Factors for Supporting Primary Care Physician Engagement With Patient Apps for Type 2 Diabetes Self-Management That Link to Primary Care: Interview Study
Notice bibliographique
Résumé
BACKGROUND: The health burden of type 2 diabetes can be mitigated by engaging patients in two key aspects of diabetes care: self-management and regular contact with health professionals. There is a clear benefit to integrating these aspects of care into a single clinical tool, and as mobile phone ownership increases, apps become a more feasible platform. However, the effectiveness of online health interventions is contingent on uptake by health care providers, which is typically low. There has been little research that focuses specifically on barriers and facilitators to health care provider uptake for interventions that link self-management apps to the user's primary care physician (PCP). OBJECTIVE: This study aimed to explore PCP perspectives on proposed features for a self-management app for patients with diabetes that would link to primary care services. METHODS: Researchers conducted 25 semistructured interviews. The interviewer discussed potential features that would link in with the patient's primary care services. Interviews were audio-recorded, transcribed, and coded. Framework analysis and the Consolidated Criteria for Reporting Qualitative Research checklist were employed to ensure rigor. RESULTS: Our analysis indicated that PCP attitudes toward proposed features for an app were underpinned by perceived roles of (1) diabetes self-management, (2) face-to-face care, and (3) the anticipated burden of new technologies on their practice. Theme 1 explored PCP perceptions about how an app could foster patient independence for self-management behaviors but could also increase responsibility and liability for the PCP. Theme 2 identified beliefs underpinning a commonly expressed preference for face-to-face care. PCPs perceived information was more motivating, better understood, and presented with greater empathy when delivered face to face rather than online. Theme 3 described how most PCPs anticipated an initial increase in workload while they learned to use a new clinical tool. Some PCPs accepted this burden on the basis that the change was inevitable as health care became more integrated. Others reported potential benefits were outweighed by effort to implement an app. This study also identified how app features can be positively framed, highlighting potential benefits for PCPs to maximize PCP engagement, buy-in, and uptake. For example, PCPs were more positive when they perceived that an app could facilitate communication and motivation between consultations, focus on building capacity for patient independence, and reinforce rather than replace in-person care. They were also more positive about app features that were automated, integrated with existing software, flexible for different patients, and included secondary benefits such as improved documentation. CONCLUSIONS: This study provided insight into PCP perspectives on a diabetes app integrated with primary care services. This was observed as more than a technological change; PCPs were concerned about changes in workload, their role in self-management, and the nature of consultations. Our research highlighted potential facilitators and barriers to engaging PCPs in the implementation process.
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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 | 0,011 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».