Understanding decision support adoption by European physicians: shifts in micro- and macro-level influences over time

Abstract

Abstract Decision support systems (DSS) are becoming essential in healthcare for enhancing diagnostic accuracy, optimizing treatment plans, and improving patient outcomes through data-driven insights. This study provides a comprehensive examination of the determinants shaping physicians' adoption of DSS, integrating insights from technology acceptance models, cross-cultural research, and institutional theory, with a focus on the unique characteristics of AI-based systems. By investigating both micro-level factors (i.e., perceived influence on quality of diagnosis and treatment decisions, social influence, perceived influence on physician–patient relationships, increased patient expectations, perceived influence on personal working processes, and transparency) and macro-level factors (i.e., national cultural dimensions, regulatory frameworks, and technological readiness), the study uncovers the complex interplay influencing adoption decisions. Analyzing these factors at two points in time reveals how DSS adoption drivers evolve as AI-based systems advance. The findings from two-stage binary sample selection models with data on 13,629 physicians from 2013 and 2018 suggest that only the perceived influence on quality of diagnosis and treatment decisions, system transparency, the existence of frameworks on confidentiality and privacy issues, and the cultural dimension masculinity/femininity have a robust influence over time. In addition, the study shows that while macro-level factors are pivotal in early adoption stages, micro-level determinants grow increasingly important as systems mature. These insights contribute to IS research by presenting a nuanced framework for understanding DSS adoption in healthcare, underscoring the need for adaptive strategies that reflect shifting technological, cultural, and regulatory landscapes.

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