<?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-j5HA-1791323897-u109668323</doi_batch_id>
		<timestamp>1791323897</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>2022</year>
				</publication_date>
				<journal_volume>
					<volume>2</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Constraint-Aware Hospital Staffing Forecasting: A Resilience-Oriented Modeling Framework for Workforce Stability</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Ahmed</given_name>
            <surname>Mansour</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Omar</given_name>
            <surname>Saeed</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2022</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/u109668323</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/u109668323</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/u109668323-1d00d4eb-8d5f-46a7-88b2-015faa7c00ca">
					  <unstructured_citation>Gopal G, Suter-Crazzolara C, Toldo L, Eberhardt W. Digital transformation in healthcare - architectures of present and future information technologies. Clin Transl Sci. 2019;12(2):104-12.</unstructured_citation>
						 <doi>10.1111/cts.12592</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-b00979e2-95ca-441c-b5f8-eead96b0679d">
					  <unstructured_citation>Suryanarayanan P, Tsou CH, Poddar A, Mahajan D, Sounderajah V, Ashrafian H, et al. Timely and efficient AI insights on EHR: system design. JMIR Form Res. 2021;5(6):e25977.</unstructured_citation>
						 <doi>10.2196/25977</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-1bcefc7f-801b-4f59-96eb-6e5cedca5895">
					  <unstructured_citation>Kashyap S, Karthik K, Goyal M, Kar A, Sharma A, Jabbour C, et al. A survey of extant organizational and computational setups for deploying predictive models in health systems. J Med Internet Res. 2021;23(10):e27431.</unstructured_citation>
						 <doi>10.2196/27431</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-b6d2cf5b-bfa3-47c4-902e-fae1bb1de240">
					  <unstructured_citation>Popovic JR. Distributed data networks: a blueprint for big data sharing and healthcare analytics. Ann N Y Acad Sci. 2017;1387(1):105-11.</unstructured_citation>
						 <doi>10.1111/nyas.13287</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-9107e776-2067-47ac-9559-de883db431c6">
					  <unstructured_citation>Ozaydin B, Zengul F, Oner N, Delen D. Healthcare research and analytics data infrastructure solution: a data warehouse for health services research. J Med Internet Res. 2020;22(6):e18579.</unstructured_citation>
						 <doi>10.2196/18579</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-e90202f4-777f-4839-9fdc-b82fa6cf76f9">
					  <unstructured_citation>Walker DM, Yeager VA, Lawrence J, McAlearney AS. Identifying opportunities to strengthen the public health informatics infrastructure: exploring hospitals’ challenges with data exchange. Milbank Q. 2021;99(2):393-425.</unstructured_citation>
						 <doi>10.1111/1468-0009.12511</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-dd91fc29-9450-4802-962f-21fc2ae90a23">
					  <unstructured_citation>Haghighi M, Baumgartner P, Coleman K, Loeb D, Knight K, Beck A. Creating surveillance data infrastructure using laboratory analytics: leveraging Visiun and Epic systems to support COVID-19 pandemic response. J Pathol Inform. 2022;13:100001.</unstructured_citation>
						 <doi>10.1016/j.jpi.2021.100001</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-eb5bcbcd-40ba-4f66-90a1-3cd14d9b0887">
					  <unstructured_citation>Marteau BL, Segovia A, Hernon JB, Haimovich JS, Schulz WL, Venkatesh AK. Accelerating multi-site health informatics with streamlined data infrastructure using OMOP-on-FHIR. Appl Clin Inform. 2022;13(3):651-61.</unstructured_citation>
						 <doi>10.1055/s-0042-1750344</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-9d1750f4-a557-4436-8f2e-045bffba44f0">
					  <unstructured_citation>Cutillo CM, Sharma KR, Foschini L, Kundu S, Mack M, Carton T. Machine intelligence in healthcare - perspectives on trustworthiness, explainability, usability, and transparency. NPJ Digit Med. 2020;3:47.</unstructured_citation>
						 <doi>10.1038/s41746-020-0254-9</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-00f393aa-bf9e-4839-93f4-ca699a0cae32">
					  <unstructured_citation>Gavrilov G, Vlahu-Gjorgievska E, Trajkovik V. Healthcare data warehouse system supporting cross-border interoperability. Health Inform J. 2020;26(2):1321-32.</unstructured_citation>
						 <doi>10.1177/1460458219876793</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-7c8a6b91-d788-4333-8ff3-5bd42230cc71">
					  <unstructured_citation>Goel AK, Campbell WS, Moldwin R. Structured data capture for oncology. JCO Clin Cancer Inform. 2021;5:194-201.</unstructured_citation>
						 <doi>10.1200/CCI.20.00103</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-53a94140-ca30-4f23-b746-da4238cc2f34">
					  <unstructured_citation>Hasselgren A, Kralevska K, Gligoroski D, Pedersen SA, Faxvaag A. Blockchain in healthcare and health sciences - a scoping review. Int J Med Inform. 2020;134:104040.</unstructured_citation>
						 <doi>10.1016/j.ijmedinf.2019.104040</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-58889a63-38c9-4a45-b9ff-1bd6dd642606">
					  <unstructured_citation>Hylock RH, Zeng X. A blockchain framework for patient-centered health records and exchange (HealthChain): evaluation and proof-of-concept study. J Med Internet Res. 2019;21(8):e13592.</unstructured_citation>
						 <doi>10.2196/13592</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-d396a28b-ffd5-4eea-9dd1-8bc4cd2c6624">
