Developing Balanced Quality Indicators for Monitoring Virtual Care in Ambulatory Care Environments: Modified Delphi Panel Process (Preprint)
Bibliographic record
Abstract
BACKGROUND While the volume of virtual visits increased with the COVID-19 pandemic, little is still known regarding the quality of virtual visits. Furthermore, there is limited guidance on how best to evaluate the quality of virtual care initiatives. OBJECTIVE The objective of this study was to curate a balanced set of quality indicators grounded in the Quintuple Aim quality framework and the National Academy of Medicine domains of quality, including sustainability, to monitor the quality of virtual care in ambulatory environments. METHODS Phase 1 involved curating a list of ambulatory virtual care quality indicators from published and grey literature, along with knowledge user feedback generated through a pre-Delphi survey; these indicators were mapped and categorized to the Quintuple Aim and National Academy of Medicine (NAM) Quality Domains. In phase 2, a 19-member pan-Canadian panel was convened as part of a 3-round modified Delphi panel process to rate and rank the quality indicators. The panel included clinicians, individuals with lived experience with the health care system, policy makers, academics, and administrators. Panelists rated indicators using the Agency for Healthcare Research and Quality measure attributes on a 9-point Likert scale in round 1, followed by a virtual Delphi panel meeting to discuss indicators before round 2 of re-rating, then a final round 3 of ranking which assessed the importance of indicators within each quality domain and subdomain. To address gaps in the literature, novel quality indicators of virtual care were identified and discussed in panel discussions, patient group consultations, and a pre-Delphi survey. For indicator advancement during the rating exercises, 75% or more of the panelists’ responses in the top tertile (scores of 7-9) with a median composite score of 7 or greater was required. RESULTS There were 140 quality indicators identified in the literature which progressed through the 3 Delphi panel rounds. There was minimal attrition among Delphi panel members (17/19, 89% participated in all 3 rounds). After round 3, 25 indicators were included in the final scorecard. Aligned with the Quintuple Aim, there are 13 quality indicators on patient experience, 6 on provider experience, 2 on population health, 2 on health equity, and 2 on health system costs. CONCLUSIONS A balanced set of 25 quality indicators of ambulatory virtual care was developed based on literature and consensus building from knowledge users across the health system. This curated set of indicators will support more comprehensive evaluations of virtual care. Organizations can use the set of indicators as part of a scorecard to compare across jurisdictions, identify priority areas, and ensure their virtual care initiatives are delivering high-quality care based on multiple domains of quality.
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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 | 0.240 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".