A Human-Centered Approach to Identifying the Challenges of Automatic Generation of Clinically Comprehensible Trauma Triage Explanations

Abstract

Rapidly understanding the rationale behind a model’s recommendation is vital in time-sensitive clinical situations such as trauma triage. Such environments may benefit from an automated translation of explanations from a tool such as LIME to a more clinical-friendly explanation that reduces the total amount of information presented to the paramedic and uses a more natural format and language. Generating clinician-friendly explanations requires an iterative process that reflects the goals, needs, knowledge, and values of the human decision-makers. In this paper, we apply concepts from Human-Centered eXplanable AI to assess an initial iteration of translating LIME-based explanations into a clinician-friendly language and format, and we successfully identify several high-priority tasks that need to be addressed to improve the explanation generation and evaluation process.