International Case Studies to Identify Success Factors and Contextual Conditions in the Digital Transformation of Health Care Systems and Derive Lessons for Germany: Study Protocol for a Mixed Methods Study
Bibliographic record
Abstract
Background: Germany's health care system continues to face significant challenges in its digital transformation due to outdated structures, interoperability issues, strict data protection regulations, and low user acceptance, despite numerous legislative initiatives, such as the Digital Care Act in 2019, which was intended to promote practical use and innovation. In contrast, several international health care systems have successfully advanced their digital transformation, offering valuable insights and potential lessons for the German health care system. Objective: This study, as part of the research project "NADI: Benefits and Acceptance of Digital Health," analyzes international health care systems to identify key success factors and develop pragmatic recommendations for German policymakers to enhance the country's digital health implementation. Methods: This study uses a mixed methods triangulation approach, combining case study selection, qualitative expert interviews, and a quantitative online survey to develop actionable policy recommendations for the digital transformation of health care in Germany. The study applies the conceptual framework of tipping points and success factors to identify critical factors in the digital transformation of health care systems, where certain actions or conditions fundamentally influence adoption and success. A total of more than 100 interviews were conducted with experts representing 8 stakeholder groups from 9 different health care systems. The qualitative data are evaluated using qualitative content analysis according to Kuckartz and Rädiker. In an online survey, a minimum of 305 participants from the German health care system will be surveyed regarding the relevance and feasibility of the key success factors identified in the international case studies. The dataset will be analyzed statistically using SPSS, both descriptively and inferentially (eg, subgroup analyses). Results: Between November 2024 and September 2025, interviews with international health care experts were conducted. As of October 2025, the qualitative content analysis is still ongoing. The recruitment phase for the online survey is planned from October 15 to December 15, 2025. Initial results are expected to be available in 2026. The study protocol was submitted during the qualitative data collection phase before the commencement of the quantitative survey. Analysis had not yet begun at the time of submission. Conclusions: The use of a case study methodology has been demonstrated to facilitate the acquisition of invaluable insights into international best practices, while concurrently offering the opportunity to identify specific success and failure factors. The integration of qualitative expert interviews serves to contextualize international findings on tipping points and success factors in the implementation and use of digital health tools. The transfer of the international results to the German context represents a central component of the research project, which aims to investigate practical implementation. The combination of these approaches forms a comprehensive basis for deriving specific recommendations for action for 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 | 0.051 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".