<?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-COWd-1790883433-b145007545</doi_batch_id>
		<timestamp>1790883433</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>2024</year>
				</publication_date>
				<journal_volume>
					<volume>4</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Artificial Intelligence Framework for Predicting Medical Equipment Utilization Using Real-Time Location System Data, Procedure Schedules, Maintenance Logs, Unit-Level Demand, and Device Availability Records</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Victor</given_name>
            <surname>Santos</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Rafael</given_name>
            <surname>Costa</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Bruno</given_name>
            <surname>Teixeira</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/b145007545</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/b145007545</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/b145007545-f46dc48e-b948-487f-b2dd-03ef552c31a4">
					  <unstructured_citation>Yoo S, Kim S, Kim E, Jung E, Lee KH, Hwang H. Real-time location system-based asset tracking in the healthcare field: lessons learned from a feasibility study. BMC Med Inform Decis Mak. 2018;18(1):80.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-6192dd19-c300-4dbc-b4e2-fc6d0332a1ea">
					  <unstructured_citation>Li RC, Marafino BJ, Nielsen D, Baiocchi M, Shieh L. Assessment of a real-time locator system to identify physician and nurse work locations. JAMA Netw Open. 2020;3(2):e1920352.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-eab7a7cf-b103-4e99-9639-79e271228828">
					  <unstructured_citation>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.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-ead3d441-b2b5-439f-af8e-174dfe5bae0d">
					  <unstructured_citation>Bartek MA, Saxena RC, Solomon S, Fong CT, Behara LD, Venigandla R, et al. Improving operating room efficiency: machine learning approach to predict case-time duration. J Am Coll Surg. 2019;229(4):346-54.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-90c6febb-1492-4e8b-81f4-593d5739b1fb">
					  <unstructured_citation>Shamayleh A, Awad M, Farhat J. IoT-based predictive maintenance management of medical equipment. J Med Syst. 2020;44(4):72.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-da9a58a3-f978-44ee-ad52-f8d6a70c25b4">
					  <unstructured_citation>Tello M, Reich ES, Puckey J, Maff R, Garcia-Arce A, Bhattacharya BS, et al. Machine learning-based forecast for the prediction of inpatient bed demand. BMC Med Inform Decis Mak. 2022;22(1):55.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-0c4de40a-8d72-4055-9ae0-53fdbef345c3">
					  <unstructured_citation>Pagel C, Banks V, Pope C, Whitmore P, Brown K, Goldman A, et al. Development, implementation and evaluation of a tool for forecasting short-term demand for beds in an intensive care unit. Oper Res Health Care. 2017;15:19-31.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-ba4b61c5-dcee-4d18-b6a2-93e369d3c6f3">
					  <unstructured_citation>Lucini FR, Reis MA, Silveira GJ, Fogliatto FS, Anzanello MJ, Andrioli GG, et al. Man vs. machine: Predicting hospital bed demand from an emergency department. PLoS One. 2020;15(8):e0237937.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-42a59f68-1bae-4585-a598-a01f1abe40c8">
					  <unstructured_citation>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.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-1316df86-d594-4360-8d0f-22301091f6e6">
					  <unstructured_citation>Kendale S, Bishara A, Burns M, Solomon S, Corriere M, Mathis M. Machine learning for the prediction of procedural case durations developed using a large multicenter database: algorithm development and validation study. JMIR AI. 2023;2(1):e44909.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-3a0a28c0-1587-4fae-8873-92f60248cd73">
					  <unstructured_citation>Chu J, Hsieh CH, Shih YN, Wu CC, Singaravelan A, Hung LP, et al. Operating room usage time estimation with machine learning models. Healthcare (Basel). 2022;10(8):1518.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-7cbff351-e469-4e36-9540-6e868d600ee7">
					  <unstructured_citation>Abd Rahman NH, Zaki MH, Hasikin K, Abd Razak NA, Ibrahim AK, Lai KW. Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management. PeerJ Comput Sci. 2023;9:e1279.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-f4a0f08b-4097-4669-b369-4f235c5c40d1">
					  <unstructured_citation>Zamzam AH, Hasikin K, Wahab AK. Integrated failure analysis using machine learning predictive system for smart management of medical equipment maintenance. Eng Appl Artif Intell. 2023;125:106715.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-9f399c64-daec-407c-b15e-d1b7c8cb34a4">
					  <unstructured_citation>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.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-e93ac735-1a75-470e-b119-9e815590e6f0">
					  <unstructured_citation>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.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-e451a916-3663-4a37-abc0-55f23ba69a9a">
					  <unstructured_citation>Frisby J, Smith V, Traub S, Patel VL. Contextual computing: a Bluetooth-based approach for tracking healthcare providers in the emergency room. J Biomed Inform. 2017;65:97-104.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-7e2417e0-cdf7-4b01-b773-85ad2bb3dbf8">
					  <unstructured_citation>Miller LE, Goedicke W, Crowson MG, Rathi VK, Naunheim MR, Agarwala AV. Using machine learning to predict operating room case duration: a case study in otolaryngology. Otolaryngol Head Neck Surg. 2023;168(2):241-7.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-b1cdc8cd-1076-4f9a-972e-1cf973439d41">
					  <unstructured_citation>Zamzam AH, Al-Ani AKI, Wahab AKA, Lai KW, Satapathy SC, Khalil A, et al. Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management. Front Public Health. 2021;9:782203.</unstructured_citation>
						 <doi>10.3389/fpubh.2021.782203</doi> 					</citation>
          					<citation key="rk-10.68159/b145007545-e266d0eb-4fb9-44b4-86ee-fa7c6b8a255e">
					  <unstructured_citation>Schiele J, Koperna T, Brunner JO. Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks. Nav Res Logist. 2021;68(1):65-88.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-24030cc8-e0a8-490f-a381-a1c31d607fd2">
					  <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):785-806.</unstructured_citation>
						 <doi>10.1007/s10729-023-09652-5</doi> 					</citation>
          					<citation key="rk-10.68159/b145007545-00315166-400a-4b4d-bc05-22d4cd02b993">
					  <unstructured_citation>Patel B, Vilendrer S, Kling SM, Brown I, Ribeira R, Eisenberg M, et al. Using a real-time locating system to evaluate the impact of telemedicine in an emergency department during COVID-19: observational study. J Med Internet Res. 2021;23(7):e29240.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-b2be7913-bb52-4244-af63-1bf362ca406a">
					  <unstructured_citation>Ala A, Goli A. Incorporating machine learning and optimization techniques for assigning patients to operating rooms by considering fairness policies. Eng Appl Artif Intell. 2024;136:108980.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-d03775a5-8d9b-4e32-8151-7a3e7686ce2f">
					  <unstructured_citation>Guissi M, Alaoui MH, Belarbi L, Chaik A. IoT for predictive maintenance of critical medical equipment in a hospital structure. Inform Autom Pomiary Gospod Ochr Śr. 2024;14(2):71-6.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/b145007545-90f5ee7a-0823-402b-a385-bf7da7c5664d">
					  <unstructured_citation>Niyonambaza I, Zennaro M, Uwitonze A. Predictive maintenance (PdM) structure using Internet of Things (IoT) for mechanical equipment used in hospitals in Rwanda. Future Internet. 2020;12(12):224.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
