{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T09:34:16Z","timestamp":1762162456801,"version":"build-2065373602"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Research Foundation of Korea (NRF) grant funded by the Korea Government","award":["No.2022R1A2C3004595"],"award-info":[{"award-number":["No.2022R1A2C3004595"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-025-03234-x","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T09:30:04Z","timestamp":1762162204000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From prediction to action: a retrospective observational study on the real-world implementation of Critical Interventions (CrIs), an AI-based clinical decision support system changing clinical behavior in the emergency department"],"prefix":"10.1186","volume":"25","author":[{"given":"Hansol","family":"Chang","sequence":"first","affiliation":[]},{"given":"Jae Yong","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Hyunjung","family":"Park","sequence":"additional","affiliation":[]},{"given":"Yee Jun","family":"Song","sequence":"additional","affiliation":[]},{"given":"Sejin","family":"Heo","sequence":"additional","affiliation":[]},{"given":"Jong Eun","family":"Park","sequence":"additional","affiliation":[]},{"given":"Gun Tak","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Se Uk","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Taerim","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Hee","family":"Yoon","sequence":"additional","affiliation":[]},{"given":"Sung Yeon","family":"Hwang","sequence":"additional","affiliation":[]},{"given":"Won Chul","family":"Cha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"issue":"1","key":"3234_CR1","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.jen.2004.11.002","volume":"31","author":"CM Fernandes","year":"2005","unstructured":"Fernandes CM, Tanabe P, Gilboy N, Johnson LA, McNair RS, Rosenau AM, Sawchuk P, Thompson DA, Travers DA, Bonalumi N, et al. Five-level triage: a report from the ACEP\/ENA Five-level triage task force. J Emerg Nurs. 2005;31(1):39\u201350. quiz 118.","journal-title":"J Emerg Nurs"},{"issue":"1","key":"3234_CR2","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1097\/MEJ.0000000000000397","volume":"25","author":"A Granstrom","year":"2018","unstructured":"Granstrom A, Strommer L, Schandl A, Ostlund A. A criteria-directed protocol for in-hospital triage of trauma patients. Eur J Emerg Med. 2018;25(1):25\u201331.","journal-title":"Eur J Emerg Med"},{"issue":"1","key":"3234_CR3","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s12245-017-0161-8","volume":"11","author":"JS Hinson","year":"2018","unstructured":"Hinson JS, Martinez DA, Schmitz PSK, Toerper M, Radu D, Scheulen J, Stewart de Ramirez SA, Levin S. Accuracy of emergency department triage using the emergency severity index and independent predictors of under-triage and over-triage in brazil: a retrospective cohort analysis. Int J Emerg Med. 2018;11(1):3.","journal-title":"Int J Emerg Med"},{"issue":"3","key":"3234_CR4","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.annemergmed.2006.05.019","volume":"49","author":"KV Iserson","year":"2007","unstructured":"Iserson KV, Moskop JC. Triage in medicine, part I: Concept, history, and types. Ann Emerg Med. 2007;49(3):275\u201381.","journal-title":"Ann Emerg Med"},{"issue":"50","key":"3234_CR5","first-page":"892","volume":"107","author":"M Christ","year":"2010","unstructured":"Christ M, Grossmann F, Winter D, Bingisser R, Platz E. Modern triage in the emergency department. Dtsch Arztebl Int. 2010;107(50):892\u20138.","journal-title":"Dtsch Arztebl Int"},{"issue":"5","key":"3234_CR6","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1016\/j.ajem.2017.10.030","volume":"36","author":"R Jouffroy","year":"2018","unstructured":"Jouffroy R, Saade A, Ellouze S, Carpentier A, Michaloux M, Carli P, Vivien B. Prehospital triage of septic patients at the SAMU regulation: comparison