<?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-sL8y-1790899043-v886777840</doi_batch_id>
		<timestamp>1790899043</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>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Medication Safety Analytics in Clinical Systems: Reconciliation Logic, Error Taxonomies, and Deployment Constraints</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Oliver</given_name>
            <surname>Grant</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>David</given_name>
            <surname>Clark</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Sophia</given_name>
            <surname>Nguyen</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2022</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/v886777840</doi>
					<resource>https://cirpublications.com/pub/journal/2/article/v886777840</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/v886777840-d68a2188-15b9-44db-a5f5-a6dce852db86">
					  <unstructured_citation>Rozenblum R, Rodriguez-Monguio R, Volk LA, Forsythe KJ, Myers S, McGurrin M, et al. Using a machine learning system to identify and prevent medication prescribing errors: a clinical and cost analysis evaluation. Jt Comm J Qual Patient Saf. 2020;46(1):3-10.</unstructured_citation>
						 <doi>10.1016/j.jcjq.2019.09.002</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-95c77ab7-2718-44f0-b7d8-bb8f684d3bb6">
					  <unstructured_citation>Syrowatka A, Kuznetsova M, Alsubai A, Beckman AL, Bain PA, Craig KJT, et al. Leveraging artificial intelligence for patient safety: systematic review and meta-analysis of studies on adverse event detection and prediction from electronic health records. Lancet Digit Health. 2022;4(1):e52-e64.</unstructured_citation>
						 <doi>10.1016/S2589-7500(21)00203-2</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-c6a67dd0-8715-4724-bdda-f372dd306ad3">
					  <unstructured_citation>Wong A, Amato MG, Seger DL, Slight SP, Beeler PE, Dykes PC, et al. Evaluation of medication-related clinical decision support alert overrides in the intensive care unit. J Crit Care. 2017;39:156-61.</unstructured_citation>
						 <doi>10.1016/j.jcrc.2017.02.027</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-69acf28e-80e9-4792-ae2f-f1ad04d8105c">
					  <unstructured_citation>Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform. 2020;8(7):e18599.</unstructured_citation>
						 <doi>10.2196/18599</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-6fa0895f-b5ed-41fc-aa56-f31b9749812e">
					  <unstructured_citation>Bates DW, Levine D, Syrowatka A, Kuznetsova M, Craig KJT, Rui A, et al. The potential of artificial intelligence to improve patient safety: a scoping review. npj Digit Med. 2021;4(1):54.</unstructured_citation>
						 <doi>10.1038/s41746-021-00423-6</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-fad8be1c-a415-415f-895c-d20b3651e4e7">
					  <unstructured_citation>Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-43.</unstructured_citation>
						 <doi>10.1136/svn-2017-000101</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-78edd2c1-5c7b-4c5b-b9e7-73e429566c1e">
					  <unstructured_citation>Mehta N, Devarakonda MV. Machine learning, natural language programming, and electronic health records: the next step in the artificial intelligence journey? J Allergy Clin Immunol. 2018;141(6):2019-2021.e1.</unstructured_citation>
						 <doi>10.1016/j.jaci.2018.02.025</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-acce73bc-3641-45e0-97d1-8ed0398902c4">
					  <unstructured_citation>Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-58.</unstructured_citation>
						 <doi>10.1056/NEJMra1814259</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-9559a182-fb59-4b98-9b05-d9c82238bd58">
					  <unstructured_citation>Panch T, Szolovits P, Atun R. Artificial intelligence, machine learning and health systems. J Glob Health. 2018;8(2):020303.</unstructured_citation>
						 <doi>10.7189/jogh.08.020303</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-50baabc7-3895-46ef-adcd-8d2ee51ceb08">
					  <unstructured_citation>Wong A, Plasek JM, Montecalvo SP, Zhou L. Natural language processing and its implications for the future of medication safety: a narrative review. Pharmacotherapy. 2018;38(8):822-41.</unstructured_citation>
						 <doi>10.1002/phar.2152</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-0aad5f3e-5694-4b43-aab5-501494bb3816">
					  <unstructured_citation>Lesselroth BJ, Adams K, Church VL, Tallett S, Russ Y, Wiedrick J, et al. Evaluation of multimedia medication reconciliation software: a randomized controlled single-blind trial to measure diagnostic accuracy for discrepancy detection. Appl Clin Inform. 2018;9(2):285-301.</unstructured_citation>
						 <doi>10.1055/s-0038-1642900</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-1ec1f4c9-c3ae-4460-b116-31805a4225ea">
					  <unstructured_citation>Lesselroth B, Adams K, Tallett S, Ong L, Bliss S, Ragland S, et al. Naturalistic usability testing of inpatient medication reconciliation software. Stud Health Technol Inform. 2017;234:201-5.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v886777840-c66c2b52-b250-425a-a81e-ab878047170c">
					  <unstructured_citation>Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.</unstructured_citation>
						 <doi>10.1038/s41591-018-0300-7</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-29132116-506d-43a0-959a-17db078c8533">
