<?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-SYsD-1790883434-k304401829</doi_batch_id>
		<timestamp>1790883434</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>2026</year>
				</publication_date>
				<journal_volume>
					<volume>6</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Foundation Models for Healthcare Systems Analytics from 2017 to 2026: A Review of Multimodal Pretraining for Patient Flow Prediction, Documentation Burden Estimation, Resource Allocation, and Operational Risk Forecasting</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Chen</given_name>
            <surname>Li</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Wang</given_name>
            <surname>Yu</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Zhang</given_name>
            <surname>Wei</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2026</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/k304401829</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/k304401829</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/k304401829-a37531bc-f9fb-40e9-a02b-4347e14c61a3">
					  <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/k304401829-c1a9c6f0-085e-4d2c-a5ff-c443b6119757">
					  <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>
											</citation>
          					<citation key="rk-10.68159/k304401829-ded13676-d501-43f1-ad6b-1fd77dee6272">
					  <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>
											</citation>
          					<citation key="rk-10.68159/k304401829-cd1c252c-c013-4377-9098-c8e4bf3751a6">
					  <unstructured_citation>Raita Y, Goto T, Faridi MK, Brown DF, Camargo CA Jr, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019;23(1):64.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-fe4adab9-7604-48a1-91b5-638a18065de5">
					  <unstructured_citation>Graham B, Bond R, Quinn M, Mulvenna M. Using data mining to predict hospital admissions from the emergency department. IEEE Access. 2018;6:10458-69.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-1cccd0e5-24ea-4703-8186-feca7a812864">
					  <unstructured_citation>Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1(1):18.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-4ea83def-5852-4ca0-a826-7b223a6cfb9d">
					  <unstructured_citation>Harutyunyan H, Khachatrian H, Kale DC, Ver Steeg G, Galstyan A. Multitask learning and benchmarking with clinical time series data. Sci Data. 2019;6(1):96.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-05a30d07-882e-4048-a0ad-e178f02f2e31">
					  <unstructured_citation>Li Y, Rao S, Solares JR, Hassaine A, Ramakrishnan R, Canoy D, et al. BEHRT: transformer for electronic health records. Sci Rep. 2020;10(1):7155.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-1ce0ec25-af3d-4420-bd30-e9815d3c9f59">
					  <unstructured_citation>Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med. 2021;4(1):86.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-52ad6f88-4d0e-4b2a-9b70-edacdc505d37">
					  <unstructured_citation>Jiang LY, Liu XC, Nejatian NP, Nasir-Moin M, Wang D, Abidin A, et al. Health system-scale language models are all-purpose prediction engines. Nature. 2023;619(7969):357-62.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-1e6b84b8-06c1-417f-932b-0756713d79f3">
					  <unstructured_citation>Solares JR, Raimondi FE, Zhu Y, Rahimian F, Canoy D, Tran J, et al. Deep learning for electronic health records: A comparative review of multiple deep neural architectures. J Biomed Inform. 2020;101:103337.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-22332ddf-c9b3-4554-a213-6bf5ac2712c5">
					  <unstructured_citation>Steinberg E, Jung K, Fries JA, Corbin CK, Pfohl SR, Shah NH. Language models are an effective representation learning technique for electronic health record data. J Biomed Inform. 2021;113:103637.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-a7e08af7-b0d4-4420-ade1-e4c9f3fbc71c">
					  <unstructured_citation>Shang J, Ma T, Xiao C, Sun J. Pre-training of graph augmented transformers for medication recommendation. arXiv preprint arXiv:1906.00346. 2019.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-984bbfbe-adf8-4670-8f71-b5d85c79e17a">
					  <unstructured_citation>Alsentzer E, Murphy J, Boag W, Weng WH, Jindi D, Naumann T, et al. Publicly available clinical BERT embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop; 2019. pp. 72-8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-e9174f1f-6ff9-43c2-8d1f-9e53a4762bdd">
