<?xml version="1.0" encoding="UTF-8"?><doi_batch version="4.3.7" xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd">
		<head>
		<doi_batch_id>cirpublications.com-SPeK-1790883432-d790098110</doi_batch_id>
		<timestamp>1790883432</timestamp>
		<depositor>
			<depositor_name>Clinical Intelligence Research Press</depositor_name>
			<email_address>info@cirpublications.com</email_address>
		</depositor>
		<registrant>Clinical Intelligence Research Press</registrant>
	</head>
	<body>
		<journal>
			<journal_metadata>
				<full_title>Journal of Health Informatics and Digital Systems</full_title>
				<abbrev_title>J. Health Inform. Digit. Syst.</abbrev_title>
				<issn>3149-8973</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2025</year>
				</publication_date>
				<journal_volume>
					<volume>5</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Machine Learning Model for Forecasting Hospital Housekeeping Demand Using Discharge Predictions, Room Turnover History, Isolation Status, Environmental Cleaning Requirements, and Unit-Level Census Patterns</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Samuel</given_name>
            <surname>Boateng</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Kwesi</given_name>
            <surname>Mensah</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Kojo</given_name>
            <surname>Asante</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Linda</given_name>
            <surname>Owusu</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/d790098110</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/d790098110</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/d790098110-7a951535-56fa-4752-8498-8cce253b1adf">
					  <unstructured_citation>Acosta-Perez F, Boutilier J, Zayas-Caban G, Adelaine S, Liao F, Patterson B. Toward real-time discharge volume predictions in multisite health care systems: longitudinal observational study. J Med Internet Res. 2025;27:e63765.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-3a5000ac-3479-4583-8f5c-c263c85e554c">
					  <unstructured_citation>Bertsimas D, Pauphilet J, Stevens J, Tandon M. Predicting inpatient flow at a major hospital using interpretable analytics. Manuf Serv Oper Manag. 2022;24(6):2809-24.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-65116c1e-df12-4f9f-bfd7-8535886f378f">
					  <unstructured_citation>Wei J, Zhou J, Zhang Z, Yuan K, Gu Q, Luk A, et al. Predicting individual patient and hospital-level discharge using machine learning. Commun Med (Lond). 2024;4(1):236.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-1d8470e5-846f-4a0c-9b3c-9f2480465589">
					  <unstructured_citation>Kirubarajan A, Shin S, Fralick M, Kwan J, Lapointe-Shaw L, Liu J, et al. Morning discharges and patient length of stay in inpatient general internal medicine. J Hosp Med. 2021;16(6):333-8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-084e7e64-5253-464d-a3dd-94d5a16d2af0">
					  <unstructured_citation>El-Bouri R, Taylor T, Youssef A, Zhu T, Clifton DA. Machine learning in patient flow: a review. Prog Biomed Eng. 2021;3(2):022002.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-9f600e39-a345-4ddf-a074-485c1cb65b1a">
					  <unstructured_citation>Pianykh OS, Guitron S, Parke D, Zhang C, Pandharipande P, Brink J, et al. Improving healthcare operations management with machine learning. Nat Mach Intell. 2020;2(5):266-73.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-9c729ae8-a78a-4954-b38f-42e6738e4dc3">
					  <unstructured_citation>Van Walraven C, Forster AJ. The TEND (Tomorrow&#039;s Expected Number of Discharges) model accurately predicted the number of patients who were discharged from the hospital the next day. J Hosp Med. 2018;13(3):158-63.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-6c3cae27-3dcb-4d00-bfce-8b38b2630989">
					  <unstructured_citation>Ward A, Mann A, Vallon J, Escobar G, Bambos N, Schuler A. Operationally-informed hospital-wide discharge prediction using machine learning. In: Proc IEEE Int Conf E-health Netw Appl Serv (HEALTHCOM); 2020. p. 1-6.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-2d49e2da-3434-4a19-9820-34d3f50d3cb2">
					  <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>
											</citation>
          					<citation key="rk-10.68159/d790098110-41c7c74a-87f7-4482-8d87-b437ca708a0a">
					  <unstructured_citation>Levin S, Barnes S, Toerper M, Debraine A, DeAngelo A, Hamrock E, et al. Machine-learning-based hospital discharge predictions can support multidisciplinary rounds and decrease hospital length of stay. BMJ Innov. 2021;7(2):293-9.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-6c57daa4-3b99-4636-9319-074e42ffdca0">
					  <unstructured_citation>Safavi KC, Khaniyev T, Copenhaver M, Seelen M, Zenteno Langle AC, Zanger J, et al. Development and validation of a machine learning model to aid discharge processes for inpatient surgical care. JAMA Netw Open. 2019;2(12):e1917221.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-fc9a7cf2-75ec-4291-9e80-65e1ca1e98d2">
					  <unstructured_citation>Nguyen M, Corbin CK, Eulalio T, Ostberg NP, Machiraju G, Marafino BJ, et al. Developing machine learning models to personalize care levels among emergency room patients for hospital admission. J Am Med Inform Assoc. 2021;28(11):2423-32.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-22cd5ebd-c79e-4da6-b855-b050fc737ad6">
					  <unstructured_citation>Ankrum AL, Neogi S, Morckel MA, Wilhite AW, Li Z, Schaffzin JK. Reduced isolation room turnover time using Lean methodology. Infect Control Hosp Epidemiol. 2019;40(10):1151-6.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-67446f19-c674-44cc-ac88-90728de6e5fb">
