Summary
Emergency medical dispatchers work in a fast-paced environment making critical decisions about sending help. This study looks closely at how they do this work by watching and talking with them over time. The authors found that the first step of the dispatch process is where AI tools could help the most without disrupting the flow or safety of the work. Their goal is to design AI that supports these dispatchers to reduce their workload and improve patient care. The study highlights the complex human communication and decision-making involved, showing careful steps for adding AI assistance.
Emergency medical dispatchCritical careAI assistanceSituated actionWorkflow analysisEthnographic studyCognitive demandsAlgorithm designHuman-computer interactionPre-hospital care
Authors
Ben Wilson, Matt Roach, Greg Browning, Chris Connor, David Rawlinson
Abstract
This study uses ethnographic immersion and observation as contextual inquiry to understand situated action at an Emergency Medical Dispatch critical care hub. The work is a response to the urgent need to recruit context-specific knowledge and participation into design that helps to narrow the AI Chasm - the gap between the promise of Artificial Intelligence (AI) systems and what they deliver for clinicians and their patients. The work of a pre-hospital critical care team's dispatch process is described and analysed to reveal both the structure of the workflow and the different cognitive demands it makes on staff tasked with dispatch decision-making. Elements of attention, communication and focus between the humans, as they carry out this work, are drawn out in order to understand the work-as-done and identify the many dependencies in the process. The motivation is to establish where AI support might be useful and to discover what challenges there could be in designing appropriate algorithmic assistance. We ask whether, where and how the design and implementation of an AI system might be considered. The ultimate objective is to improve the decision process itself to the benefit of clinicians and patients. The study identifies three key steps in the situated workflow and details how decision-makers negotiate each one as emergency calls follow complex routes between them. We find compelling evidence that the first of these decision steps constitutes the most promising candidate for unobtrusive assistance that could be safe and effective in improving both clinician workload and clinical outcomes.