Towards AI-powered Emergency Queueing System with Patient Privacy Protection Model for Australian Hospitals

This paper proposes an AI-powered queueing system for hospital emergency departments (ED) in Australia. The system will prioritize patient privacy and improve efficiency as increasing demand has led to longer waiting times (Wang et al., 2018). Currently, the Australasian Triage Scale (ATS) is inconsistently applied and does not function as a queueing tool, causing delays in patient care and overcrowding in emergency departments.
The proposed solution integrates machine learning to predict the principal diagnosis based on data from the Australian Institute of Health and Welfare, drawing on machine-learning techniques for disease diagnosis (Ahsan et al., 2022). It enhances the ATS by including additional clinical descriptors such as pain, airway, and circulation, ensuring a more accurate assignment of treatment acuity. The queueing model asynchronously prioritizes patients based on multiple factors, such as the time of triage and clinical urgency, rather than solely relying on the ATS category. This method aims to minimize subjectivity in triage decisions, improving the fairness and efficiency of patient prioritization (Wang et al., 2018). Additionally, the system incorporates a healthcare data privacy protection model to safeguard patient information, with a focus on balancing security and privacy in big healthcare data (Abouelmehdi et al., 2018). The specific technique for privacy protection will be selected following a review of existing healthcare protection models.
The expected outcomes of this study include an increase in the percentage of patients “seen on time” and a reduction in the total time spent in emergency departments. This would help alleviate ED overcrowding, improve patient satisfaction, and optimize the overall emergency care process (Raghupathi and Raghupathi, 2014). The AI-powered system offers a novel approach to managing ED queues more effectively, benefiting both patients and healthcare providers.