of qSOFA, MRST, MEWS and PRESEP scores. Am J Emerg Med. 2018;36(5):820\u20134.","journal-title":"Am J Emerg Med"},{"issue":"3","key":"3234_CR7","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.hrtlng.2019.02.005","volume":"48","author":"B Khwannimit","year":"2019","unstructured":"Khwannimit B, Bhurayanontachai R, Vattanavanit V. Comparison of the accuracy of three early warning scores with SOFA score for predicting mortality in adult sepsis and septic shock patients admitted to intensive care unit. Heart Lung. 2019;48(3):240\u20134.","journal-title":"Heart Lung"},{"issue":"5","key":"3234_CR8","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1111\/1742-6723.12455","volume":"27","author":"AR Allen","year":"2015","unstructured":"Allen AR, Spittal MJ, Nicolas C, Oakley E, Freed GL. Accuracy and interrater reliability of paediatric emergency department triage. Emerg Med Australas. 2015;27(5):447\u201352.","journal-title":"Emerg Med Australas"},{"issue":"S2","key":"3234_CR9","doi-asserted-by":"publisher","first-page":"S18","DOI":"10.1017\/cem.2017.365","volume":"19","author":"MJ Bullard","year":"2017","unstructured":"Bullard MJ, Musgrave E, Warren D, Unger B, Skeldon T, Grierson R, van der Linde E, Swain J. Revisions to the Canadian emergency department triage and acuity scale (CTAS) guidelines 2016. CJEM. 2017;19(S2):S18\u201327.","journal-title":"CJEM"},{"issue":"4","key":"3234_CR10","doi-asserted-by":"publisher","first-page":"305","DOI":"10.4258\/hir.2019.25.4.305","volume":"25","author":"SW Choi","year":"2019","unstructured":"Choi SW, Ko T, Hong KJ, Kim KH. Machine Learning-Based prediction of Korean triage and acuity scale level in emergency department patients. Healthc Inf Res. 2019;25(4):305\u201312.","journal-title":"Healthc Inf Res"},{"key":"3234_CR11","doi-asserted-by":"publisher","first-page":"101762","DOI":"10.1016\/j.artmed.2019.101762","volume":"102","author":"M Fernandes","year":"2020","unstructured":"Fernandes M, Vieira SM, Leite F, Palos C, Finkelstein S, Sousa JMC. Clinical decision support systems for triage in the emergency department using intelligent systems: a review. Artif Intell Med. 2020;102:101762.","journal-title":"Artif Intell Med"},{"issue":"7","key":"3234_CR12","doi-asserted-by":"publisher","first-page":"e0201016","DOI":"10.1371\/journal.pone.0201016","volume":"13","author":"WS Hong","year":"2018","unstructured":"Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS ONE. 2018;13(7):e0201016.","journal-title":"PLoS ONE"},{"issue":"2","key":"3234_CR13","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1111\/acem.14190","volume":"28","author":"H Kareemi","year":"2021","unstructured":"Kareemi H, Vaillancourt C, Rosenberg H, Fournier K, Yadav K. Machine learning versus usual care for diagnostic and prognostic prediction in the emergency department: A systematic review. Acad Emerg Med. 2021;28(2):184\u201396.","journal-title":"Acad Emerg Med"},{"issue":"1","key":"3234_CR14","doi-asserted-by":"publisher","first-page":"10537","DOI":"10.1038\/s41598-022-14422-4","volume":"12","author":"H Chang","year":"2022","unstructured":"Chang H, Yu JY, Yoon S, Kim T, Cha WC. Machine learning-based suggestion for critical interventions in the management of potentially severe conditioned patients in emergency department triage. Sci Rep. 2022;12(1):10537.","journal-title":"Sci Rep"},{"issue":"12","key":"3234_CR15","doi-asserted-by":"publisher","first-page":"3842","DOI":"10.3390\/jcm9123842","volume":"9","author":"H Chang","year":"2020","unstructured":"Chang H, Yu JY, Yoon SY, Hwang SY, Yoon H, Cha WC, Sim MS, Jo IJ, Kim T. Impact of COVID-19 pandemic on the overall diagnostic and therapeutic process for patients of emergency department and those with acute cerebrovascular disease. J Clin Med. 2020;9(12):3842.","journal-title":"J Clin Med"},{"issue":"4","key":"3234_CR16","first-page":"77","volume":"31","author":"DH Lee","year":"2016","unstructured":"Lee DH, Jung HY, Kim MH, Lim ME, Kim DH, Han YW, Lee YW, Choi JH, Kim SH. Trends of clinical decision support system (CDSS). Electron Telecommun Trends. 