Health Care Provider Perspectives on the Use of a Digital Behavioral Health App to Support Patients: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Despite the growing evidence indicating the efficacy of digital cognitive behavioral interventions (dCBIs) for behavioral health (BH) treatment, broad and consistent use of such interventions has been limited by knowledge obtained in real-world settings, including factors that impact provider uptake/referral. Engaging providers early in the implementation process offers an opportunity to explore their needs and behaviors, integrate interventions into workflows, and better understand provider setting capabilities. OBJECTIVE: This study assessed providers' views on the feasibility and acceptability of delivering a cognitive behavioral therapy (CBT)-based mobile app in multiple care settings. METHODS: Participating providers included BH and physical health (PH) providers from a women's health center, an outpatient BH clinic, and both rural/urban primary care settings. All participating providers cocreated workflows through facilitated workshops, including establishing feedback loops between the project team and providers and identifying clinical champions at each site. Over a 12-week period, the providers referred adult patients experiencing anxiety or depression to a mobile app-based dCBI, RxWell, and provided other indicated treatments as part of usual care. Referrals were completed by the providers through the electronic medical record. To better understand facilitators of and challenges in integrating RxWell into routine practice and perceptions of sustainability, a series of qualitative interviews was conducted. Interview data were analyzed to identify major themes using an inductive content analysis approach. RESULTS: A total of 19 provider interviews were conducted to discover motivators and barriers for referring RxWell. The providers benefited from a focused discussion on how to incorporate the referral process into their workflow, and knowing the app content was rooted in evidence. Although the providers believed engaging in experiential learning was important, they indicated that more education on the digital health coach role and how to monitor patient progress is needed. The providers thought patient engagement may be impacted by motivation, a lack of comfort using a smartphone, or preference for in-person therapy. The providers also expressed enthusiasm in continuing to refer the app. They liked the ability to provide patients with support between sessions, to have an extra treatment option that teaches BH exercises, and to have a CBT treatment option that overcomes barriers (eg, wait times, copays, travel) to traditional therapy modalities. CONCLUSIONS: Digital intervention success in health care settings relies heavily on engagement of key stakeholders, such as providers, in both design and implementation of the intervention and focused evaluation within intended care setting(s). Scaling digital interventions to meet the mental health needs of patients in usual care settings leans on thoughtfully constructed and streamlined workflows to enable seamless referral of patients by providers. Our findings strongly suggest that providers are supportive of digital tool integration to support the mental health of patients and endorse its use within their routine workflow.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 | 0,001 |
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 ».