Deep attention model for triage of emergency department patients

Abstract

© 2018 by SIAM. Optimization of patient throughput and wait time in emer-gency departments (ED) is an important task for hospital systems. For that reason, Emergency Severity Index (ESI) system for patient triage was introduced to help guide man-ual estimation of acuity levels, which is used by nurses to rank the patients and organize hospital resources. However, despite improvements that it brought to managing medical resources, such triage system greatly depends on nurse's sub-jective judgment and is thus prone to human errors. Here, we propose a novel deep model based on the word attention mechanism designed for predicting a number of resources an ED patient would need. Our approach incorporates rou-tinely available continuous and nominal (structured) data with medical text (unstructured) data, including patient's chief complaint, past medical history, medication list, and nurse assessment collected for 338,500 ED visits over three years in a large urban hospital. Using both structured and unstructured data, the proposed approach achieves the AUC of ∼ 88% for the task of identifying resource intensive pa-tients (binary classification), and the accuracy of ∼ 44% for predicting exact category of number of resources (multi-class classification task), giving an estimated lift over nurses' per-formance by 16% in accuracy. Furthermore, the attention mechanism of the proposed model provides interpretabil-ity by assigning attention scores for nurses' notes which is crucial for decision making and implementation of such ap-proaches in the real systems working on human health.