2016;31(4):77\u201385.","journal-title":"Electron Telecommun Trends"},{"issue":"2","key":"3234_CR17","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s10877-016-9849-1","volume":"31","author":"A Belard","year":"2017","unstructured":"Belard A, Buchman T, Forsberg J, Potter BK, Dente CJ, Kirk A, Elster E. Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care. J Clin Monit Comput. 2017;31(2):261\u201371.","journal-title":"J Clin Monit Comput"},{"issue":"6","key":"3234_CR18","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1002\/emp2.12277","volume":"1","author":"A Kirubarajan","year":"2020","unstructured":"Kirubarajan A, Taher A, Khan S, Masood S. Artificial intelligence in emergency medicine: A scoping review. J Am Coll Emerg Physicians Open. 2020;1(6):1691\u2013702.","journal-title":"J Am Coll Emerg Physicians Open"},{"issue":"6","key":"3234_CR19","first-page":"547","volume":"28","author":"J Park","year":"2017","unstructured":"Park J, Lim T. Korean triage and acuity scale (KTAS). J Korean Soc Emerg Med. 2017;28(6):547\u201351.","journal-title":"J Korean Soc Emerg Med"},{"key":"3234_CR20","unstructured":"The Korea Society of Emergency Medicine. Korean Triage and Acuity Scale. 2023. http:\/\/www.ktas.org\/. Accessed 26 June 2024."},{"issue":"1","key":"3234_CR21","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1186\/s13012-018-0772-3","volume":"13","author":"S Van de Velde","year":"2018","unstructured":"Van de Velde S, Kunnamo I, Roshanov P, Kortteisto T, Aertgeerts B, Vandvik PO, Flottorp S. Panel ge: the GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support. Implement Sci. 2018;13(1):86.","journal-title":"Implement Sci"},{"key":"3234_CR22","doi-asserted-by":"publisher","first-page":"102684","DOI":"10.1016\/j.media.2022.102684","volume":"84","author":"W Jin","year":"2023","unstructured":"Jin W, Li X, Fatehi M, Hamarneh G. Guidelines and evaluation of clinical explainable AI in medical image analysis. Med Image Anal. 2023;84:102684.","journal-title":"Med Image Anal"},{"issue":"11","key":"3234_CR23","doi-asserted-by":"publisher","first-page":"e052663","DOI":"10.1136\/bmjopen-2021-052663","volume":"11","author":"A Naemi","year":"2021","unstructured":"Naemi A, Schmidt T, Mansourvar M, Naghavi-Behzad M, Ebrahimi A, Wiil UK. Machine learning techniques for mortality prediction in emergency departments: a systematic review. BMJ Open. 2021;11(11):e052663.","journal-title":"BMJ Open"},{"issue":"1","key":"3234_CR24","doi-asserted-by":"publisher","first-page":"e740","DOI":"10.1002\/ams2.740","volume":"9","author":"B Mueller","year":"2022","unstructured":"Mueller B, Kinoshita T, Peebles A, Graber MA, Lee S. Artificial intelligence and machine learning in emergency medicine: a narrative review. Acute Med Surg. 2022;9(1):e740.","journal-title":"Acute Med Surg"},{"issue":"4","key":"3234_CR25","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s10140-020-01794-1","volume":"27","author":"SK Moulik","year":"2020","unstructured":"Moulik SK, Kotter N, Fishman EK. Applications of artificial intelligence in the emergency department. Emerg Radiol. 2020;27(4):355\u20138.","journal-title":"Emerg Radiol"},{"issue":"3","key":"3234_CR26","doi-asserted-by":"publisher","first-page":"R97","DOI":"10.1186\/cc13868","volume":"18","author":"V Beck","year":"2014","unstructured":"Beck V, Chateau D, Bryson GL, Pisipati A, Zanotti S, Parrillo JE, Kumar A, Group CAToSSDR. Timing of vasopressor initiation and mortality in septic shock: a cohort study. Crit Care. 2014;18(3):R97.","journal-title":"Crit Care"},{"issue":"1","key":"3234_CR27","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s13613-022-01021-9","volume":"12","author":"MA Ammar","year":"2022","unstructured":"Ammar MA, Ammar AA, Wieruszewski PM, Bissell BD, Long T, Albert M, Khanna L, Sacha AK. Timing of vasoactive agents and