					  <unstructured_citation>Han A, Isaacson A, Muennig P. The promise of big data for precision population health management in the US. Public Health. 2020;185:110-6.</unstructured_citation>
						 <doi>10.1016/j.puhe.2020.04.040</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-40b0477b-e99a-4383-bd22-1a9c340c5cad">
					  <unstructured_citation>Vyas S, Gupta S, Bhargava D, Boddu R. Fuzzy logic system implementation on the performance parameters of health data management frameworks. J Healthc Eng. 2022;2022:9382322.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/u109668323-9377a307-b9a0-4f71-bbf0-cdcbbc4662e5">
					  <unstructured_citation>Fadrique LX, Rahman D, Vaillancourt H, Boissonneault P, Donovska T, Morita PP. Overview of policies, guidelines, and standards for active assisted living data exchange: thematic analysis. JMIR Mhealth Uhealth. 2020;8(6):e15923.</unstructured_citation>
						 <doi>10.2196/15923</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-a8aebb36-de04-4cc0-8df7-9354f328985a">
					  <unstructured_citation>Davis BD, Swenson A. How Carequality, The Sequoia Project, and eHealth Exchange support the interoperable exchange of health data in the USA. J Digit Imaging. 2022;35(4):812-6.</unstructured_citation>
						 <doi>10.1007/s10278-021-00538-y</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-6c9deaee-4250-44db-80eb-2b77c21789b9">
					  <unstructured_citation>Yang Z, Jiang K, Lou M, Gong Y, Zhang L, Liu J, et al. Defining health data elements under the HL7 development framework for metadata management. J Biomed Semantics. 2022;13(1):10.</unstructured_citation>
						 <doi>10.1186/s13326-022-00265-5</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-6219d7c5-82af-49c5-969f-7eac871808f8">
					  <unstructured_citation>Nalin M, Baroni I, Faiella G, Romano M, Matrisciano F, Gelenbe E, et al. The European cross-border health data exchange roadmap: case study in the Italian setting. J Biomed Inform. 2019;94:103183.</unstructured_citation>
						 <doi>10.1016/j.jbi.2019.103183</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-b35d6e5c-b55d-40d6-91f8-4ed6933412cd">
					  <unstructured_citation>Rowe JP, Lester JC. Artificial intelligence for personalized preventive adolescent healthcare. J Adolesc Health. 2020;67(2 Suppl):S52-S58.</unstructured_citation>
						 <doi>10.1016/j.jadohealth.2020.02.021</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-ae9fed6d-140a-487d-add2-a6358f93f2b3">
					  <unstructured_citation>Kasparick M, Andersen B, Franke S, Rockstroh M, Golatowski F, Timmermann D, et al. Enabling artificial intelligence in high acuity medical environments. Minim Invasive Ther Allied Technol. 2019;28(2):120-6.</unstructured_citation>
						 <doi>10.1080/13645706.2019.1599957</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-f3735ce9-f019-4e0a-961a-06611985819b">
					  <unstructured_citation>Thomasian NM, Kamel IR, Bai HX. Machine intelligence in non-invasive endocrine cancer diagnostics. Nat Rev Endocrinol. 2022;18(2):81-95.</unstructured_citation>
						 <doi>10.1038/s41574-021-00543-9</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-202c1057-5156-443b-993b-8f9197af2e89">
					  <unstructured_citation>Dreyer KJ, Geis JR. When machines think: radiology’s next frontier. Radiology. 2017;285(3):713-8.</unstructured_citation>
						 <doi>10.1148/radiol.2017171183</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-21b884f5-cc6c-4282-944b-ba207511f807">
					  <unstructured_citation>Reddy S, Rogers W, Makinen VP, Coiera E, Brown P, Wenzel M, et al. Evaluation framework to guide implementation of AI systems into healthcare settings. BMJ Health Care Inform. 2021;28(1):e100444.</unstructured_citation>
						 <doi>10.1136/bmjhci-2021-100444</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-728b5e27-8309-4372-a4b7-1581bf84e9cb">
					  <unstructured_citation>Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol. 2021;27(21):2818-33.</unstructured_citation>
						 <doi>10.3748/wjg.v27.i21.2818</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-1f5167ef-97a9-485c-aa17-deaf4118a62c">
					  <unstructured_citation>Cheng JY, Abel JT, Balis UGJ, McClintock DS, Pantanowitz L. Challenges in the development, deployment, and regulation of artificial intelligence in anatomic pathology. Am J Pathol. 2021;191(10):1684-92.</unstructured_citation>
						 <doi>10.1016/j.ajpath.2020.10.018</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-0551fb78-ff5f-47cc-86b3-46b48e50e7d4">
					  <unstructured_citation>Mun SK, Wong KH, Lo SB, Li Y, Bayarsaikhan S. Artificial intelligence for the future radiology diagnostic service. Front Mol Biosci. 2021;7:614258.</unstructured_citation>
						 <doi>10.3389/fmolb.2020.614258</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-8153dea7-b41c-4e1b-8a19-a62c02489d9e">
					  <unstructured_citation>Cè M, Caloro E, Pellegrino ME, Basile M, Sorce A, Fazzini D, et al. Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis - a narrative review. Explor Target Antitumor Ther. 2022;3(6):795-816.</unstructured_citation>
						 <doi>10.37349/etat.2022.00113</doi> 					</citation>
          					<citation key="rk-10.68159/u109668323-e0f8ecbb-d299-4277-82be-e60c46fd6ea2">
					  <unstructured_citation>Ta AWA, Goh HL, Ang C, Koh LY, Poon K, Miller SM. Two Singapore public healthcare AI applications for national screening programs and other examples. Health Care Sci. 2022;1(2):41-57.</unstructured_citation>
						 <doi>10.1002/hcs2.10</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
