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
Notice bibliographique
Résumé
Background: Germany's health care system continues to face significant challenges in its digital transformation due to outdated structures, interoperability issues, strict data protection regulations, and low user acceptance, despite numerous legislative initiatives, such as the Digital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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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 | 0,051 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,030 | 0,005 |
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 ».