Abstract
In this paper, we will present the planning and design process of a triage decision-support application, aimed to be used for both research data gathering and real-time triage decision-making. Triage is an initial classification of emergency department (ED) patients, according to the severity level of their medical condition. The need of fast and accurate triage decision-making, lead to the development of widely used triage algorithms, such as ESI (Emergency Severity Index).
Observations and interviews with triage personnel exposed difficulties of triage process and helped us create an ESI-based decision making model. Next, we built a multiple-choice questioner to characterize the application and required features. 40 triage nurses completed the questioner. Results indicated that the most highly requested feature was an automated severity grade calculator, which became the core of the proposed design.
While current design focuses on the analytical decision model, statistical analysis of the questioner results indicated that it is often insufficient when facing medical reality complexities, dictating nurse’s frequent use of intuition. Using triage systems data analysis and modern machine-learning methodologies, we inspire to develop a second version of the application that will integrate intuitive insights into triage scale algorithmic decision process.