Developing Balanced Quality Indicators for Monitoring Virtual Care in Ambulatory Care Environments: Modified Delphi Panel Process
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,259 | 0,190 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,005 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,005 | 0,018 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».