Emergency Centre Organization and Automated Triage System

TL;DR

This paper introduces an automated triage system for emergency centers that uses fuzzy inference, Q-learning, and genetic algorithms to optimize patient queueing, potentially reducing waiting times significantly.

Contribution

It presents a novel integrated system combining fuzzy logic, reinforcement learning, and genetic algorithms for automated triage and queue optimization in emergency care.

Findings

Simulations show a 48-minute reduction in average waiting time.

The system prioritizes urgent patients effectively.

The approach integrates multiple AI techniques in a user-friendly interface.

Abstract

The excessive rate of patients arriving at accident and emergency centres is a major problem facing South African hospitals. Patients are prioritized for medical care through a triage process. Manual systems allow for inconsistency and error. This paper proposes a novel system to automate accident and emergency centre triage and uses this triage score along with an artificial intelligence estimate of patient-doctor time to optimize the queue order. A fuzzy inference system is employed to triage patients and a similar system estimates the time but adapts continuously through fuzzy Q-learning. The optimal queue order is found using a novel procedure based on genetic algorithms. These components are integrated in a simple graphical user interface. Live tests could not be performed but simulations reveal that the average waiting time can be reduced by 48 minutes and priority is given to urgent patients