Cancer Clinicians’ Views Regarding an App That Helps Patients With Cancer Meet Their Information Needs: Qualitative Interview Study
Notice bibliographique
Résumé
BACKGROUND: Many patients with cancer have unmet information needs during the course of the illness. Smart devices, such as smartphones and tablet computers, provide an opportunity to deliver information to patients remotely. We aim to develop an app intervention to help patients with cancer meet their illness-related information needs in noninpatient settings. In addition to the in-depth exploration of the issues faced by the target users of a potential intervention, it is important to gain an understanding of the context in which the intervention will be used and the potential influences on its adoption. As such, understanding the views of clinicians is key to the successful implementation of this type of app in practice. Additionally, clinicians have an awareness of their patients' needs and can provide further insight into the type of app and features that might be most beneficial. OBJECTIVE: This study aims to explore cancer clinicians' views on this type of intervention and whether they would support the use of an app in cancer care. Specifically, the perceived acceptability of an app used in consultations, useful app features, the potential benefits and disadvantages of an app, and barriers to app use were explored. METHODS: A total of 20 qualitative, semistructured interviews were conducted with 22 clinicians from urological, colorectal, breast, or gynecological cancer clinics across 2 hospitals in South Wales. The interviews were audio recorded, transcribed, and analyzed using thematic analysis. RESULTS: Clinicians felt that it would be acceptable for patients to use such an app in noninpatient settings, including during consultations. The benefits of this type of app were anticipated to be a more informed patient, an increased sense of control for patients, better doctor-patient communication, and a more efficient and effective consultation. In contrast, an increase in clinicians' workload and poorer communication in consultations, which depended on the included app features, were identified as potential disadvantages. The anticipated barriers to app use included patients' age and prior experience with smart technology, their access to smart devices, the confidentiality of information, and an avoidant coping approach to their condition. CONCLUSIONS: This study suggests that clinicians should support their patients in using an app to help them meet their information needs both at home and during consultations. This study highlights some of the potential barriers for this type of intervention in practice, which could be minimized during the intervention design process.
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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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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) | 0,002 | 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 ».