Provision of Virtual Outpatient Care during the COVID-19 Pandemic and Beyond: Enabling Factors and Experiences from the UBC Pharmacists Clinic
Bibliographic record
Abstract
The COVID-19 pandemic has generated an unprecedented level of interest in, and uptake of, technology-enabled virtual health care delivery as clinicians seek ways to safely care for patients with physical distancing. This paper describes the UBC Pharmacists Clinic's technical systems and lessons learned using enabling technology and the provision of virtual patient care by pharmacists. Of 2036 scheduled appointments at the clinic in 2019, only 1.5% of initial appointments were conducted virtually which increased to 64% for follow-up appointments. Survey respondents (n = 18) indicated an overall high satisfaction with the format, quality of care delivery, ease of use and benefits to their overall health. Other reports indicate that the majority of patients would like the option to book appointments electronically, email their healthcare provider, and have telehealth visits, although a small minority (8%) have access to virtual modes of care. The Clinic team is bridging the technology gap to better align virtual service provision with patient preferences. Practical advice and information gained through experience are shared here. As the general population and health care providers become increasingly comfortable with video conferencing as a result of COVID-19, it is anticipated that requests for video appointments will increase, technological barriers will decrease and conditions will enable providers to increase their virtual care capabilities. Lessons learned at the Clinic have application to pharmacists in both out-patient and in-patient care settings.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".