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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>Guideline Adherence Modeled as Temporal Logic: A Conformance Verification Framework for Order-Set Evaluation</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Samuel</given_name>
            <surname>Boateng</surname>
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            <given_name>Kwesi</given_name>
            <surname>Mensah</surname>
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            <given_name>Kojo</given_name>
            <surname>Asante</surname>
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					<year>2026</year>
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					  <unstructured_citation>Anselma L, Piovesan L, Terenziani P. Temporal detection and analysis of guideline interactions. Artif Intell Med. 2017;76:40-62.</unstructured_citation>
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					  <unstructured_citation>Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019;25(9):1337-40.</unstructured_citation>
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