General Practitioners’ Perspectives on Digital Health Applications for Mental Disorders and Their Prescribing Behavior: Mixed Methods Study
Notice bibliographique
Résumé
Background: The high number of mental disorders poses challenges for health care systems. In 2020, digital health applications (DHAs) were introduced in Germany as a new form of health care financed by the statutory health insurance. They aim to detect, monitor, treat, or alleviate disease, injury, or disability. DHAs for mental disorders (DHA-MD) intend to improve outpatient care for patients with mental disorders. However, evidence on general practitioners' (GPs') perspectives on DHA-MD and their prescribing behavior is limited. Objective: This study aimed to analyze GPs' perspectives on DHA-MD and their prescribing behavior in the care of patients with mental disorders. Methods: A mixed methods study was conducted (January-October 2024), including a Germany-wide online survey and qualitative interviews with GPs and medical assistants (MAs). Sampling was conducted in collaboration with German research practice networks, which distributed the study invitation to their affiliated GPs. The questionnaire as well as the interview guides for GPs and MAs was developed by the study team according to the Consolidated Framework for Implementation Research. Descriptive analyses of prescribing behavior and perceived need (measured on an 11-point scale) for DHA-MD were conducted, followed by multivariate regression analyses to identify predictors of prescribing behavior and perceived need for DHA-MD. The interviews with GPs and MAs were analyzed using qualitative content analysis according to Mayring. Results: A sample of 149 GPs participated, and 12 GPs as well as 5 MAs were interviewed. The median prescription frequency of DHA-MD per quarter was 1, whereas the median estimated need was 3. Working in a half digitized and half paper-based practice (odds ratio 5.133, 95% CI 1.695-15.542) as well as working in a completely digitized practice (odds ratio 3.006, 95% CI 1.296-6.969) positively predicted the prescribing behavior. The duration of GPs' medical practice (b=-0.057; P=.01) negatively predicted the perceived need, while working in a group practice (b=0.980; P=.02) positively predicted the perceived need for DHA-MD. In the interviews, GPs and MAs reported that they valued DHA-MD as a temporary or supplementary option for bridging waiting times for psychotherapy and considered their effectiveness to be highly dependent on indication and patient adherence. Reported barriers of GPs according to DHA-MD included lacking knowledge about DHA-MD, missing effectiveness studies, and difficulties integrating them into existing care processes. Conclusions: GPs are reluctant to prescribe DHA-MD, as the need is considered to be low and their use is primarily seen as a temporary or supplementary treatment option rather than a stand-alone intervention. There are significant reasons for rejection and barriers that hinder prescription in primary care. Addressing these barriers and involving GPs as well as patients in future research are essential for the development of DHA-MD.
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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 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».