Quality measures of virtual care in ambulatory healthcare environments: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Delivery of virtual care increased throughout the COVID-19 pandemic and persisted after physical distancing measures ended. However, little is known about how to measure the quality of virtual care, as current measures focus on in-person care and may not apply to a virtual context. This scoping review aims to understand the connections between virtual care modalities used with ambulatory patient populations and quality measures across the Quintuple Aim (provider experience, patient experience, per capita cost, population health and health equity). DESIGN: Virtual care was considered any interaction between patients and/or their circle of care occurring remotely using any form of information technology. Five databases (MEDLINE, Embase, PsycInfo, Cochrane Library, JBI) and grey literature sources (11 websites, 3 search engines) were searched from 2015 to June 2021 and again in August 2022 for publications that analysed virtual care in ambulatory settings. Indicators were extracted, double-coded into the Quintuple Aim framework; patient and provider experience indicators were further categorised based on the National Academy of Medicine quality framework (safety, effectiveness, patient-centredness, timeliness, efficiency and equity). Sustainability was added to capture the potential for continued use of virtual care. RESULTS: 13 504 citations were double-screened resulting in 631 full-text articles, 66 of which were included. Common modalities included video or audio visits (n=43), remote monitoring (n=11) and mobile applications (n=11). The most common quality indicators were related to patient experience (n=58 articles), followed by provider experience (n=25 articles), population health outcomes (n=23 articles) and health system costs (n=19 articles). CONCLUSIONS: The connections between virtual care modalities and quality domains identified here can inform clinicians, administrators and other decision-makers how to monitor the quality of virtual care and provide insights into gaps in current quality measures. The next steps include the development of a balanced scorecard of virtual care quality indicators for ambulatory settings to inform quality improvement.
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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 | 0.028 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".