use cases

Enhancing Healthcare Triage

Addressing challenges in triage services with AI by automating tasks and reducing waiting times in waiting lists and administrative burden.


The central issue in triage services lies in the need for accurate and timely assessment of patients' medical conditions. This evaluation is essential for improving patient outcomes and healthcare system efficiency. However, inaccurate triage can lead to a range of issues, including delays in care and the provision of low-value services (1). For example, up to two-thirds of emergency department visits are estimated to be unnecessary and avoidable, resulting in excessive healthcare spending at the national level. This underscores the critical need to implement more effective and precise triage systems in healthcare settings (2)(3).

Size of the Problem:

  • Up to two-thirds of emergency department visits are unnecessary and avoidable (3).
  • This results in an excess of $32 billion in healthcare spending at the national level (2).
  • Emergency department wait times are increasing, with a growing number of patients waiting over 12 hours from admission to decision (1).
  • Gastroenterology consultants at the Western General Hospital in Edinburgh triage approximately 30 to 40 referrals per day (4).
  • The triage process is complex, with over 120 permutations of identified outcomes (4).

Why it matters

The problem of inaccurate triage in healthcare services represents a significant concern as it leads to treatment delays, the provision of low-value care, and excessive healthcare spending nationally. This situation is further exacerbated by increasingly long wait times in emergency departments, with a growing number of patients waiting over 12 hours from admission to decision. Additionally, the complexity of the triage process in specialties like gastroenterology underscores the urgent need to implement more effective and precise systems. Improving the accuracy and efficiency of triage would not only benefit patients by enhancing their health outcomes but also alleviate the burden on healthcare professionals and reduce costs associated with healthcare.


  1. Automated Priority Classification with AI: Utilizing machine learning algorithms, AI analyzes clinical data, symptoms, and patient histories to automatically assign priority levels. This helps healthcare professionals make quicker, more informed decisions, optimizing the triage process and improving patient care.
  2. Real-Time Data Analysis and Resource Optimization: AI systems such as the Electronic Triage System (ETS) enhance patient severity distribution in emergency departments by analyzing real-time data. ETS improves triage accuracy and resource allocation, ensuring timely care for critical patients. This system has shown to enhance decision-making with more equitable distribution of patients across severity levels.
  3. AI-Powered Chatbots and Virtual Assistants: Advanced AI chatbots and virtual assistants support clinical staff by efficiently collecting and analyzing patient information, prioritizing care based on the assessed urgency, and guiding patients to appropriate care pathways. These tools significantly improve the accuracy of patient classification and streamline the triage process.
  4. Digital Health Assistant for Enhanced Triage: We have developed a digital assistant that specializes in initial medical assessments to assist healthcare professionals in hospital settings. This assistant is designed to streamline triage processes by gathering critical patient information, assessing the severity of their symptoms, and providing initial recommendations for the next steps in care. Its integration into clinical settings helps to enhance the efficiency of triage, allowing specialists and nurses to focus more on direct patient care and less on administrative tasks.
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  • National Hospital and Ambulatory Medical Care Survey (NHAMCS): Provides detailed data on emergency department visits and outpatient consultations in the United States, including demographic information, medical conditions, procedures performed, and care outcomes.
  • Electronic Health Records (EHR): Comprehensive records containing patients' medical history, diagnoses, treatments, laboratory test results, etc., offering valuable insights into patient needs and clinical outcomes prediction.
  • Clinical Data Warehouses: Store clinical data from various sources such as hospital information systems, electronic medical records, and connected medical devices, providing a wide range of information for triage analysis and resource optimization.
  • Public Health Databases: Sources like the Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) offer epidemiological data, disease trends, and health statistics at global and national levels, relevant for emergency resource planning and management.
  • Research Databases: Platforms such as PubMed, MEDLINE, or Scopus contain a wealth of clinical studies, systematic reviews, and meta-analyses, offering insights into triage practices, outcome prediction models, and resource management strategies.


  1. Delshad S, Dontaraju VS, Chengat V. Artificial Intelligence-Based Application Provides Accurate Medical Triage Advice When Compared to Consensus Decisions of Healthcare Providers. Cureus. 2021 Aug 6;13(8):e16956. doi: 10.7759/cureus.16956. PMID: 34405077; PMCID: PMC8352839.
  2. Dugas AF, Kirsch TD, Toerper M, Korley F, Yenokyan G, France D, Hager D, Levin S. An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes. J Emerg Med. 2016 Jun;50(6):910-8. doi: 10.1016/j.jemermed.2016.02.026. Epub 2016 Apr 25. Erratum in: J Emerg Med. 2016 Aug;51(2):224. PMID: 27133736.
  3. Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA Jr, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019 Feb 22;23(1):64. doi: 10.1186/s13054-019-2351-7. PMID: 30795786; PMCID: PMC6387562.
  4. Using intelligent automation to improve the triage and referral management pathway. (n.d.). NHS Transformation Directorate.
  5. Tool Developed to Assist with Triage in the Emergency Department. (2022, November 3). Johns Hopkins Medicine.

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