					  <unstructured_citation>Huang K, Altosaar J, Ranganath R. ClinicalBERT: Modeling clinical notes and predicting hospital readmission. arXiv preprint arXiv:1904.05342. 2019.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-16ff10ff-90fc-4824-8c61-39c44694a502">
					  <unstructured_citation>El-Bouri R, Eyre DW, Watkinson P, Zhu T, Clifton DA. Hospital admission location prediction via deep interpretable networks for the year-round improvement of emergency patient care. IEEE J Biomed Health Inform. 2021;25(1):289-300.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-f4e64345-1bc7-4ec4-8bc3-7137ae5e5515">
					  <unstructured_citation>Barak-Corren Y, Chaudhari P, Perniciaro J, Waltzman M, Fine AM, Reis BY. Prediction across healthcare settings: a case study in predicting emergency department disposition. NPJ Digit Med. 2021;4(1):169.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-9112b9fb-57ce-46a1-a259-b06e8ad15c3e">
					  <unstructured_citation>Zeleke AJ, Palumbo P, Tubertini P, Miglio R, Chiari L. Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis. Front Artif Intell. 2023;6:1179226.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-035f9509-088d-4e5a-bef9-af260b67c8f8">
					  <unstructured_citation>Arndt BG, Beasley JW, Watkinson MD, Temte JL, Tuan WJ, Sinsky CA, et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med. 2017;15(5):419-26.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-f170eb24-7770-4f1b-a1cb-5dfa3e2ccc7c">
					  <unstructured_citation>Tai-Seale M, Olson CW, Li J, Chan AS, Morikawa C, Durbin M, et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Aff (Millwood). 2017;36(4):655-62.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-66cbcea6-f798-43dc-b7ae-6e44d49874db">
					  <unstructured_citation>Baxter SL, Saseendrakumar BR, Cheung M, Savides TJ, Longhurst CA, Sinsky CA, et al. Association of electronic health record inbasket message characteristics with physician burnout. JAMA Netw Open. 2022;5(11):e2244363.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-c1ea62b2-0e4d-4741-9fcc-77ea611d479f">
					  <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/k304401829-e39f0cad-b1d1-430d-b8bb-6f3dcbbe70e4">
					  <unstructured_citation>Lukac PJ, Turner W, Vangala S, Chin AT, Khalili J, Shih YC, et al. Ambient AI scribes in clinical practice: a randomized trial. NEJM AI. 2025;2(12):AIoa2501000.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-f1d02b32-8f01-42a9-8900-90170515d183">
					  <unstructured_citation>Georgiev K, Doudesis D, McPeake J, Mills NL, Shenkin SD, Fleuriot JD, et al. Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records. NPJ Digit Med. 2025;8(1):764.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-de9187df-5f7b-4beb-ac6d-2c159636e428">
					  <unstructured_citation>Zeinali F, Taaffe K, Gaafary C, Jackson W, Ramsay M, Hobbs J, et al. Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study. BMC Emerg Med. 2026.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-6e588761-924d-4a06-9219-041bf3e5d88f">
					  <unstructured_citation>AlSaad R, Abd-Alrazaq A, Boughorbel S, Ahmed A, Renault MA, Damseh R, et al. Multimodal large language models in health care: applications, challenges, and future outlook. J Med Internet Res. 2024;26:e59505.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-3d35cf6a-b765-4833-9b4a-476de0d58005">
					  <unstructured_citation>Yuanyuan Z, Adel B, Mina B, Jamil Z, Hugues T, Lydie B, et al. A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data. NPJ Digit Med. 2025;8(1):362.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-f6ed95f2-0a0c-422e-91a4-64eddd46a1bc">
					  <unstructured_citation>Rittenberg E, Liebman JB, Rexrode KM. Primary care physician gender and electronic health record workload. J Gen Intern Med. 2022;37(13):3295-301.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-01eb8eb8-a749-48ee-ade7-98981d7ae53f">
					  <unstructured_citation>Roberts K. Large language models for reducing clinicians’ documentation burden. Nat Med. 2024;30(4):942-3.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-6596bb2d-45d8-4f0d-9ea0-642f4e53a31d">
					  <unstructured_citation>Song JW, Park J, Kim JH, You SC. Large language model assistant for emergency department discharge documentation. JAMA Netw Open. 2025;8(10):e2538427.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/k304401829-62fb04e5-caf9-4a09-855b-f3befa679f25">
					  <unstructured_citation>Jain R, Singh M, Rao AR, Garg R. Predicting hospital length of stay using machine learning on a large open health dataset. BMC Health Serv Res. 2024;24(1):860.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
