Optimizing Triage and Patient Flow in Emergency Departments: A Data-Driven Approach

Abstract

The optimization of triage and patient flow in emergency departments (EDs) is critical for
enhancing patient care and operational efficiency. This study presents a data-driven approach to
improve triage processes and patient flow management. By leveraging advanced data analytics
and machine learning algorithms, we analyze historical patient data to identify patterns and
predict patient influx, acuity levels, and resource needs. Our model incorporates real-time data
inputs to dynamically adjust triage priorities and resource allocation, ensuring timely and
appropriate care delivery. The implementation of this data-driven system in a high-volume ED
demonstrated significant reductions in patient wait times, improved patient satisfaction, and
more efficient use of medical staff and resources. This research highlights the potential of
integrating data analytics into ED operations to address the challenges of overcrowding and
resource constraints.