Digital and artificial ıntelligence-supported triage system in emergency department
DOI:
https://doi.org/10.47243/jos.2857Keywords:
Artificial Intelligence, Triage, Emergency Department, Digital Health, Decision Support SystemsAbstract
Aim: This study aims to evaluate the potential, feasibility, and current limitations of artificial intelligence (AI)-supported triage systems in emergency departments by examining their impact on healthcare services. Traditional triage systems rely on subjective assessments, which contribute to increased issues such as patient overcrowding and resource insufficiencies. In this context, AI-based systems enable faster, more objective, and consistent prioritization. Materials and Methods: Original research and review articles published between 2020 and 2025 were searched in relevant databases. Results: According to the literature review, AI-supported triage systems accelerate patient prioritization, reduce the cognitive load of healthcare workers, and offer advantages such as improving patient outcomes and decreasing waiting times. However, significant challenges remain regarding data security, lack of algorithmic transparency, ethical concerns, and system integration. Discussion and Conclusion: AI-supported digital triage systems have the potential to enhance efficiency and the quality of patient care in emergency departments. For their safe and effective widespread adoption, multicenter prospective clinical studies are necessary, alongside strengthening digital infrastructure and implementing comprehensive training programs for healthcare professionals.
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ANDRADE MAGALHÃES, M. E., LEMES DA SILVA, C. V., MONTEIRO DE OLIVEIRA, H., RODRIGUES DE LIMA, A. B., SALUM FLORES, M. T., FERREIRA LEITE, I.,& ZANONI, R. D. (2024). The use of artificial intelligence in patient triage in emergency departments: An integrative review. Environmental & Social Management Journal/Revista de Gestão Social e Ambiental, 18(12). https://doi.org/10.24857/rgsa.v18n12-052
BERLYAND, Y., RAJA, A. S., DORNER, S. C., PRABHAKAR, A. M., SONIS, J. D., GOTTUMUKKALA, R. V., SUCCI, M. D., & YUN, B. J. (2018). How artificial intelligence could transform ED operations. The American Journal of Emergency Medicine, 36(8), 1515–1517. https://doi.org/10.1016/j.ajem.2018.01.017
CHANG, Y.-H., LIN, Y.-C., HUANG, F.-W., CHEN, D.-M., CHUNG, Y.-T., CHEN, W.-K., & WANG, C. C. N. (2024). Using machine learning and natural language processing in triage for prediction of clinical disposition in the ED. BMC Emergency Medicine, 24, 237. https://doi.org/10.1186/s12873-024-01152-1
CHRISTIAN, M. D. (2019). Triage. Critical Care Clinics,35(4),575–589. https://doi.org/10.1016/j.ccc.2019.06.009
DA'COSTA, A., TEKE, J., ORIGBO, J. E., OSONUGA, A., EGBON, E., & OLAWADE, D. B. (2025). AI-driven triage in emergency departments: A review of benefits, challenges, and future directions. International Journal of Medical Informatics, 197,105838. https://doi.org/10.1016/j.ijmedinf.2025.105838
FARROHKNIA, N., CASTRÉN, M., EHRENBERG, A., LIND, L., OREDSSON, S., JONSSON, H., ASPLUND, K., & GÖRANSSON, K. E. (2011). Emergency department triage scales and their components: A systematic review of the scientific evidence. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine,19,42. https://doi.org/10.1186/1757-7241-19-42
FATAHI, M.(2025). A review of how artificial intelligence could influence the emergency department workflow. Journal of Health and Medical Sciences,8(1),48–58. https://doi.org/10.31014/aior.1994.08.01.228
FRIEDMAN, A. B., DELGADO, M. K., & WEISSMAN, G. E. (2024). Artificial intelligence for emergency care triage—Much promise, but still much to learn. JAMA Network Open, 7(5), e248857. https://doi.org/10.1001/jamanetworkopen.2024.8857
IVANOV, O., MOLANDER, K., DUNNE, R., LIU, S., BRECHER, D., MASEK, K., LEWIS, E., WOLF, L., TRAVERS, D., DELANEY, D., MONTGOMERY, K., & REILLY, C. (2022). Detection of sepsis during emergency department triage using machine learning.arXiv.https://doi.org/10.48550/arXiv.2204.07657
KACHMAN, M. M., BRENNAN, I., OSKVAREK, J. J., WASEEM, T., & PINES, J. M. (2024). How artificial intelligence could transform emergency care. The American Journal of Emergency Medicine, 81, 40–46. https://doi.org/10.1016/j.ajem.2024.04.024
MOULIK, S. K., KOTTER, N., & FISHMAN, E. K. (2020). Applications of artificial intelligence in the emergency department. Emergency Radiology, 27(4), 355–358. https://doi.org/10.1007/s10140-020-01794-1
PORTO, B. M. (2024). Improving triage performance in emergency departments using machine learning and natural language processing: A systematic review. BMC Emergency Medicine, 24, 219. https://doi.org/10.1186/s12873-024-01135-2
ROBERTSON-STEEL, I. (2006). Evolution of triage systems. Emergency Medicine Journal: EMJ, 23(2), 154–155. https://doi.org/10.1136/emj.2005.030270
SAMADBEIK, M., STAIB, A., BOYLE, J., KHANNA, S., BOSLEY, E., BODNAR, D., LIND, J., AUSTIN, J. A., TANNER, S., MESHKAT, Y., DE COURTEN, B., & SULLIVAN, C. (2024). Patient flow in emergency departments: A comprehensive umbrella review of solutions and challenges across the health system. BMC Health Services Research, 24(1), 274. https://doi.org/10.1186/s12913-024-10725-6
TAHERNEJAD, A., SAHEBI, A., ABADI, A. S. S., & SAFARI, M. (2024). Application of artificial intelligence in triage in emergencies and disasters: A systematic review. BMC Public Health, 24(1), 3203. https://doi.org/10.1186/s12889-024-20447-3
YADGAROV, M. Y., LANDONI, G., BERIKASHVILI, L. B., POLYAKOV, P. A., KADANTSEVA, K. K., SMIRNOVA, A. V., KUZNETSOV, I. V., SHEMETOVA, M. M., YAKOVLEV, A. A., & LIKHVANTSEV, V. V. (2024). Early detection of sepsis using machine learning algorithms: A systematic review and network meta-analysis. Frontiers in Medicine, 11, 1491358. https://doi.org/10.3389/fmed.2024.1491358
YAN, M. Y., GUSTAD, L. T., & NYTRO, O. (2022). Sepsis prediction, early detection, and identification using clinical text for machine learning: A systematic review. Journal of the American Medical Informatics Association: JAMIA, 29(3), 559–575. https://doi.org/10.1093/jamia/ocab236
YANCEY, C. C., & O'ROURKE, M. C. (2023). Emergency department triage. In StatPearls [Internet]. Treasure Island, FL: StatPearls Publishing. Retrieved from.https://www.ncbi.nlm.nih.gov/books/NBK557583
ZABOLI, A. (2024). Establishing a common ground: The future of triage systems. BMC Emergency Medicine, 24(1), 148. https://doi.org/10.1186/s12873-024-01070-2
ZACHARIASSE, J. M., VAN DER HAGEN, V., SEIGER, N., MACKWAY-JONES, K., VAN VEEN, M., & MOLL, H. A. (2019). Performance of triage systems in emergency care: A systematic review and meta-analysis. BMJ Open, 9(5), e026471. https://doi.org/10.1136/bmjopen-2018-026471
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