A dynamic multi-attribute utility theory–based decision support system for patient prioritization in the emergency department

Abstract

The triage process may result in long waiting periods during which vital indicators of patients with apparently less urgent problems are not monitored after the initial triage. The integration of technology and decision theory has the potential to assist nurses in recognizing priorities by collecting data on the changing clinical information of patients and methodically organizing it. This study investigates the potential for integrating technology and multi-attribute utility theory (MAUT) to develop a dynamic decision support system (DSS) for patient prioritization in Emergency Department (ED) settings. An enhancement to the conventional MAUT model was made to incorporate changes in vital signs over time. A pilot study was conducted with data from 12 nurses and 47 patients. The dynamic MAUT model was assessed with a physician who made prioritization decisions independent of the model. A statistical analysis shows no significant difference between the recommendation proposed by the model and the decisions made by the physician. The results from the analysis give evidence for the potential benefits of combining technology with decision theory to aid nurses in prioritizing ED patients. These results can be used to further develop a DSS for dynamic patient prioritization in ED settings.