Implementing Triage-Bot: Supporting the Current Practice for Triage Nurses
Authors:   Queen’s University School of Nursing/Health Quality Programs, Kingston, Ontario, Canada, Sears Kim , Belbin Sam ,   Bader College, Queen’s University, Herstmonceux Castle, England, UK, Rashno Elyas ,   Queen’s School of Computing, Queen’s University, Kingston, Ontario, Canada, Sharma Drishti ,   Queen’s School of Computing, Queen’s University, Kingston, Ontario, Canada, Woo Kevin ,   Queen’s University School of Nursing/Health Quality Programs, Kingston, Ontario, Canada, Zulkernine Farhana ,   Queen’s School of Computing, Queen’s University, Kingston, Ontario, Canada, Neagu Ciprian Daniel ,   School of Computer Science, Artificial Intelligence and Electronics, University of Bradford, UK, Amani Bita ,   Faculty of Law, Queen’s University, Kingston, Ontario, Canada, Alaca Furkan ,   Queen’s School of Computing, Queen’s University, Kingston, Ontario, Canada
Published: September 2024

In Canada, emergency departments (ED) have 15.1 million unscheduled visits every year; this has been suggested to indicate that patients rely on ED to address the gaps experienced by 6.5 million Canadians who lack a primary care provider. When this large number of visits is coupled with a predicted shortage of 100,000 nurses in Canada by 2030, ED can be expected to face resource limitations, which highlights the importance of triage systems as a source of immediate support. Technology that incorporates innovative analytical methods, automation of routine, and efficient processing can be leveraged to enhance patient outcomes, streamline clinical processes, and improve the overall quality and efficiency of healthcare delivery. This paper aims to highlight how the Triage-Bot, a proposed AI system, can assist ED nurses when triaging patients. The Triage-Bot system is based on the Canadian Triage and Acuity Scale (CTAS), which currently serves as a standardized and highly effective tool for prioritizing patient care in emergency departments across the country. Pre-set and open-ended questions are asked using voice and video, allowing patients to describe their health concerns and conditions. Triage-Bot automatically measures the following vital signs: heart rate (HR), heart rate variability (HRV), oxygen saturation (SpO2), respiratory rate (RR), blood pressure (BP), blood glucose (BG), and stress. The system uses artificial intelligence models, particularly those with a deep learning approach that simultaneously analyzes both the user's facial expression and voice tone. Implementation: A systematic review addressed the implications of AI in nursing and concluded that it could contribute to patient care by providing personalized instructions and/or remotely monitoring patients. The Triage-Bot system can be implemented in healthcare facilities, such as emergency department waiting rooms. The information it collects can then be added to a patient’s health records to support nurses in assessing the severity of each patient’s condition. Limitations: If the system is accessed without a nurse’s guidance, it is imperative that the user receives information regarding when to visit a healthcare provider or ED. Continuous improvements in Triage-Bot’s accessibility for patients with varying abilities are required to ensure that the system remains user-friendly during times of illness. The voice and text interaction can also be influenced by a user’s understanding of language, culture, and age-related factors.