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			<depositor_name>Clinical Intelligence Research Press</depositor_name>
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				<full_title>Journal of Health Informatics and Digital Systems</full_title>
				<abbrev_title>J. Health Inform. Digit. Syst.</abbrev_title>
				<issn>3149-8973</issn>
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					<year>2023</year>
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					<volume>3</volume>
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				<issue>2</issue>
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					<title>Artificial Intelligence-Enabled Hospital Command Center for Predicting Patient Transfer Bottlenecks Using Admission Requests, Unit Occupancy, Bed Cleaning Duration, Isolation Requirements, and Staffing Constraints</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>James</given_name>
            <surname>Walker</surname>
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            <given_name>Olivia</given_name>
            <surname>Harris</surname>
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					<year>2023</year>
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					  <unstructured_citation>Lee SY, Chinnam RB, Dalkiran E, Krupp S, Nauss M. Prediction of emergency department patient disposition decision for proactive resource allocation for admission. Health Care Manag Sci. 2020;23(3):339-59.</unstructured_citation>
						 <doi>10.1007/s10729-019-09485-2</doi> 					</citation>
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					  <unstructured_citation>Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS One. 2018;13(7):e0201016.</unstructured_citation>
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          					<citation key="rk-10.68159/h134661506-246a9c3e-d62e-4e41-a55b-ead587accbb5">
					  <unstructured_citation>Barak-Corren Y, Israelit SH, Reis BY. Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow. Emerg Med J. 2017;34(5):308-14.</unstructured_citation>
						 <doi>10.1136/emermed-2016-205566</doi> 					</citation>
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					  <unstructured_citation>Long EF, Mathews KS. The boarding patient: effects of ICU and hospital occupancy surges on patient flow. Prod Oper Manag. 2018;27(12):2122-43.</unstructured_citation>
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					  <unstructured_citation>Friebel R, Fisher R, Deeny SR, Gardner T, Molloy A, Steventon A. The implications of high bed occupancy rates on readmission rates in England: a longitudinal study. Health Policy. 2019;123(8):765-72.</unstructured_citation>
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					  <unstructured_citation>Bosque-Mercader L, Siciliani L. The association between bed occupancy rates and hospital quality in the English National Health Service. Eur J Health Econ. 2023;24(2):209-36.</unstructured_citation>
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					  <unstructured_citation>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.</unstructured_citation>
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					  <unstructured_citation>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-e921.</unstructured_citation>
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					  <unstructured_citation>Franklin BJ, Yenduri R, Parekh VI, Fogerty RL, Scheulen JJ, Weiner JP, 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.</unstructured_citation>
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					  <unstructured_citation>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.</unstructured_citation>
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					  <unstructured_citation>King Z, Farrington J, Utley M, Kung E, Elkhodair S, Harris S, et al. Machine learning for real-time aggregated prediction of hospital admission for emergency patients. NPJ Digit Med. 2022;5(1):104.</unstructured_citation>
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          					<citation key="rk-10.68159/h134661506-1702e3c5-ce36-412b-a1e8-d0a557e2fa88">
					  <unstructured_citation>Parker CA, Liu N, Wu SX, Shen Y, Lam SSW, Ong MEH. Predicting hospital admission at the emergency department triage: a novel prediction model. Am J Emerg Med. 2019;37(8):1498-504.</unstructured_citation>
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					  <unstructured_citation>Patel D, Cheetirala SN, Raut G, Tamegue J, Kia A, Rai A, et al. Predicting adult hospital admission from emergency department using machine learning: an inclusive gradient boosting model. J Clin Med. 2022;11(23):6888.</unstructured_citation>
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					  <unstructured_citation>Sterling NW, Brann F, Patzer RE, Di M, Koebbe M, Watkins C, et al. Prediction of emergency department resource requirements during triage: an application of current natural language processing techniques. J Am Coll Emerg Physicians Open. 2020;1(6):1676-83.</unstructured_citation>
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					  <unstructured_citation>Baas S, Dijkstra S, Braaksma A, van Rooij P, Snijders FJ, Tiemessen N, et al. Real-time forecasting of COVID-19 bed occupancy in wards and intensive care units. Health Care Manag Sci. 2021;24(2):402-19.</unstructured_citation>
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					  <unstructured_citation>Heins J, Schoenfelder J, Heider S, Heller AR, Brunner JO. A scalable forecasting framework to predict COVID-19 hospital bed occupancy. INFORMS J Appl Anal. 2022;52(6):508-23.</unstructured_citation>
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					  <unstructured_citation>Bekker R, uit het Broek M, Koole G. Modeling COVID-19 hospital admissions and occupancy in the Netherlands. Eur J Oper Res. 2023;304(1):207-18.</unstructured_citation>
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					  <unstructured_citation>Deschepper M, Eeckloo K, Malfait S, Benoit D, Callens S, Vansteelandt S. Prediction of hospital bed capacity during the COVID-19 pandemic. BMC Health Serv Res. 2021;21(1):468.</unstructured_citation>
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					  <unstructured_citation>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):785-804.</unstructured_citation>
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					  <unstructured_citation>Braaksma A, Copenhaver MS, Zenteno AC, Ugarph E, Levi R, Wiler JL, et al. Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients. Health Care Manag Sci. 2023;26(3):501-15.</unstructured_citation>
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					  <unstructured_citation>Shenoy ES, Lee H, Ryan EE, Hou T, Walensky RP, Hooper DC, et al. A discrete event simulation model of patient flow in a general hospital incorporating infection control policy for Methicillin-Resistant Staphylococcus aureus (MRSA) and Vancomycin-Resistant Enterococcus (VRE). Med Decis Making. 2018;38(2):246-61.</unstructured_citation>
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					  <unstructured_citation>Chang AM, Cohen DJ, Lin A, Augustine J, Handel DA, Howell E, et al. Hospital strategies for reducing emergency department crowding: a mixed-methods study. Ann Emerg Med. 2018;71(4):497-505.</unstructured_citation>
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					  <unstructured_citation>Mahmoudian Y, Nemati A, Safaei AS. A forecasting approach for hospital bed capacity planning using machine learning and deep learning with application to public hospitals. Healthc Anal. 2023;4:100245.</unstructured_citation>
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					  <unstructured_citation>Teow KL, Tan KB, Phua HP, Zhu Z. Applying gravity model to predict demand of public hospital beds. Oper Res Health Care. 2018;17:65-70.</unstructured_citation>
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