					  <unstructured_citation>Chen M, Decary M. Artificial intelligence in healthcare: an essential guide for health leaders. Healthc Manage Forum. 2020;33(1):10-8.</unstructured_citation>
						 <doi>10.1177/0840470419873123</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-b9162f42-977b-4af9-bca4-0068ae18f1e5">
					  <unstructured_citation>Corny J, Rajkumar A, Martin O, Dode X, Lajonchère JP, Billuart O, et al. A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error. J Am Med Inform Assoc. 2020;27(11):1688-94.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v886777840-9ad5326d-4e66-4f77-aa48-4d0643c15761">
					  <unstructured_citation>Chin YPH, Culley S, Jalan R, Covington JD, Tajuddin N, Shafner L, et al. Assessing the international transferability of a machine learning model for detecting medication error in the general internal medicine clinic: multicenter preliminary validation study. JMIR Med Inform. 2021;9(1):e23454.</unstructured_citation>
						 <doi>10.2196/23454</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-41dddc87-ae9d-4a63-9891-3f2086bf9579">
					  <unstructured_citation>Babel A, Taneja R. Artificial intelligence solutions to increase medication adherence in patients with non-communicable diseases. Front Digit Health. 2021;3:669869.</unstructured_citation>
						 <doi>10.3389/fdgth.2021.669869</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-a74cd236-0491-423c-94c0-7355c6973866">
					  <unstructured_citation>Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70-6.</unstructured_citation>
						 <doi>10.1097/SLA.0000000000002693</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-1d458b28-3e19-4451-bca8-6bf9e9dcf74f">
					  <unstructured_citation>Joshi S, Sharma M. Modeling conceptual framework for implementing barriers of AI in public healthcare for improving operational excellence: experiences from developing countries. Sustainability. 2022;14(18):11698.</unstructured_citation>
						 <doi>10.3390/su141811698</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-8ea142f2-ca5b-4501-8e40-087884c832e3">
					  <unstructured_citation>Kristiansen TB, Kristensen PA, Edwards IR, Nissen A. Erroneous data: the Achilles’ heel of AI and personalized medicine. Front Digit Health. 2022;4:862095.</unstructured_citation>
						 <doi>10.3389/fdgth.2022.862095</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-1889b1b3-a586-4cb6-b056-8f886e7b1127">
					  <unstructured_citation>Council for International Organizations of Medical Sciences (CIOMS) Working Group XIV. Artificial intelligence in pharmacovigilance: a CIOMS report. Geneva: CIOMS; 2022.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v886777840-a732223e-2a9e-4212-9b5b-9fa647826c7d">
					  <unstructured_citation>European Parliamentary Research Service. Artificial intelligence in healthcare: applications, risks, and ethical and societal impacts. Brussels: European Parliament; 2022</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/v886777840-5992f30a-d1d9-4672-aee5-0d57d77d6f14">
					  <unstructured_citation>Fong A, Adams K, Samarth A, McDonough L, Pierce E, Hilmas E, et al. Exploring opportunities for AI supported medication error categorization: a brief report in human machine collaboration. Front Drug Saf Regul. 2022;2:1021068.</unstructured_citation>
						 <doi>10.3389/fdsfr.2022.1021068</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-78b22df6-299e-41a5-877b-6316e6abe11c">
					  <unstructured_citation>Agbabiaka TB, Savović J, Ernst E. Methods for causality assessment of adverse drug reactions: a systematic review. Drug Saf. 2018;41(1):21-37.</unstructured_citation>
						 <doi>10.1007/s40264-017-0605-3</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-19dc9f85-2630-4aa9-a147-3e75e23a99d3">
					  <unstructured_citation>Furniss D, Lyons I, Franklin BD, Mayer A, Chumbley G, Wei L, et al. Procedural and documentation variations in intravenous infusion administration: a mixed methods study of policy and practice across 16 hospital trusts in England. BMC Health Serv Res. 2018;18(1):270.</unstructured_citation>
						 <doi>10.1186/s12913-018-3025-x</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-17df3cd1-6b93-43a3-a60b-f6d1cc9fdd4d">
					  <unstructured_citation>Manskow US, Kristensen T, Baysa E, Fagereng E, Blixgård HK, Berntsen G, et al. Challenges faced by health professionals in obtaining correct medication information in the absence of a shared digital medication list. Pharmacy (Basel). 2021;9(1):46.</unstructured_citation>
						 <doi>10.3390/pharmacy9010046</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-8dd23c08-e250-42ef-853c-1f0d3a706c0e">
					  <unstructured_citation>Väänänen A, Haase KR. Artificial intelligence in healthcare: a scoping review on benefits, challenges, and applications. F1000Res. 2021;10:6.</unstructured_citation>
						 <doi>10.12688/f1000research.30798.1</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-1f86a089-b4be-421b-83f2-b9b697f2e70d">
					  <unstructured_citation>Golpayegani D, Sahli N, Fernandez B. Towards a taxonomy of AI risks in the health domain. HEALTHINF. 2022:426-33.</unstructured_citation>
						 <doi>10.5220/0010860200003123</doi> 					</citation>
          					<citation key="rk-10.68159/v886777840-1ffe5364-de0b-4781-8f0e-4e296ffdc7da">
					  <unstructured_citation>Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73-81.</unstructured_citation>
						 <doi>10.1080/13645706.2019.1571881</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
