Explainable AI for Clinical Decision Support Systems in Intensive Care

Paper

Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care

Jeffrey N. Clark, University of Bristol, (UoB)
Matthew Wragg, University of Bristol
Emily Nielsen, University of Bristol
Miquel Perello-Nieto, University of Bristol
Nawid Keshtmand, University of Bristol
Michael Ambler, UoB and University Hospitals Bristol and Weston NHS Foundation Trust
Shiv Sharma, University Hospitals Bristol and Weston NHS Foundation Trust
Christopher P. Bourdeaux, University Hospitals Bristol and Weston NHS Foundation Trust
Amberly Brigden, University of Bristol
Raul Santos-Rodriguez, University of Bristol

Abstract

There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and less human interpretable. This complexity raises concerns about trustworthiness, impacting safe and effective adoption of such technologies. Improved understanding of decision-making processes and requirements for explanations coming from decision support tools is a vital component in providing effective explainable solutions. This is particularly relevantin the data-intensive, fast-paced environments of intensive care units (ICUs). To explore these issues, group interviews were conducted with seven ICU clinicians, representing various roles and experience levels. Thematic analysis revealed three core themes: (T1) ICU decision-making relies on a wide range of factors, (T2) the complexity of patient state is challenging for shared decision-making, and (T3) requirements and capabilities of AI decision support systems. We include design recommendations from clinical input, providing insights to inform future AI systems for intensive care.