References

1. Calder LA, Forster AJ, Stiell IG, et al. Mapping out the emergency department disposition decision for high-acuity patients. Ann Emerg Med. 2012;60(5):567-576. e4.
2. Wiler JL, Gentle C, Halfpenny JM, et al. Optimizing emergency department front-end operations. Annals of emergency medicine. 2010;55(2):142-160.e1.
3. Hinson JS, Martinez DA, Cabral S, et al. Triage Performance in Emergency Medicine: A Systematic Review. Annals of Emergency Medicine. Published online November 22, 2018.
4. Ahrq Q. Emergency Severity Index (ESI): A Triage Tool for Emergency Department Care. 4th ed. (Gilboy N, Tanabe P, Travers D, Rosenau AM, eds.).; 2012.
5. McHugh M, Tanabe P, McClelland M, Khare RK. More patients are triaged using the Emergency Severity Index than any other triage acuity system in the United States. Academic Emergency Medicine. 2012;19(1):106-109.
6. Mistry B, Stewart De Ramirez S, Kelen G, et al. Accuracy and Reliability of Emergency Department Triage Using the Emergency Severity Index: An International Multicenter Assessment. Annals of Emergency Medicine. 2018;71(5):581-587.e3.
7. Zhang X, Carabello M, Hill T, Bell SA, Stephenson R, Mahajan P. Trends of Racial/Ethnic Differences in Emergency Department Care Outcomes Among Adults in the United States From 2005 to 2016. Front Med (Lausanne). 2020;7:300.
8. Mistry B, Balhara KS, Hinson JS, et al. Nursing Perceptions of the Emergency Severity Index as a Triage Tool in the United Arab Emirates: A Qualitative Analysis. J Emerg Nurs. 2018;44(4):360-367.
9. Joseph JW, Landry AM, Kennedy M, et al. Association of Race and Ethnicity With Triage Emergency Severity Index Scores and Total Visit Work Relative Value Units for Emergency Department Patients. JAMA Network Open. 2022;5(9):e2231769.
10. Chmielewski N, Moretz J. ESI Triage Distribution in U.S. Emergency Departments. Adv Emerg Nurs J. 2022;44(1):46-53.
11. Levin S, Toerper M, Hamrock E, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Annals of Emergency Medicine. 2018;71(5):565-574.
12. Dugas AF, Kirsch TD, Toerper M, et al. An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes. Journal of Emergency Medicine. 2016;50(6):910-918.
13. Sax DR, Warton EM, Mark DG, et al. Evaluation of the Emergency Severity Index in US Emergency Departments for the Rate of Mistriage. JAMA Network Open. 2023;6(3):e233404.
14. Zachariasse JM, van der Hagen V, Seiger N, Mackway-Jones K, van Veen M, Moll HA. Performance of triage systems in emergency care: a systematic review and meta-analysis. BMJ Open. 2019;9(5):e026471.
15. Vigil JM, Alcock J, Coulombe P, et al. Ethnic Disparities in Emergency Severity Index Scores among U.S. Veteran’s Affairs Emergency Department Patients. PLoS One. 2015;10(5):e0126792.
16. Platts-Mills TF, Travers D, Biese K, et al. Accuracy of the Emergency Severity Index triage instrument for identifying elder emergency department patients receiving an immediate life-saving intervention. Acad Emerg Med. 2010;17(3):238-243.
17. Grossmann FF, Zumbrunn T, Frauchiger A, Delport K, Bingisser R, Nickel CH. At risk of undertriage? Testing the performance and accuracy of the emergency severity index in older emergency department patients. Ann Emerg Med. 2012;60(3):317-325.e3.
18. Ginsburg AD, Oliveira J E Silva L, Mullan A, et al. Should age be incorporated into the adult triage algorithm in the emergency department? Am J Emerg Med. 2021;46:508-514.
19. Malinovska A, Pitasch L, Geigy N, Nickel CH, Bingisser R. Modification of the Emergency Severity Index Improves Mortality Prediction in Older Patients. West J Emerg Med. 2019;20(4):633-640.
20. 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. Artificial Intelligence in Medicine. 2020;102:101762.
21. Miles J, Turner J, Jacques R, Williams J, Mason S. Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review. Diagnostic and Prognostic Research. 2020;4(1):16.
22. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Intern Med. 2018;178(11):1544-1547.
23. Parikh RB, Teeple S, Navathe AS. Addressing Bias in Artificial Intelligence in Health Care. JAMA. 2019;322(24):2377-2378.
24. Yang J, Soltan AAS, Eyre DW, Yang Y, Clifton DA. An adversarial training framework for mitigating algorithmic biases in clinical machine learning. NPJ Digit Med. 2023;6:55.
25. Agarwal R, Bjarnadottir M, Rhue L, et al. Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework. Health Policy and Technology. 2023;12(1):100702.
26. Success Stories From the AHRQ Digital Healthcare Research Program – HopScore: An Electronic Outcomes-Based Emergency Triage System. Published online January 23, 2020. Accessed January 23, 2020.
27. Levin S, Toerper M, Hinson J, et al. Machine-learning-based electronic triage: a prospective evaluation. Annals of Emergency Medicine. 2018;72(4).

Disclosure

Dr. Hinson, Dr. Levin and Johns Hopkins University are entitled to royalty distributions related to CDS technology that was evaluated. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with conflict-of-interest policies.