Exploring Clinicians’ and Patients’ Acceptance and Utilization of a Digital Solution to Support Individualized Care in Diabetes Specialist Outpatient Care (DigiDiaS): Qualitative Study
Notice bibliographique
Résumé
Background: With the increasing prevalence of type 1 diabetes alongside limited health care resources, the need for more sustainable health care services is apparent. Central to ensuring the standard of diabetes care while simultaneously optimizing resource utilization is improved patient-clinician communication and the provision of individualized care. Digital outpatient solutions incorporating patient-reported outcome measures (PROMs) have been introduced in diabetes outpatient care over recent years; however, features and delivery methods differ, and existing studies on their use and perceived clinical value are limited. Furthermore, clinicians' acceptance has been suggested as a key factor in the sustainability of digital solutions. Thus, to support the implementation of digital outpatient solutions perceived as valuable by clinicians and patients, we need more knowledge about how they are accepted and utilized in clinical practice. Objective: This study investigates how clinicians and patients with type 1 diabetes accept and utilize a digital outpatient solution to support individualized care in the context of full-scale implementation at a diabetes specialist outpatient clinic. Furthermore, we aim to explore the synchronous interaction between patients and clinicians when they are allowed to prepare through the filling and reviewing of asynchronous PROMs before consultations. Methods: This qualitative study uses interpretive description as a methodological approach. The digital outpatient solution features various components, including PROM questionnaires, asynchronous chat, remote consultations, e-learning, and information distribution. Data were collected through semistructured interviews with 10 clinicians and 20 patients with type 1 diabetes and observations of consultations. The data from the patient and clinician interviews (267 A4 pages) were analyzed separately before being jointly analyzed in the context of the findings from the observations (40 A4 pages). Results: Our analysis resulted in the following three main themes that describe the interplay between clinicians' and patients' acceptance, utilization, and perceived clinical value of a digital outpatient solution: (1) clinicians' acceptance of the digital outpatient solution influences patients' acceptance, (2) variations in the use of different features influence the extent of individualized care, and (3) clinicians' and patients' utilization influences perceived care efficiency and quality. Those who demonstrated higher acceptance and more extensive utilization reported that the solution was more valuable in enhancing individualized care efficiency and quality. Conclusions: This study highlights the interplay between clinicians' and patients' acceptance, utilization, and perceived clinical value of a digital outpatient solution in diabetes specialist outpatient care. Our findings suggest that when clinicians and patients understand why and how digital solutions are used, such solutions can enhance care efficiency and quality, contributing to sustainable health care. Future research should aim to gain an in-depth understanding of clinicians' and patients' acceptance, as well as the effectiveness of change management strategies when implementing digital outpatient solutions in diabetes specialist outpatient care.
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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 | 0,013 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,003 |
| 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 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 ».