Prescribing Experiences, Potentials, and Challenges of Digital Health Applications in the Field of Hormones and Metabolism: Cross-Sectional Survey Study of Health Care Providers in Germany
Notice bibliographique
Résumé
BACKGROUND: In 2020, the global prevalence of overweight and obesity was approximately 42%. One of the most common associated conditions is type 2 diabetes mellitus, which had a global prevalence of around 10.5% in 2021. Digital health applications (DiHA), which can be prescribed as certified medical devices in Germany, have been shown to effectively support disease management in patients with overweight and diabetes mellitus. However, little is known about DiHA-prescribing behavior of health care providers (HCPs) specializing in hormones and metabolism or about potential barriers to prescribing these applications. OBJECTIVE: This study aimed to assess HCPs' experience with and willingness to prescribe DiHA in the field of hormones and metabolism. In addition, it sought to examine the patient-relevant health care effects that HCPs perceive as potentially achievable or have already observed with DiHA use, as well as the barriers they perceive to prescribing these applications. METHODS: An online questionnaire was developed based on preliminary studies and a literature review consisting of 86 items covering 6 key areas: experience and willingness to prescribe, health care effects, barriers, scientific evidence, digital affinity, and sociodemographics. The anonymous survey was distributed via the German Diabetes Association to 6035 HCPs in Germany between August 2 and October 9, 2024. Descriptive analyses, as well as correlation and regression analyses, were conducted. RESULTS: A total of 350 HCPs participated in the survey (response rate=5.8%). Although the low response rate may limit generalizability, the findings provide insights into prescribing behavior within this specialty. More than half (187/350, 53.4%) had never prescribed any of the 54 DiHA available at the time of the survey, with 47.6% (89/187) citing a lack of experience as the primary reason. Among those who had prescribed a DiHA (163/350, 46.6%), the majority (139/163, 85.3%) had prescribed 1 of the 8 DiHA available for obesity or diabetes mellitus. Looking ahead, 42.9% (149/348) of all surveyed HCPs stated that they were either very unlikely (83/348, 23.9%) or somewhat unlikely (66/348, 19%) to prescribe these DiHA in the next 12 months. The greatest perceived benefits of DiHA were improvements in self-management, health literacy, and adherence. The main barriers to prescribing DiHA in the field of hormones and metabolism included inadequate reimbursement for ancillary medical services, poor compatibility with existing practice software, and a lack of digital affinity or motivation among patients. CONCLUSIONS: DiHA have not yet been fully integrated into standard health care. To improve prescribing, we recommend integrating DiHA into medical guidelines, ensuring proper reimbursement, and involving HCPs in the pricing and health-economic evaluation of DiHA. The recommendations outlined should be considered to maximize DiHA's potential and improve HCPs' acceptance, providing valuable insights for health policy to enhance the integration, reimbursement, and use of DiHA.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| 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 ».