Human-machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system.

Autor: Henry KE; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA., Kornfield R; Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.; Center for Behavioral Intervention Technologies, Northwestern University, Chicago, IL, USA., Sridharan A; Howard County General Hospital, Columbia, MD, USA., Linton RC; Howard County General Hospital, Columbia, MD, USA., Groh C; Department of Industrial Engineering, University of Wisconsin-Madison, Madison, WI, USA., Wang T; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA., Wu A; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Mutlu B; Department of Industrial Engineering, University of Wisconsin-Madison, Madison, WI, USA. bilge@cs.wisc.edu.; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA. bilge@cs.wisc.edu., Saria S; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA. ssaria@cs.jhu.edu.; Bayesian Health, New York, NY, 10005, USA. ssaria@cs.jhu.edu.
Jazyk: angličtina
Zdroj: NPJ digital medicine [NPJ Digit Med] 2022 Jul 21; Vol. 5 (1), pp. 97. Date of Electronic Publication: 2022 Jul 21.
DOI: 10.1038/s41746-022-00597-7
Abstrakt: While a growing number of machine learning (ML) systems have been deployed in clinical settings with the promise of improving patient care, many have struggled to gain adoption and realize this promise. Based on a qualitative analysis of coded interviews with clinicians who use an ML-based system for sepsis, we found that, rather than viewing the system as a surrogate for their clinical judgment, clinicians perceived themselves as partnering with the technology. Our findings suggest that, even without a deep understanding of machine learning, clinicians can build trust with an ML system through experience, expert endorsement and validation, and systems designed to accommodate clinicians' autonomy and support them across their entire workflow.
(© 2022. The Author(s).)
Databáze: MEDLINE
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