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

TL;DR

This study investigates ICU clinicians' needs for explainable AI decision support, highlighting key decision factors, complexity challenges, and design recommendations to improve AI integration in intensive care.

Contribution

It provides novel insights into clinicians' requirements for explainable AI in ICU settings through qualitative analysis and offers design guidelines for future AI systems.

Findings

Clinicians consider multiple factors in decision-making.

Complexity of patient data challenges shared decisions.

Design recommendations for explainable AI in ICU environments.

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 relevant in 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.