Applying User-Centered Design and Implementation Science to the Early-Stage Development of a Telehealth-Enhanced Hybrid Cardiac Rehabilitation Program: Quality Improvement Study
Notice bibliographique
Résumé
BACKGROUND: Cardiac rehabilitation (CR) is an evidence-based intervention that improves event-free survival in patients with cardiac conditions, yet <27% of all eligible patients use CR in the United States. CR is traditionally delivered in clinic-based settings where implementation barriers abound. Innovative nontraditional program designs and strategies are needed to support widespread CR uptake. OBJECTIVE: This study aimed to demonstrate how user-centered design (UCD) and implementation science (IS) principles and methods can be integrated into the early-stage development of nontraditional CR interventions. METHODS: As part of a NewYork-Presbyterian Hospital (NYPH) quality improvement initiative (March 2020-February 2022), we combined UCD and IS principles and methods to design a novel home- and clinic-based telehealth-enhanced hybrid CR (THCR) program. We co-designed this program with multilevel stakeholders using an iterative 3-step UCD process to identify user and contextual barriers and facilitators to CR uptake (using semistructured interviews and contextual inquiry [step 1]), design an intervention prototype that targets contextual and user factors and emulates the evidence-based practice (through design workshops and journey mapping [step 2]), and review and refine the prototype (according to real-world usability testing and feedback [step 3]). The UCD process was informed by the Theoretical Domains Framework and Consolidated Framework for Implementation Research. RESULTS: At step 1, we conducted semistructured interviews with 9 provider- and system-level stakeholders (female: n=6, 67%) at 3 geographically diverse academic medical centers, which revealed behavioral (eg, self-efficacy and knowledge) and contextual (eg, social distancing guidelines, physical space, staffing, and reimbursement) barriers to uptake; hybrid delivery was a key facilitator. Step 2 involved conducting 20 design workshops and 3 journey-mapping sessions with multidisciplinary NYPH stakeholders (eg, digital health team, CR clinicians, and creative director) where we identified key design elements (eg, mix of clinic- and home-based CR and synchronous remote patient monitoring), yielding an initial THCR prototype that leveraged NYPH's telehealth infrastructure. At step 3, we conducted usability testing with 2 CR clinicians (both female) administering home-based sessions to 3 CR patients (female: n=1, 33%), which revealed usability themes (eg, ease of using remote patient monitoring devices or a telehealth platform, technology disruptions, and confidence in using the telehealth platform to safely monitor patients) and design solutions (eg, onboarding sessions, safety surveys, and fully supervised remote sessions) to be included in the final THCR prototype. CONCLUSIONS: Combining UCD and IS methods while engaging multidisciplinary stakeholders in an iterative process yielded a theory-informed THCR program targeting user and contextual barriers to real-world CR implementation. We provide a detailed summary of the process and guidance for incorporating UCD and IS principles and methods into the early-stage development of a nontraditional CR intervention. The feasibility, acceptability, appropriateness, and usability of the final THCR prototype is being evaluated in an ongoing study.
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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 | 0,017 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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.
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