cirpublications.com-iTNS-1790883435-w028037728 1790883435 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2024 4 1 Chinedu Okafor Amina Bello 2024 10.68159/w028037728 https://cirpublications.com/pub/journal/2/article/w028037728 Kane EM, Scheulen JJ, Püttgen A, Martinez D, Levin S, Bush BA, et al. Use of systems engineering to design a hospital command center. Jt Comm J Qual Patient Saf. 2019;45(5):370-9. McInerney C, McCrorie C, Benn J, Habli I, Lawton T, Mebrahtu TF, et al. Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol. BMJ Open. 2022;12(3):e054090. Mebrahtu TF, McInerney CD, Benn J, McCrorie C, Granger J, Lawton T, et al. The impact of hospital command centre on patient flow and data quality: findings from the UK National Health Service. Int J Qual Health Care. 2023;35(4):mzad072. Franklin BJ, Mueller SK, Bates DW, Gandhi TK, Morris CA, Goralnick E. Use of hospital capacity command centers to improve patient flow and safety: a scoping review. J Patient Saf. 2022;18(6):e912-21. Kwon H, An S, Lee HY, Cha WC, Kim S, Cho M, et al. Review of smart hospital services in real healthcare environments. Healthc Inform Res. 2022;28(1):3-15. Overmann KM, Wu DT, Xu CT, Bindhu SS, Barrick L. Real-time locating systems to improve healthcare delivery: a systematic review. J Am Med Inform Assoc. 2021;28(6):1308-17. Mesko S, Weng J, Das P, Koong AC, Herman JM, Elrod-Joplin D, et al. Using patient flow analysis with real-time patient tracking to optimize radiation oncology consultation visits. BMC Health Serv Res. 2022;22(1):1517. Conley K, Chambers C, Elnahal S, Choflet A, Williams K, DeWeese T, et al. Using a real-time location system to measure patient flow in a radiation oncology outpatient clinic. Pract Radiat Oncol. 2018;8(5):317-23. Boussina A, Shashikumar S, Amrollahi F, Pour H, Hogarth M, Nemati S. Development and deployment of a real-time healthcare predictive analytics platform. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). New York: IEEE; 2023. p. 1-4. De Roock E, Martin N. Process mining in healthcare: an updated perspective on the state of the art. J Biomed Inform. 2022;127:103995. Stone K, Zwiggelaar R, Jones P, Mac Parthaláin N. A systematic review of the prediction of hospital length of stay: towards a unified framework. PLOS Digit Health. 2022;1(4):e0000017. He L, Madathil SC, Oberoi A, Servis G, Khasawneh MT. A systematic review of research design and modeling techniques in inpatient bed management. Comput Ind Eng. 2019;127:451-66. Franklin BJ, Yenduri R, Parekh VI, Fogerty RL, Scheulen JJ, High H, et al. Hospital capacity command centers: a benchmarking survey on an emerging mechanism to manage patient flow. Jt Comm J Qual Patient Saf. 2023;49(4):189-98. Davenport PB, Carter KF, Echternach JM, Tuck CR. Integrating high-reliability principles to transform access and throughput by creating a centralized operations center. J Nurs Adm. 2018;48(2):93-9. Grosman-Rimon L, Li DH, Collins BE, Wegier P. Can we improve healthcare with centralized management systems, supported by information technology, predictive analytics, and real-time data? A review. Medicine (Baltimore). 2023;102(45):e35769. Gholamhosseini L, Sadoughi F, Safaei A. Hospital real-time location system: a practical approach in healthcare. Iran J Public Health. 2019;48(4):593-602. Duma D, Aringhieri R. An ad hoc process mining approach to discover patient paths of an emergency department. Flex Serv Manuf J. 2020;32(1):6-34. Alowad A, Samaranayake P, Ahsan K, Alidrisi H, Karim A. Enhancing patient flow in emergency department using lean strategies: an integrated voice of customer and voice of process perspective. Bus Process Manag J. 2021;27(1):75-105. Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS One. 2018;13(7):e0201016. 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;23(1):64. Amrami K, Domnick R, Heinzen E, Helfinstine K, Jayakumar A, Johnson P, et al. Deployment of an interdisciplinary predictive analytics task force to inform hospital operational decision-making during the COVID-19 pandemic. Mayo Clin Proc. 2021;96(3):690-8. Hertelendy AJ, Goniewicz K, Khorram-Manesh A. The COVID-19 pandemic: how predictive analysis, artificial intelligence and GIS can be integrated into a clinical command system to improve disaster response and preparedness. Am J Emerg Med. 2021;45:671-2. Van Calster B, Wynants L, Timmerman D, Steyerberg EW, Collins GS. Predictive analytics in health care: how can we know it works? J Am Med Inform Assoc. 2019;26(12):1651-4. Ludlow K, Westbrook J, Jorgensen M, Lind KE, Baysari MT, Gray LC, et al. Co-designing a dashboard of predictive analytics and decision support to drive care quality and client outcomes in aged care: a mixed-method study protocol. BMJ Open. 2021;11(8):e048657. Schäfer F, Walther M, Grimm DG, Hübner A. Combining machine learning and optimization for the operational patient-bed assignment problem. Health Care Manag Sci. 2023;26(4):785-99. Araz OM, Olson D, Ramirez-Nafarrate A. Predictive analytics for hospital admissions from the emergency department using triage information. Int J Prod Econ. 2019;208:199-207. Levin S, Toerper M, Hamrock E, Hinson JS, Barnes S, Gardner H, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Ann Emerg Med. 2018;71(5):565-74. Zeleke AJ, Palumbo P, Tubertini P, Miglio R, Chiari L. Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a gradient boosting algorithm analysis. Front Artif Intell. 2023;6:1179226. Johnson MR, Naik H, Chan WS, Greiner J, Michaleski M, Liu D, et al. Forecasting ward-level bed requirements to aid pandemic resource planning: lessons learned and future directions. Health Care Manag Sci. 2023;26(3):477-500. Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Al-Thwayee A, Al-Mallah MH. Predictors of in-hospital length of stay among cardiac patients: a machine learning approach. Int J Cardiol. 2019;288:140-7. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1(1):18.