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			<depositor_name>Clinical Intelligence Research Press</depositor_name>
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				<full_title>Journal of Health Informatics and Digital Systems</full_title>
				<abbrev_title>J. Health Inform. Digit. Syst.</abbrev_title>
				<issn>3149-8973</issn>
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					<year>2026</year>
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					<volume>6</volume>
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				<issue>1</issue>
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					<title>Generative Artificial Intelligence for Healthcare Administration: A Systematic Review of Applications in Documentation Support, Operational Reporting, Patient Communication, Governance Dashboards, and Workflow Automation</title>
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            <given_name>Andrei</given_name>
            <surname>Popescu</surname>
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            <given_name>Mihai</given_name>
            <surname>Ionescu</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Elena</given_name>
            <surname>Stan</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Sorin</given_name>
            <surname>Dumitrescu</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Irina</given_name>
            <surname>Pavel</surname>
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								<publication_date>
					<year>2026</year>
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					  <unstructured_citation>Korngiebel DM, Mooney SD. Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery. NPJ Digit Med. 2021;4(1):93.</unstructured_citation>
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