Impact of patient engagement on the design of a mobile health technology for cardiac surgery
Notice bibliographique
Résumé
ABSTRACT Objective The aims of this study were to describe the impact of patient engagement on the initial design and content of a mobile health (mHealth) technology that supports enhanced recovery protocols (ERPs) for cardiac surgery. Methods Engagement occurred at the level of consultation and took the form of an advisory panel. Patients that underwent cardiac surgery (2017-2018) at St. Boniface Hospital (Winnipeg, Manitoba) and consented to be contacted about future research, and their caregivers, were approached for participation. A qualitative exploration was undertaken to determine advisory panel members’ key messages about, and the impact of, patient engagement on mHealth technology design and content. Results Ten individuals participated in the advisory panel. Key design-specific messages centered around access, tracking, synchronization, and reminders. Key content-specific messages centered around roles of cardiac surgery team members and medical terms, educational videos, information regarding cardiac surgery procedures, travel before/after surgery, nutrition (i.e., what to eat), medications (i.e., drug interactions), resources (i.e., medical devices), and physical activity (i.e., addressing fears and providing information, recommendations, and instructions). These key messages were a rich source of information for mHealth technology developers and were incorporated as supported by the existing capabilities of the underlying technology platform. Conclusions Patient engagement facilitated the development of a mHealth technology whose design and content were driven by the lived experiences of cardiac surgery patients and caregivers. The result was a detail-oriented and patient-centered mHealth technology that helps to empower and inform patients and their caregivers about the patient journey across the perioperative period of cardiac surgery. KEY QUESTIONS What is already known about this subject? Enhanced recovery protocols (ERPs) have been proposed as a clinical strategy to effectively address complex and multi-system vulnerabilities, like those commonly present in older adults undergoing cardiac surgery. Mobile health (mHealth) technologies have the potential to improve delivery and patient experience with ERPs, but their development in the academic research setting is often limited by a lack of end-user (e.g., i.e., patient and caregiver) involvement. What does this study add? To our knowledge, this is one of the first studies to engage patients and caregivers in the development of a mHealth technology that supports ERPs for cardiac surgery. This study describes a process for engaging patients and caregivers as “co-producers” of a mHealth technology to support delivery of ERPs during the perioperative period of cardiac surgery. It also demonstrates that engaging patients and caregivers in research, through the formation of an advisory panel, yields a rich source of information to guide the design and content of mHealth technologies in cardiac research. How might this impact on clinical practice? In an era in which mHealth technologies are being increasingly looked to for the optimization of healthcare delivery, this study underscores the utility of using patient and caregiver voices to drive the development of patient-centered mHealth technologies to support clinical practice.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».