Paper Number
ECIS2026-2623
Paper Type
CRP
Abstract
AI has the potential to reorder clinical work practices and take over tasks traditionally requiring human expertise. While recent evidence suggests that AI leads to a different trajectory of change in contrast to traditional information technologies, we currently lack empirical evidence on how AI affects the healthcare routines through which clinical work is organized. With this study, we aim to explore the trajectory of AI-induced organizational change by drawing on organizational routines as a theoretical lens. Based on the findings from an in-depth longitudinal case study in the radiology department of a university hospital in Germany, we uncover that AI leads to increased variations in routine performances. Our findings contribute to theory by revealing how AI impacts clinical workflow routines and further we extend IS research on AI in healthcare by describing three mechanisms through which radiologists resolve these variations during routine performances.
Recommended Citation
Abaspur, Salar; Guse, Richard; and Sunyaev, Ali, "The Impact Of Artificial Intelligence On Organizational Routines In Healthcare: A Longitudinal Case Study" (2026). ECIS 2026 Proceedings. 20.
https://aisel.aisnet.org/ecis2026/hit/hit/20
The Impact Of Artificial Intelligence On Organizational Routines In Healthcare: A Longitudinal Case Study
AI has the potential to reorder clinical work practices and take over tasks traditionally requiring human expertise. While recent evidence suggests that AI leads to a different trajectory of change in contrast to traditional information technologies, we currently lack empirical evidence on how AI affects the healthcare routines through which clinical work is organized. With this study, we aim to explore the trajectory of AI-induced organizational change by drawing on organizational routines as a theoretical lens. Based on the findings from an in-depth longitudinal case study in the radiology department of a university hospital in Germany, we uncover that AI leads to increased variations in routine performances. Our findings contribute to theory by revealing how AI impacts clinical workflow routines and further we extend IS research on AI in healthcare by describing three mechanisms through which radiologists resolve these variations during routine performances.