On the Complexity of Health Data Protection-in-Practice: Insights from a Longitudinal Qualitative Study

Digitalization of healthcare presents opportunities for improving the quality of healthcare services and promises economic benefits. However, the success of digital health and the benefits cannot be actualized without considering health data protection practices in the process of healthcare service delivery. Despite the criticality of protecting health data in the system use lifecycle (from recording to consuming and taking informed actions), there is a paucity of research to investigate this complex phenomenon. Using longitudinal qualitative data on a state-wide digital health transformation project, we contextually theorize the practices for protecting health data. Our study reveals five types of health data protectionin-practice, namely data minimization, informal encoding, accuracy, improving cyber-awareness, and appropriate access management. Our results provide new insights into information system use (especially, effective use), and highlight practices that can improve health data protection

Exploring artificial intelligence adoption in public organizations: a comparative case study

Despite the enormous potential of artificial intelligence (AI), many public organizations struggle to adopt this technology. Simultaneously, empirical research on what determines successful AI adoption in public settings remains scarce. Using the technology organization environment (TOE) framework, we address this gap with a comparative case study of eight Swiss public organizations. Our findings suggest that the importance of technological and organizational factors varies depending on the organization’s stage in the adoption process, whereas environmental factors are generally less critical. Accordingly, this study advances our theoretical understanding of the specificities of AI adoption in public organizations throughout the different adoption stages