A cognitive task analysis for developing a clinical decision support system for emergency triage

Abstract

Introduction: The Emergency Department (ED) serves as a vital gateway to acute care, where timely and accurate triage decisions are essential to ensure appropriate patient prioritisation and efficient use of limited resources. Triage nurses operate in high-pressure environments and must make rapid decisions, often under conditions of uncertainty, relying on a blend of analytical reasoning and intuitive judgement. However, this complex decision-making process is susceptible to a range of challenges, including cognitive biases, communication break-downs, procedural inconsistencies, fatigue, and stress, all of which can compromise patient safety and care quality. This study explores the multifaceted nature of triage decision-making, focusing on the influencing factors, cognitive processes, and real-world challenges experienced by nurses. By deepening our understanding of these elements, the paper lays the groundwork for the development of effective Clinical Decision Support Systems (CDSS) that can enhance clinical judgement and support nurses in delivering safe, timely, and efficient emergency care.

Methods: The study used cognitive task analysis through interviews and observations to capture the cognitive strategies used by nurses during triage. This approach provided detailed insights into how nurses assess patient acuity, handle uncertainty, verify decisions, and manage challenges.

Results: This study identified 26 themes from interviews and observations, illustrating how nurses use experience and protocols such as the Emergency Severity Index to manage patient flow. Key challenges encountered in triage included overcrowding, staff shortages, high patient acuity, communication barriers, frequent interruptions, and multitasking demands. Despite these hurdles, nurses adapted through prioritization and collaboration.

Discussion: The findings highlight significant implications for emergency health care, mainly the need for improvements in triage decision making, resource utilization, and patient safety. Data-driven clinical decision support systems can enhance decision making, streamline assessments, reduce delays, and improve safety and equity in triage, particularly in high-stress, resource-constrained environments.

Relevance to Clinical Practice: This study has significant implications for clinical practice, particularly in emergency care settings where effective triage is critical for patient outcomes. By exploring the cognitive processes and challenges faced by triage nurses, the research provides valuable insights into the complexities of decision making under pressure. The findings emphasize the importance of clinical decision support systems to enhance decision accuracy, reduce cognitive load, and mitigate the risk of errors. Implementing data-driven technologies and refining triage protocols can lead to more efficient resource allocation, more streamlined workflows, reduced waiting times, and improved patient safety. By aligning clinical decision support system design with the cognitive processes of triage nurses, this study supports the development of tools that enhance decision accuracy, reduce cognitive load, and improve patient prioritization, ultimately promoting safer, faster, and more consistent triage in high-pressure emergency settings