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Record W4205164313 · doi:10.51731/cjht.2022.238

Approaches to Evaluations of Virtual Care in Primary Care

2022· article· en· W4205164313 on OpenAlexaboutno aff
Daphne Hui, Bert Dolcine, Hannah Loshak

Bibliographic record

VenueCanadian Journal of Health Technologies · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careScope (computer science)Quality (philosophy)Economic evaluationResource (disambiguation)MedicineNursingComputer sciencePolitical science

Abstract

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A literature search informed this Environmental Scan and identified 11 evaluations of virtual care in primary care health settings and 7 publications alluding to methods, standards, and guidelines (referred to as evaluation guidance documents in this report) being used in various countries to evaluate virtual care in primary care health settings. The majority of included literature was from Australia, the US, and the UK, with 2 evaluation guidance documents published by the Heart and Stroke Foundation of Canada. Evaluation guidance documents recommended using measurements that assess the effectiveness and quality of clinical care including safety outcomes, time and travel, financial and operational impact, participation, health care utilization, technology experience including feasibility, user satisfaction, and barriers and facilitators or measures of health equity. Evaluation guidance documents specified that the following key decisions and considerations should be integrated into the planning of a virtual care evaluation: refining the scope of virtual care services; selecting an appropriate meaningful comparator; and identifying opportune timing and duration for the evaluation to ensure the evaluation is reflective of real-world practice, allows for adequate measurement of outcomes, and is comprehensive, timely, feasible, non-complex, and non–resource-intensive. Evaluation guidance documents highlighted that evaluations should be systematic, performed regularly, and reflect the stage of virtual care implementation to encompass the specific considerations associated with each stage. Additionally, evaluations should assess individual virtual care sessions and the virtual care program as a whole. Regarding economic components of virtual care evaluations, the evaluation guidance documents noted that costs or savings are not limited to monetary or financial measures but can also be represented with time. Cost analyses such as cost-benefit and cost-utility estimates should be performed with a specific emphasis on selecting an appropriate perspective (e.g., patient or provider), as that influences the benefits, effects, and how the outcome is interpreted. Two identified evaluations assessed economic outcomes through cost analyses in the perspective of the patient and provider. Evidence suggests that, in some circumstances, virtual care may be more cost-effective and reduces the cost per episode and patient expenses (e.g., travel and parking costs) compared to in-person care. However, virtual care may increase the number of individuals treated, which would increase overall health care spending. Four identified evaluations assessed health care utilization. The evidence suggests that virtual care reduces the duration of appointments and may be more time-efficient compared to in-person care. However, it is unclear if virtual care reduces the use of medical resources and the need for follow-up appointments, hospital admissions, and emergency department visits compared to in-person care. Five identified evaluations assessed participation outcomes. Evidence was variable, with some evidence reporting that virtual care reduced attendance (e.g., reduced attendance rates) and other evidence noting improved attendance (e.g., increased completion rate and decreased cancellations and no-show rates) compared to in-person care. Three identified evaluations assessed clinical outcomes in various health contexts. Some evidence suggested that virtual care improves clinical outcomes (e.g., in primary care with integrated mental health services, symptom severity decreased) or has a similar effect on clinical outcomes compared to in-person care (e.g., use of virtual care in depression elicited similar results with in-person care). Three identified evaluations assessed the appropriateness of prescribing. Some studies suggested that virtual care improves appropriateness by increasing guideline-based or guideline-concordant antibiotic management, or elicits no difference with in-person care.

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)0.255
metaresearch head score (Gemma)0.339
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.255
Threshold uncertainty score0.919

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2550.339
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0290.026
Science and technology studies0.0040.008
Scholarly communication0.0200.016
Open science0.0060.014
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0190.002

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 head0.120
GPT teacher head0.353
Teacher spread0.233 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2022
Admission routes1
Has abstractyes

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