Recurring implementation determinants in digital health innovations: a multi-context multiple case study and...

Digital health technologies are increasingly used in healthcare, but their implementation in routine practice remains uneven. Many solutions are introduced as pilots but are not sustained, scaled up, or fully embedded in everyday organizational work. This study aimed to identify implementation determinants that recur across different real-world digital health settings and to organize them into a practical analytical framework for implementation planning and readiness assessment. We conducted a secondary qualitative multiple case study with cross-case synthesis. Thirteen digital health implementations from six European countries, developed within an Erasmus+ project, were analyzed using standardized implementation case reports. The cases covered a range of technologies, including telemonitoring, artificial intelligence, extended reality solutions, and digital platforms, implemented at different levels of healthcare systems. The reports described implementation context, technology type, stakeholders, barriers and facilitators, organizational conditions, and lessons learned. Because the material consisted of secondary standardized reports rather than primary qualitative data, the analysis focused on recurring reported determinants and did not aim to develop a fully inductive explanatory theory. Established implementation frameworks were used ex post to support interpretation. The analysis identified 20 implementation determinants. These were organized into four interdependent domains: technological, organizational, user-related, and system-level. Frequently reported issues included interoperability, usability, leadership and governance, workflow integration, staff competencies, user engagement, funding stability, and regulatory alignment. Across the cases, implementation challenges rarely concerned the technology alone. The resulting four-domain framework offers a concise way to structure implementation knowledge across heterogeneous digital health settings. It may support early identification of implementation risks, readiness assessment, and more systematic planning of digital health interventions. The framework is best understood as an analytical and planning tool, rather than as a model that predicts implementation success.