MétaCan
Menu
← Back to cohort
Record W4368351675 · doi:10.2196/preprints.47838

A mixed-methods evaluation of the implementation and use of an asynchronous virtual care delivery platform (Preprint)

2023· preprint· en· W4368351675 on OpenAlexaboutno aff
Emily Gard Marshall, Laura Sadler, Richard Buote, Lauren Moritz, Joanna Zed, Michael Donahue

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsModalitiesAsynchronous communicationPreprintTelemedicineComputer scienceMedical educationMedicinePsychologyFamily medicineWorld Wide WebHealth carePolitical scienceSociologyTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND Prior to the COVID-19 pandemic, primary care in Canada lagged behind other countries in the use of virtual care modalities. The COVID-19 pandemic incited widespread acceptance and uptake of virtual care in Canada. The use of synchronous virtual care (e.g., telephone or video modalities) was widespread and has been a valuable tool in primary care. While patients and providers have shown interest in using asynchronous modalities (e.g., messaging or email), the use of these modalities was limited compared to synchronous modalities. OBJECTIVE To evaluate the implementation and use of an asynchronous virtual care platform at two academic family medicine clinics in Nova Scotia, Canada, in 2021. METHODS This mixed-methods study utilized qualitative interview data with family physicians, family practice nurses, and administrative staff from two family medicine clinics; and administrative data, including indicators of the platform’s uptake and use. Critical incident narratives were elicited through semi-structured interviews, and these data were thematically analyzed. Descriptive analyses of administrative data from January to September 2021 were conducted to describe asynchronous care platform utilization over time and by patient characteristics. RESULTS The results of this evaluation were organized using a logic model. Interviewees identified inputs, such as technical support and training, and the importance of appropriate funding models to support the use of asynchronous care modalities, which was notably absent. Identified outputs included registration and use of the platform. Various invitation modes were used, but the most successful were individual invitations sent following conversations between the patient and provider. Most of the platform users were women and people over the age of 50, aligning with the characteristics of the highest primary care users. Most interactions on the platform were resolved within a day, but almost a quarter were resolved within 15 minutes. Short-term outcomes of using the asynchronous virtual care platform were considered across the Quadruple Aim Framework. The platform has the potential to improve population health outcomes, improve care and patient experience, improve provider satisfaction, and lower costs. CONCLUSIONS The use of virtual care has increased during the COVID-19 pandemic, and patients and providers have expressed a desire for virtual care access to remain beyond the pandemic. This evaluation identifies the necessary inputs to support the implementation of an asynchronous virtual care platform and, if well supported, asynchronous virtual care platforms have the potential to address the four quadrants of the Quadruple Aim, thereby improving the delivery of primary healthcare for patients and providers. This evaluation is formative, and the findings may be used to inform the continued uptake of asynchronous virtual care platforms within primary healthcare settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.075
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.134
GPT teacher head0.457
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicTelemedicine and Telehealth Implementation→French-language works237,207→