Patient and Family Engagement Approaches for Digital Health Initiatives: Protocol for a Case Study
Notice bibliographique
Résumé
BACKGROUND: Digital health initiatives such as patient portals, virtual care platforms, and smartphone-based apps are being implemented at a rapid pace in health care organizations worldwide. This is often done to improve access beyond traditional in-person care and enhance care quality. Recent studies have indicated that better outcomes of using these initiatives and technologies may be achieved when patients and their family members are engaged in all aspects of planning, implementation, use, and evaluation. However, little guidance exists for how health care administrators can achieve effective engagement in digital health initiatives specifically. OBJECTIVE: The objective of this study is to document processes related to planning and implementing patient and family engagement (PFE) in digital health initiatives. This information will be used to develop tangible resources (eg, a field guide) that other organizations can use to implement PFE approaches for digital health initiatives in their organizations. METHODS: A previously developed multidimensional conceptual framework for PFE in health and health care contexts will be used to guide this work. To understand the intricacies involved in using PFE approaches in digital health strategies, a case study will be conducted. More specifically, this work will employ an embedded single-case design with PFE in digital health initiatives at a large Canadian mental health and addictions teaching hospital. Multiple digital health projects being undertaken at the study site will be explored to better understand where the PFE is intended to support the design, implementation, and operation of the digital health platform or technology. These projects will form the individual units of analysis. Data collection will involve field notes and artifact collection by a participant observer and interviews with the various digital health project teams. Data analysis will include a content and thematic analysis, triangulation of the findings, and a chronological mapping of data to a PFE process. RESULTS: Funding for this work was provided by the Canadian Institutes of Health Research (CIHR), via a Health System Impact Fellowship. As of August 2020, digital health projects that will form the case study units have been identified, and the participant observer has started to embed themselves into these projects. Although the development and collection of field notes and artifacts, respectively, have begun, interviews have not been conducted. The study is expected to conclude in September 2021. Once this study is complete, the development of a field guide and resources to support the uptake of PFE strategies in digital health will begin. CONCLUSIONS: By better understanding the processes involved in PFE in digital health projects, guidance can be provided to relevant stakeholders and organizations about how to do this work in an effective manner. It is then anticipated that with the increasing use of PFE approaches, there may be improved uptake, experience, and outcomes associated with using digital health technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/24274.
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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,073 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,011 | 0,005 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,006 | 0,007 |
| Intégrité de la recherche | 0,008 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,077 | 0,013 |
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 ».