corticosteroid initiation in septic shock. Ann Intensiv Care. 2022;12(1):47.","journal-title":"Ann Intensiv Care"},{"key":"3234_CR28","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.jcrc.2019.11.004","volume":"55","author":"DC Hidalgo","year":"2020","unstructured":"Hidalgo DC, Patel J, Masic D, Park D, Rech MA. Delayed vasopressor initiation is associated with increased mortality in patients with septic shock. J Crit Care. 2020;55:145\u20138.","journal-title":"J Crit Care"},{"key":"3234_CR29","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.ijmedinf.2018.01.011","volume":"112","author":"RW Grout","year":"2018","unstructured":"Grout RW, Cheng ER, Carroll AE, Bauer NS, Downs SM. A six-year repeated evaluation of computerized clinical decision support system user acceptability. Int J Med Inf. 2018;112:74\u201381.","journal-title":"Int J Med Inf"},{"issue":"5","key":"3234_CR30","doi-asserted-by":"publisher","first-page":"e228","DOI":"10.1002\/mp.13562","volume":"47","author":"G Mahadevaiah","year":"2020","unstructured":"Mahadevaiah G, Rv P, Bermejo I, Jaffray D, Dekker A, Wee L. Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance. Med Phys. 2020;47(5):e228\u201335.","journal-title":"Med Phys"},{"issue":"5","key":"3234_CR31","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1055\/s-0042-1757923","volume":"13","author":"D Rubins","year":"2022","unstructured":"Rubins D, McCoy AB, Dutta S, McEvoy DS, Patterson L, Miller A, Jackson JG, Zuccotti G, Wright A. Real-Time user feedback to support clinical decision support system improvement. Appl Clin Inf. 2022;13(5):1024\u201332.","journal-title":"Appl Clin Inf"},{"key":"3234_CR32","doi-asserted-by":"publisher","first-page":"103917","DOI":"10.1016\/j.jbi.2021.103917","volume":"123","author":"L Souza-Pereira","year":"2021","unstructured":"Souza-Pereira L, Ouhbi S, Pombo N. A process model for quality in use evaluation of clinical decision support systems. J Biomed Inf. 2021;123:103917.","journal-title":"J Biomed Inf"},{"issue":"47","key":"3234_CR33","doi-asserted-by":"publisher","first-page":"e35992","DOI":"10.1097\/MD.0000000000035992","volume":"102","author":"Y Cho","year":"2023","unstructured":"Cho Y, Yeo IH, Lee DE, Kim JK. Coronavirus disease pandemic impact on emergency department visits for cardiovascular disease in korea: a review. Medicine. 2023;102(47):e35992.","journal-title":"Medicine"},{"issue":"Suppl","key":"3234_CR34","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.15441\/ceem.23.151","volume":"10","author":"HH Yoo","year":"2023","unstructured":"Yoo HH, Ro YS, Ko E, Lee J-H, Han S-h, Kim T, Shin TG, Kim S, Chang H. Epidemiologic trends of patients who visited nationwide emergency departments: a report from the National emergency department information system (NEDIS) of Korea, 2018\u20132022. Clin Experimental Emerg Med. 2023;10(Suppl):S1.","journal-title":"Clin Experimental Emerg Med"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03234-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03234-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03234-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T09:30:07Z","timestamp":1762162207000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03234-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,3]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3234"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03234-x","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,3]]},"assertion":[{"value":"7 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Institutional Review Board (IRB) of the Samsung Medical Center. The Institutional Review Board of Samsung Medical Center waived the need for informed consent because of the retrospective, observational, and anonymous nature of the study by the institutional review board (IRB) of Samsung Medical Center. (IRB No. 2022-04-116-001). This study was conducted in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"405"}}