General Practitioners’ Perspectives on Digital Health Applications for Mental Disorders and Their Prescribing Behavior: Mixed Methods Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".