<?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-IqmG-1790880677-j466339512</doi_batch_id>
		<timestamp>1790880677</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>Federated Analytics Framework for Benchmarking Hospital Operational Performance Across Health Systems Using Secure Aggregation of Bed Occupancy, Discharge Delays, Staffing Ratios, and Service Demand Indicators</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>George</given_name>
            <surname>Brown</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Michael</given_name>
            <surname>Taylor</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Sarah</given_name>
            <surname>Wilson</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Olivia</given_name>
            <surname>Harris</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2025</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/j466339512</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/j466339512</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/j466339512-06386962-26d9-477a-8269-42a632ec9370">
					  <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/j466339512-b3f618ae-0c95-47c2-b261-e5a2b8741513">
					  <unstructured_citation>Hu Y, Dong J, Perry O, Cyrus RM, Gravenor S, Schmidt MJ. Use of a novel patient-flow model to optimize hospital bed capacity for medical patients. Jt Comm J Qual Patient Saf. 2021;47(6):354-63.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-62229d53-83c9-477c-abe4-d05b0d9d587c">
					  <unstructured_citation>van den Ende E, Schouten B, Pladet L, Merten H, van Galen L, Marinova M, et al. Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands. Int J Qual Health Care. 2023;35(2):mzad022.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-32f97809-14c9-4550-8cd0-c0a0d807ab17">
					  <unstructured_citation>Friebel R, Juarez RM. Spill over effects of inpatient bed capacity on accident and emergency performance in England. Health Policy. 2020;124(11):1182-91.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-908aa35d-80d6-4e78-9a92-4bc526a482f7">
					  <unstructured_citation>Griffiths P, Maruotti A, Recio Saucedo A, Redfern OC, Ball JE, Briggs J, et al. Nurse staffing, nursing assistants and hospital mortality: retrospective longitudinal cohort study. BMJ Qual Saf. 2019;28(8):609-17.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-805ce07f-33a2-4fce-8829-c20e64b0a944">
					  <unstructured_citation>Cadel L, Guilcher SJT, Kokorelias KM, Sutherland J, Glasby J, Kiran T, et al. Initiatives for improving delayed discharge from a hospital setting: a scoping review. BMJ Open. 2021;11(2):e044291.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-b7602b08-fa8f-478a-98c6-03637d860521">
					  <unstructured_citation>Vinci A, Furia G, Cammalleri V, Colamesta V, Chierchini P, Corrado O, et al. Burden of delayed discharge on acute hospital medical wards: a retrospective ecological study in Rome, Italy. PLoS One. 2024;19(1):e0294785.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-afe4861c-34d1-4bf6-95ee-dfd5be688644">
					  <unstructured_citation>Zaranko B, Sanford NJ, Kelly E, Rafferty AM, Bird J, Mercuri L, et al. Nurse staffing and inpatient mortality in the English National Health Service: a retrospective longitudinal study. BMJ Qual Saf. 2023;32(5):254-63.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-fa59847f-8fd5-428b-b493-fb54d050d8e0">
					  <unstructured_citation>McHugh MD, Aiken LH, Sloane DM, Windsor C, Douglas C, Yates P. Effects of nurse-to-patient ratio legislation on nurse staffing and patient mortality, readmissions, and length of stay: a prospective study in a panel of hospitals. Lancet. 2021;397(10288):1905-13.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-4079329b-2058-4b47-ab73-bca230a35a90">
					  <unstructured_citation>Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):119.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-3eb636be-214c-4d5b-92ec-df98f7302646">
					  <unstructured_citation>Guo P, Wang P, Zhou J, Jiang S, Patel VM. Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning. In: Proc IEEE/CVF Conf Comput Vis Pattern Recognit; 2021. p. 2423-32.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-e09f9ee5-cba6-4844-87dd-29819e9f1fe5">
					  <unstructured_citation>Kaissis GA, Makowski MR, Rückert D, Braren RF. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell. 2020;2(6):305-11.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-e60e132a-ab4d-4035-895b-a66d2fa03e37">
