The socio-technical adoption and diffusion of digital health innovations: The development of the STAD-HC model based on telemedicine in Germany

Digital and telemedicine innovations are increasingly central to healthcare transformation, yet their adoption and diffusion remain complex. While the Diffusion of Innovations (DOI) theory explains staged adoption processes, it underrepresents socio-political and institutional factors. The Social Shaping of Technology (SST) approach complements DOI by focusing on the co-construction of technology through cultural, organizational, and political dynamics. This study investigates how DOI and SST can be combined to explain the adoption and diffusion of digital health innovations, with a focus on telemedicine in the German healthcare system. Using the Decision Matrix for Theory Borrowing, DOI and SST were selected as complementary frameworks. A secondary analysis of 12 expert interviews was conducted using SST-based content analysis. Data were coded according to Rogers' five adoption stages and four SST dimensions. Findings were mapped in a DOI–SST matrix and analysed across micro-, meso-, and macro-levels. The analysis shows that innovation trajectories are shaped by interdependent socio-technical factors across all adoption stages. Key barriers include cultural skepticism, organizational inertia, fragmented governance, and economic misalignment. The Socio-Technical Adoption and Diffusion Model for Healthcare (STAD-HC) was developed to represent these dynamics, linking DOI's stage logic with SST's contextual dimensions. The STAD-HC model offers a multi-level framework to analyse how digital health technologies are adopted, contested, and institutionalized. By integrating behavioural processes with structural influences, it provides practical guidance for managing innovation and supports future research on complex digital health technologies such as AI and blockchain