TY - JOUR

T1 - The robot butler

T2 - How and why should we study predictive algorithms and artificial intelligence (AI) in healthcare?

AU - Gjødsbøl, Iben Mundbjerg

AU - Ringgaard, Anna Kirstine

AU - Holm, Peter Christoffer

AU - Brunak, Søren

AU - Bundgaard, Henning

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024

Y1 - 2024

N2 - Artificial intelligence (AI) and algorithms are heralded as significant solutions to the widening gap between the rising healthcare needs of ageing and multi-morbid populations and the scarcity of resources to provide such care. Objective: This article investigates how the PMHnet algorithm – an AI prognostication tool developed in Denmark to predict the one-year all-cause mortality risk for patients hospitalized with ischemic heart disease – was presented to cardiologists working in the hospital setting, and how they responded to this novel decision-support tool. Methods: Empirically, we draw upon ethnographic fieldwork in the Danish-led international research project, PM Heart, which since 2019 has developed the PMHnet algorithm and implemented the software into the electronic health record system in hospitals in Eastern Denmark (the Capital Region and Region Zealand). Results: Paying careful attention to the hopes and concerns of cardiologists who will have to embrace and adapt to algorithmic tools in their everyday work of diagnosing and treating patients, we identify three analytical themes meriting attention when AI is implemented in healthcare: 1) the re-negotiation of agency and autonomy in human-algorithm relations, 2) accountability in algorithmic prognostication and 3) the complex relationship between association and causation actualized by predictive algorithms. From these analytical themes, we elicit methodological questions to guide future ethnographic explorations of how AI and advanced algorithms are put to use in the healthcare system, with what implications, and for whom. Conclusion: We conclude that local, qualitative investigations of how algorithms are used, embraced and contested in everyday clinical practice are needed in order to understand their implications – good and bad, intended and unintended – for clinicians, patients and healthcare provision.

AB - Artificial intelligence (AI) and algorithms are heralded as significant solutions to the widening gap between the rising healthcare needs of ageing and multi-morbid populations and the scarcity of resources to provide such care. Objective: This article investigates how the PMHnet algorithm – an AI prognostication tool developed in Denmark to predict the one-year all-cause mortality risk for patients hospitalized with ischemic heart disease – was presented to cardiologists working in the hospital setting, and how they responded to this novel decision-support tool. Methods: Empirically, we draw upon ethnographic fieldwork in the Danish-led international research project, PM Heart, which since 2019 has developed the PMHnet algorithm and implemented the software into the electronic health record system in hospitals in Eastern Denmark (the Capital Region and Region Zealand). Results: Paying careful attention to the hopes and concerns of cardiologists who will have to embrace and adapt to algorithmic tools in their everyday work of diagnosing and treating patients, we identify three analytical themes meriting attention when AI is implemented in healthcare: 1) the re-negotiation of agency and autonomy in human-algorithm relations, 2) accountability in algorithmic prognostication and 3) the complex relationship between association and causation actualized by predictive algorithms. From these analytical themes, we elicit methodological questions to guide future ethnographic explorations of how AI and advanced algorithms are put to use in the healthcare system, with what implications, and for whom. Conclusion: We conclude that local, qualitative investigations of how algorithms are used, embraced and contested in everyday clinical practice are needed in order to understand their implications – good and bad, intended and unintended – for clinicians, patients and healthcare provision.

KW - Artificial intelligence

KW - cardiovascular disease

KW - clinical care

KW - ethnography

KW - methodological guidance

KW - personalized medicine

KW - precision medicine

KW - predictive algorithms

KW - qualitative (studies)

U2 - 10.1177/20552076241241674

DO - 10.1177/20552076241241674

M3 - Journal article

C2 - 38528969

AN - SCOPUS:85188541890

VL - 10

JO - Digital Health

JF - Digital Health

SN - 2055-2076

ER -