Virtual Primary Care Implementation during COVID-19 in High-Income Countries: A Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Primary care physicians across the world are grappling with adopting virtual services to provide appropriate patient care during the COVID-19 pandemic. As the crisis continues, it is imperative to recognize the wide-scale barriers and seek strategies to mitigate the challenges of rapid adoption to virtual care felt by patients and physicians alike. OBJECTIVE The purpose of this scoping review was to map the challenges, strategies, and lessons learned from high-income countries that can be mobilized to inform decision-makers on how to best implement virtual primary care services during and after the COVID-19 pandemic. Moreover, the findings of our scoping review identified the barriers and strategies within the Quadruple Aim components, which may prove to be an effective implementation strategy for virtual care adoption in primary care settings. METHODS The two concepts of virtual care and COVID-19 were searched in MEDLINE, EMBASE, and CINAHL on Aug 10, 2020, and Scopus was searched on Aug 15, 2020. The database searches returned 10,549 citations and an additional 766 citations were retrieved from searching the citations from the reference lists of articles that met all inclusion criteria. After deduplication, 6,580 unique citations remained. Following title and abstract screening, 1,260 full-text articles were reviewed, of which 49 articles were included for data extraction, and 38 articles met the eligibility criteria for inclusion in the review. RESULTS Seven factors were identified as major barriers to the implementation of virtual primary care. Of the 38 articles included in this scoping review, 20 (53%) articles focused on challenges to equitable access to care, specifically regarding the lack of access to internet, smartphones, and Internet bandwidth for rural, seniors, and underserved populations. The second most common factor discussed in the articles was the lack of funding for virtual care (n= 14; 37%), such as inadequate reimbursement policies for virtual care. Other factors included negative patient and clinician perceptions of virtual care (n=11; 29%), lack of appropriate regulatory policies (n= 10, 26%), inappropriate clinical workflows (n= 9, 24%), lack of virtual care infrastructure (n= 8; 21%), and lastly, a need for appropriate virtual care training and education for clinicians (n=5;13%). CONCLUSIONS This review identified several barriers and strategies to mitigate those barriers that address the challenges of virtual primary care implementation related to equity, regulatory policies, technology and infrastructure, education, clinician and patient experience, clinical workflows, and funding for virtual care. These strategies included providing equitable alternatives to access care for patients with limited technical literacy and English proficiency and altering clinical workflows to integrate virtual care services. As many countries enter potential subsequent waves of the COVID-19 pandemic, applying early lessons learned to mitigate implementation barriers can help with the transition to equitable and appropriate virtual primary care services.
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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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".