					  <unstructured_citation>Scott D, Kane H, Rankin A. Time to clean: a systematic review and observational study on the time required to clean items of reusable communal patient care equipment. J Infect Prev. 2017;18(6):289-94.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-210fdf71-65be-4203-9f1c-0e2daac39188">
					  <unstructured_citation>Matterson G, Browne K, Tehan PE, Russo PL, Kiernan M, Mitchell BG. Cleaning time and motion: an observational study on the time required to clean shared medical equipment in hospitals effectively. J Hosp Infect. 2024;152:138-41.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-ef5a88f9-63eb-42a9-a211-ffa37babca7b">
					  <unstructured_citation>Anderson DJ, Chen LF, Weber DJ, Moehring RW, Lewis SS, Triplett PF, et al. Enhanced terminal room disinfection and acquisition and infection caused by multidrug-resistant organisms and Clostridium difficile (the Benefits of Enhanced Terminal Room Disinfection study): a cluster-randomised, multicentre, crossover study. Lancet. 2017;389(10071):805-14.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-f245bcd0-ae65-42bc-a650-8ee329f1de2c">
					  <unstructured_citation>Anderson DJ, Moehring RW, Weber DJ, Lewis SS, Chen LF, Schwab JC, et al. Effectiveness of targeted enhanced terminal room disinfection on hospital-wide acquisition and infection with multidrug-resistant organisms and Clostridium difficile: a secondary analysis of a multicentre cluster randomised controlled trial with crossover design (BETR Disinfection). Lancet Infect Dis. 2018;18(8):845-53.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-0b745404-3baf-4112-8ab6-06929bda91d5">
					  <unstructured_citation>Assadian O, Harbarth S, Vos M, Knobloch JK, Asensio A, Widmer AF. Practical recommendations for routine cleaning and disinfection procedures in healthcare institutions: a narrative review. J Hosp Infect. 2021;113:104-14.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-bf3d0741-dd9f-4e82-8b95-0a5cb9643af6">
					  <unstructured_citation>McCoy TH Jr, Pellegrini AM, Perlis RH. Assessment of time-series machine learning methods for forecasting hospital discharge volume. JAMA Netw Open. 2018;1(7):e184087.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-aac3b788-f02d-4698-badd-460b9f35ce21">
					  <unstructured_citation>Ahn I, Gwon H, Kang H, Kim Y, Seo H, Choi H, et al. Machine learning-based hospital discharge prediction for patients with cardiovascular diseases: development and usability study. JMIR Med Inform. 2021;9(11):e32662.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-ae3e2db5-7322-41c0-94ff-cf57903ad8de">
					  <unstructured_citation>Duckworth C, Burns D, Fernandez CL, Wright M, Leyland R, Stammers M, et al. Predicting onward care needs at admission to reduce discharge delay using explainable machine learning. Sci Rep. 2025;15(1):16033.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-f4949d0a-b3d4-4cab-8b72-2d7a723fe3e7">
					  <unstructured_citation>Goldhaber NH, Schaefer RL, Martinez R, Graham A, Malachowski E, Rhodes LP, et al. Surgical pit crew: initiative to optimise measurement and accountability for operating room turnover time. BMJ Health Care Inform. 2023;30(1):e100741.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-1e202336-d853-481f-a3e8-409627bc2e55">
					  <unstructured_citation>He L, Madathil SC, Servis G, Khasawneh MT. Neural network-based multi-task learning for inpatient flow classification and length of stay prediction. Appl Soft Comput. 2021;108:107483.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-6bb960b0-bf49-4547-bf25-159be687950e">
					  <unstructured_citation>Bishop JA, Javed HA, El-Bouri R, Zhu T, Taylor T, Peto T, et al. Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge. PLoS One. 2021;16(11):e0260476.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-5e490d19-0601-44e0-b5be-988b451d7dcb">
					  <unstructured_citation>Zaribafzadeh H, Howell TC, Webster WL, Vail CJ, Kirk AD, Allen PJ, et al. Development of multiservice machine learning models to predict postsurgical length of stay and discharge disposition at the time of case posting. Ann Surg Open. 2025;6(1):e547.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-7f9170a6-2e2b-4482-b27d-54007b976483">
					  <unstructured_citation>Lazar DJ, Kia A, Freeman R, Divino CM. A machine learning model enhances prediction of discharge for surgical patients. J Am Coll Surg. 2020;231(4 Suppl):S132.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-baef3771-7981-47ed-99cc-3a991ea7646a">
					  <unstructured_citation>Taylor KP, Harris D. Cleaning and disinfecting protocols for hospital environmental surfaces: a systematic review of the literature. J Hosp Adm. 2019;8(6):27-40.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-40088a3e-b803-4c76-ae9b-446270493836">
					  <unstructured_citation>Coppin JD, Villamaria FC, Williams MD, Copeland LA, Zeber JE, Jinadatha C. Increased time spent on terminal cleaning of patient rooms may not improve disinfection of high-touch surfaces. Infect Control Hosp Epidemiol. 2019;40(5):605-6.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/d790098110-5ae197b9-f6b7-4088-81b2-1e4f7940fb01">
					  <unstructured_citation>Zhang C, Zhang D, Tang L, Tian F. Research on a machine learning method for predicting discharge time of thyroid cancer patients receiving 131I treatment: a retrospective study. Front Physiol. 2025;16:1599657.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
