Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study

Siri L van der Meijden; Anne A H de Hond; Patrick J Thoral; Ewout W Steyerberg; Ilse M J Kant; Giovanni Cinà; M Sesmu Arbous

doi:10.2196/39114

Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study

JMIR Hum Factors. 2023 Jan 5:10:e39114. doi: 10.2196/39114.

Authors

Siri L van der Meijden^{1

2

3}, Anne A H de Hond^{2

4}, Patrick J Thoral⁵, Ewout W Steyerberg⁴, Ilse M J Kant^{2

4}, Giovanni Cinà^{6

7

8}, M Sesmu Arbous¹

Affiliations

¹ Department of Intensive Care Medicine, Leiden University Medical Center, Leiden, Netherlands.
² Clinical AI Implementation and Research Lab, Leiden University Medical Center, Leiden, Netherlands.
³ Healthplus.ai, Amsterdam, Netherlands.
⁴ Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands.
⁵ Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam University Medical Centers, Amsterdam, Netherlands.
⁶ Pacmed, Amsterdam, Netherlands.
⁷ Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, Netherlands.
⁸ Department of Medical Informatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.

PMID: 36602843
PMCID: PMC9853335
DOI: 10.2196/39114

Abstract

Background: Artificial intelligence-based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the development and implementation of these tools.

Objective: We aimed to investigate physicians' perspectives and their current decision-making behavior before implementing a discharge AI-CDS tool for predicting readmission and mortality risk after ICU discharge.

Methods: We conducted a survey of physicians involved in decision-making on discharge of patients at two Dutch academic ICUs between July and November 2021. Questions were divided into four domains: (1) physicians' current decision-making behavior with respect to discharging ICU patients, (2) perspectives on the use of AI-CDS tools in general, (3) willingness to incorporate a discharge AI-CDS tool into daily clinical practice, and (4) preferences for using a discharge AI-CDS tool in daily workflows.

Results: Most of the 64 respondents (of 93 contacted, 69%) were familiar with AI (62/64, 97%) and had positive expectations of AI, with 55 of 64 (86%) believing that AI could support them in their work as a physician. The respondents disagreed on whether the decision to discharge a patient was complex (23/64, 36% agreed and 22/64, 34% disagreed); nonetheless, most (59/64, 92%) agreed that a discharge AI-CDS tool could be of value. Significant differences were observed between physicians from the 2 academic sites, which may be related to different levels of involvement in the development of the discharge AI-CDS tool.

Conclusions: ICU physicians showed a favorable attitude toward the integration of AI-CDS tools into the ICU setting in general, and in particular toward a tool to predict a patient's risk of readmission and mortality within 7 days after discharge. The findings of this questionnaire will be used to improve the implementation process and training of end users.

Keywords: AI; acceptance; adoption; artificial intelligence; attitude; clinical decision support; clinical support; decision support; decision-making; digital health; discharge; eHealth; hospital; intensive care unit; opinion; perspective; prediction; risk; survey.

©Siri L van der Meijden, Anne A H de Hond, Patrick J Thoral, Ewout W Steyerberg, Ilse M J Kant, Giovanni Cinà, M Sesmu Arbous. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 05.01.2023.