TY - JOUR
T1 - Embedding artificial intelligence in healthcare
T2 - An ethnographic exploration of an AI-based mHealth app through the lens of legitimacy
AU - Howe, Sydney
AU - Smak Gregoor, Anna
AU - Uyl-de Groot, Carin
AU - Wakkee, Marlies
AU - Nijsten, Tamar
AU - Wehrens, Rik
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/11/8
Y1 - 2024/11/8
N2 - Purpose: Skin cancer, a significant global health problem, imposes financial and workload burdens on the Dutch healthcare system. Artificial intelligence (AI) for diagnostic augmentation has gained momentum in dermatology, but despite significant research on adoption, acceptance, and implementation, we lack a holistic understanding of why technologies (do not) become embedded in the healthcare system. This study utilizes the concept of legitimacy, omnipresent but underexplored in health technology studies, to examine assumptions guiding the integration of an AI mHealth app for skin lesion cancer risk assessment in the Dutch healthcare system. Methods: We conducted a 3-year ethnographic case study, using participant observation, interviews, focus groups, and document analysis to explore app integration within the Dutch healthcare system. Participants included doctors, policymakers, app users, developers, insurers, and researchers. Our analysis focused on moments of legitimacy breakdown, contrasting company narratives and healthcare-based assumptions with practices and affectively-charged experiences of professionals and app users. Results: Three major kinds of legitimacy breakdowns impacted the embedding of this app: first, lack of institutional legitimacy led to informal workarounds by the company at the institutional level; second, quantification privilege impacted app influence in interactions with doctors; and third, interactive limits between users and the app contradicted expectations around ease of use and work burden alleviation. Conclusions: Our results demonstrate that legitimacy is a useful lens for understanding the embedding of health technology while taking into account institutional complexity. A legitimacy lens is helpful for decision-makers and researchers because it can clarify and anticipate pain points for the integration of AI into healthcare systems.
AB - Purpose: Skin cancer, a significant global health problem, imposes financial and workload burdens on the Dutch healthcare system. Artificial intelligence (AI) for diagnostic augmentation has gained momentum in dermatology, but despite significant research on adoption, acceptance, and implementation, we lack a holistic understanding of why technologies (do not) become embedded in the healthcare system. This study utilizes the concept of legitimacy, omnipresent but underexplored in health technology studies, to examine assumptions guiding the integration of an AI mHealth app for skin lesion cancer risk assessment in the Dutch healthcare system. Methods: We conducted a 3-year ethnographic case study, using participant observation, interviews, focus groups, and document analysis to explore app integration within the Dutch healthcare system. Participants included doctors, policymakers, app users, developers, insurers, and researchers. Our analysis focused on moments of legitimacy breakdown, contrasting company narratives and healthcare-based assumptions with practices and affectively-charged experiences of professionals and app users. Results: Three major kinds of legitimacy breakdowns impacted the embedding of this app: first, lack of institutional legitimacy led to informal workarounds by the company at the institutional level; second, quantification privilege impacted app influence in interactions with doctors; and third, interactive limits between users and the app contradicted expectations around ease of use and work burden alleviation. Conclusions: Our results demonstrate that legitimacy is a useful lens for understanding the embedding of health technology while taking into account institutional complexity. A legitimacy lens is helpful for decision-makers and researchers because it can clarify and anticipate pain points for the integration of AI into healthcare systems.
UR - http://www.scopus.com/inward/record.url?scp=85209574523&partnerID=8YFLogxK
U2 - 10.1177/20552076241292390
DO - 10.1177/20552076241292390
M3 - Article
C2 - 39525560
AN - SCOPUS:85209574523
SN - 2055-2076
VL - 10
JO - Digital Health
JF - Digital Health
ER -
