have conducted an average of 66.4% of their visits virtually, compared to an average of 6.5% pre-pandemic. Participants anticipated continuing use of virtual visits with an average of 43.9% post-pandemic. Overall, 74.5% of participants were satisfied with their experience using virtual visits, and 88% believed they could incorporate virtual visits well within the usual workflow. Participants highlighted some challenges in offering virtual care. For example, 58% were concerned about patients' limited access to technology, 55% about patients' knowledge of technology, and 41% about the lack of integration with their current EMR, the increase in demand over time, and the connectivity issues such as inconsistent Wi-Fi/Internet connection. There were significant differences in perception of some challenges between clinicians in urban vs, rural areas. Clinicians in rural areas were more likely to consider the inconsistent Wi-Fi and limited connectivity as barriers to incorporating virtual visits within the practice setting (58.8% vs. 40.2%, P = 0.030). In comparison, clinicians in urban areas were significantly more concerned about patients overusing virtual care services (39.4% vs. 21.6%, P = 0.024). As for support tools, 47% of clinicians advocated for virtual care standards outlined by their profession's college. About 32% identified change management support and technical training as supportive tools. Moreover, 39% and 28% thought local colleagues and in-house organizational support are helpful resources, respectively. CONCLUSION: Our study shows that the adoption of virtual visits has exponentially increased during the pandemic, with a significant interest in continuing to use virtual care options in the delivery of primary care post-pandemic. The study sheds light on tools and resources that could enhance operational efficiencies in adopting virtual visits in primary care settings and highlights challenges that, when addressed, can expand the health system capacity and sustained use of virtual 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".