Removing Dust From the German Health Care System by Introducing Health Apps Into Standard Care: Semistructured Interview Study
Bibliographic record
Abstract
BACKGROUND: In 2019, Germany launched the Digital Healthcare Act. The reform enables physicians to prescribe health apps as treatments to their statutory-insured patients. OBJECTIVE: We aimed to determine the extent to which the integration of health apps into standard care could be considered beneficial and which aspects of the regulation could still be improved. METHODS: We conducted a semistructured interview study with 23 stakeholders in Germany and analyzed them thematically. We used descriptive coding for the first-order codes and pattern coding for the second-order codes. RESULTS: We created 79 first-order codes and 9 second-order codes following the interview study. Most stakeholders argued that the option of prescribing health apps could improve treatment quality. CONCLUSIONS: The inclusion of health apps into German standard care could improve the quality of treatment by expanding treatment portfolios. The educational elements of the apps might additionally lead to more patient emancipation through a better understanding of personal conditions. Location and time flexibility are the biggest advantages of the new technologies, but they also raise the most significant concerns for stakeholders because app use requires personal initiative and self-motivation. Overall, stakeholders agree that the Digital Healthcare Act has the potential to remove dust from the German health care system.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".