					  <unstructured_citation>Xu J, Glicksberg BS, Su C, Walker P, Bian J, Wang F. Federated learning for healthcare informatics. J Healthc Inform Res. 2021;5(1):1-19.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-c4bf3919-1cef-4f1e-9016-4a430c7e5a6f">
					  <unstructured_citation>Pati S, Kumar S, Varma A, Edwards B, Lu C, Qu L, et al. Privacy preservation for federated learning in health care. Patterns. 2024;5(7):100987.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-11b1fb2d-d57d-447e-8844-d141474d4d8a">
					  <unstructured_citation>Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: concept and applications. ACM Trans Intell Syst Technol. 2019;10(2):1-19.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-9a7a8fc2-6706-48db-8d32-dda2f3cb60ba">
					  <unstructured_citation>Lasater KB, Sloane DM, McHugh MD, Cimiotti JP, Riman KA, Martin B, et al. Evaluation of hospital nurse-to-patient staffing ratios and sepsis bundles on patient outcomes. Am J Infect Control. 2021;49(7):868-73.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-448915f2-6422-4f97-9faa-50d1e1710652">
					  <unstructured_citation>Chen Y, Qin X, Wang J, Yu C, Gao W. FedHealth: a federated transfer learning framework for wearable healthcare. IEEE Intell Syst. 2020;35(4):83-93.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-953ecce3-cb28-471f-aa47-3e8f0496f21c">
					  <unstructured_citation>Dou Q, So TY, Jiang M, Liu Q, Vardhanabhuti V, Kaissis G, et al. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ Digit Med. 2021;4(1):60.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-2a4b80ef-521f-4895-817a-4c4d4e70ddea">
					  <unstructured_citation>Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, et al. Practical secure aggregation for privacy-preserving machine learning. In: Proc ACM SIGSAC Conf Comput Commun Secur; 2017. p. 1175-91.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-232120c8-88a3-4aba-816e-22568201811d">
					  <unstructured_citation>Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R, et al. A hybrid approach to privacy-preserving federated learning. In: Proc ACM Workshop Artif Intell Secur; 2019. p. 1-11.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-197e59b6-5093-4919-92dd-f200f11c0723">
					  <unstructured_citation>Kaissis G, Ziller A, Passerat-Palmbach J, Ryffel T, Usynin D, Trask A, et al. End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat Mach Intell. 2021;3(6):473-84.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-8d712e62-2f33-428f-a1b9-3c25ba836d1c">
					  <unstructured_citation>Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, et al. Swarm learning for decentralized and confidential clinical machine learning. Nature. 2021;594(7862):265-70.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-1f6b9438-ad4e-4539-a1c9-c290113865e0">
					  <unstructured_citation>Choudhury A, Volmer L, Martin F, Fijten R, Wee L, Dekker A, et al. Advancing privacy-preserving health care analytics and implementation of the personal health train: federated deep learning study. JMIR AI. 2025;4(1):e60847.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-e83ac415-3ecc-410e-88a0-5cd828542f8c">
					  <unstructured_citation>Ryffel T, Créquit P, Baillet M, Paumier J, Marfoq Y, Girardot O, et al. Federated analysis with differential privacy in oncology research: longitudinal observational study across hospital data warehouses. JMIR Med Inform. 2025;13(1):e59685.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-30d672c8-da6b-4c16-adff-77e0a56036aa">
					  <unstructured_citation>Fang C, Dziedzic A, Zhang L, Oliva L, Verma A, Razak F, et al. Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data. EBioMedicine. 2024;101:104995.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-75e6e07f-f839-48d8-8f6b-0ec21151032b">
					  <unstructured_citation>Lasater KB, Muir KJ, Sloane DM, McHugh MD, Aiken LH. Alternative models of nurse staffing may be dangerous in high-stakes hospital care. Med Care. 2024;62(7):434-40.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-cd05e798-6aa9-471f-a48d-8eba6c9491aa">
					  <unstructured_citation>Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag. 2020;37(3):50-60.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-94863932-6797-48f9-8bf8-b6ac007f3c8c">
					  <unstructured_citation>Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: a survey. Pattern Recognit. 2024;151:110424.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j466339512-2857e5fe-e26e-4df2-aafe-4ec55cfead97">
					  <unstructured_citation>Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021;27(10):1735-43.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
