Exploring the Integration of AI in Sepsis Management: A Pilot Study on Clinician Acceptance and Decision-Making in ICU Settings

Abstract

This paper presents a human factors qualitative study on an AI application for managing sepsis in Intensive Care Units (ICUs). The study involved semi-structured interviews with nine ICU clinicians and nurses across three London hospitals. It consisted of two parts: the first applied methods to understand sepsis resuscitation processes and establish opportunities for the AI tool to mitigate gaps in the process. The second part examined adherence to AI recommendations based on factors like shift timing and user seniority, and whether shared risk in team decisions affects adherence. The findings revealed that while acknowledging the AI tool's potential benefits, participants would require a clear rationale explaining the AI results. They preferred AI suggestions that aligned with their views and did not risk patient safety, often seeking the confirmation of a colleague in uncertain situations. Overall, the study emphasised the cautious, context-dependent acceptance of AI recommendations in ICU settings. It also demonstrated the need for human factors studies to evaluate the user response to AI and its implications on decision-making.

Keywords: Artificial Intelligence, trust and acceptance, real-world evidence generation

DOI: 10.54941/ahfe1004657