Abstract

AI technologies are envisioned as a solution to current societal challenges, including in the healthcare sector. However, their implementation in real-world hospital settings remains limited. In this paper we examine the slow implementation of AI in healthcare as a problem of installed base constraints. AI technologies are fundamentally different from traditional medical information technology. However, existing implementation practices are still shaped by regulations, development and implementation approaches established for conventional medical information technologies, constituting the installed base. We examine these constraints as frictions between novel AI technologies and the installed base through an interview-based study with 14 interviews with 11 experts in the Norwegian healthcare sector. We address the following research question: How do frictions between novel AI technologies and the existing sociotechnical installed base shape the process of AI implementation in public hospitals? Our findings reveal seven key frictions relating to data complexities, hospital information infrastructures, regulatory landscape, resource requirements, accountability, implementation strategies, and incentive structures. We contribute to understanding the shift needed to support the implementation and use of AI technologies in healthcare.

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