{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T01:14:39Z","timestamp":1780449279115,"version":"3.54.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T00:00:00Z","timestamp":1713484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dutch national Medicines Coordination Center"},{"name":"Landelijk Co\u00f6rdinatiecentrum Geneesmiddelen"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Out of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion and Conclusion<\/jats:title>\n                  <jats:p>AI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models\u2019 development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae076","type":"journal-article","created":{"date-parts":[[2024,4,20]],"date-time":"2024-04-20T00:57:21Z","timestamp":1713574641000},"page":"1411-1422","source":"Crossref","is-referenced-by-count":50,"title":["The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review"],"prefix":"10.1093","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5275-1199","authenticated-orcid":false,"given":"Jetske","family":"Graafsma","sequence":"first","affiliation":[{"name":"Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen , Groningen, 9713GZ, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2602-5658","authenticated-orcid":false,"given":"Rachel M","family":"Murphy","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics Amsterdam UMC, University of Amsterdam , Amsterdam, 1000GG, The Netherlands"},{"name":"Amsterdam Public Health Institute, Digital Health and Quality of Care , Amsterdam, 1105AZ, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ewoudt M W","family":"van de Garde","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, St Antonius Hospital , Utrecht, 3430AM, The Netherlands"},{"name":"Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University , Utrecht, 3584CS, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatma","family":"Karapinar-\u00c7arkit","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Center , Maastricht, 6229HX, The Netherlands"},{"name":"Department of Clinical Pharmacy, CARIM, Cardiovascular Research Institute Maastricht, Maastricht University , Maastricht, 6229ER, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hieronymus J","family":"Derijks","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, Jeroen Bosch Hospital , Den Bosch, 5200ME, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rien H L","family":"Hoge","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, Wilhelmina Hospital , Assen, 9401RK, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanna E","family":"Klopotowska","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics Amsterdam UMC, University of Amsterdam , Amsterdam, 1000GG, The Netherlands"},{"name":"Amsterdam Public Health Institute, Digital Health and Quality of Care , Amsterdam, 1105AZ, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patricia M L A","family":"van den Bemt","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen , Groningen, 9713GZ, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"2024052019465029300_ocae076-B1","doi-asserted-by":"crossref","first-page":"b1775","DOI":"10.1136\/bmj.b1775","article-title":"Global priorities for patient safety research","volume":"338","author":"Bates","year":"2009","journal-title":"BMJ"},{"issue":"17","key":"2024052019465029300_ocae076-B2","first-page":"1890","article-title":"Frequency of and risk factors for preventable medication-related hospital admissions in the Netherlands","volume":"168","author":"Leendertse","year":"2008","journal-title":"Arch Intern Med"},{"issue":"12","key":"2024052019465029300_ocae076-B3","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1002\/pds.5122","article-title":"Prevalence and incidence rate of hospital admissions related to medication between 2008 and 2013 in The Netherlands","volume":"29","author":"Lghoul-Oulad Sa\u00efd","year":"2020","journal-title":"Pharmacoepidemiol Drug Saf"},{"issue":"12","key":"2024052019465029300_ocae076-B4","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1007\/s00228-017-2330-3","article-title":"The prevalence of medication-related adverse events in inpatients-a systematic review and meta-analysis","volume":"73","author":"Laatikainen","year":"2017","journal-title":"Eur J Clin Pharmacol"},{"issue":"2","key":"2024052019465029300_ocae076-B5","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1056\/NEJMsa2206117","article-title":"The safety of inpatient health care","volume":"388","author":"Bates","year":"2023","journal-title":"N Engl J Med"},{"issue":"3","key":"2024052019465029300_ocae076-B6","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2165\/11596000-000000000-00000","article-title":"Targeting outpatient drug safety: recommendations of the Dutch HARM-Wrestling Task Force","volume":"35","author":"Warl\u00e9-van Herwaarden","year":"2012","journal-title":"Drug Saf"},{"issue":"1","key":"2024052019465029300_ocae076-B7","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1136\/amiajnl-2011-000360","article-title":"Comparison of a basic and an advanced pharmacotherapy-related clinical decision support system in a hospital care setting in The Netherlands","volume":"19","author":"Eppenga","year":"2012","journal-title":"J Am Med Inform Assoc"},{"key":"2024052019465029300_ocae076-B8","first-page":"1488","article-title":"When an alert is not an alert: a pilot study to characterize behavior and cognition associated with medication alerts","volume":"2018","author":"Reese","year":"2018","journal-title":"AMIA Annu Symp Proc"},{"issue":"6","key":"2024052019465029300_ocae076-B9","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.ijmedinf.2015.02.004","article-title":"Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients","volume":"84","author":"de Wit","year":"2015","journal-title":"Int J Med Inform"},{"issue":"4","key":"2024052019465029300_ocae076-B10","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1093\/jamia\/ocu010","article-title":"Drug-drug interaction checking assisted by clinical decision support: a return on investment analysis","volume":"22","author":"Helmons","year":"2015","journal-title":"J Am Med Inform Assoc"},{"issue":"7","key":"2024052019465029300_ocae076-B11","doi-asserted-by":"crossref","first-page":"e15653","DOI":"10.2196\/15653","article-title":"Appropriateness of overridden alerts in computerized physician order entry: systematic review","volume":"8","author":"Poly","year":"2020","journal-title":"JMIR Med Inform"},{"issue":"2","key":"2024052019465029300_ocae076-B12","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1197\/jamia.M1809","article-title":"Overriding of drug safety alerts in computerized physician order entry","volume":"13","author":"van der Sijs","year":"2006","journal-title":"J Am Med Inform Assoc"},{"key":"2024052019465029300_ocae076-B13","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/978-3-319-99713-1_11","volume-title":"Fundamentals of Clinical Data Science","author":"Wasylewicz","year":"2019"},{"issue":"12","key":"2024052019465029300_ocae076-B14","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.1093\/jamia\/ocad193","article-title":"Post-implementation optimization of medication alerts in hospital computerized provider order entry systems: a scoping review","volume":"30","author":"Ledger","year":"2023","journal-title":"J Am Med Inform Assoc"},{"issue":"10425","key":"2024052019465029300_ocae076-B15","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/S0140-6736(23)02465-0","article-title":"The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial","volume":"403","author":"Bakker","year":"2024","journal-title":"Lancet"},{"issue":"1","key":"2024052019465029300_ocae076-B16","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1093\/jamia\/ocaa279","article-title":"Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals","volume":"28","author":"Van Dort","year":"2021","journal-title":"J Am Med Inform Assoc"},{"issue":"2","key":"2024052019465029300_ocae076-B17","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1002\/cpt.2624","article-title":"Contextualized drug-drug interaction management improves clinical utility compared with basic drug-drug interaction management in hospitalized patients","volume":"112","author":"Wasylewicz","year":"2022","journal-title":"Clin Pharmacol Ther"},{"key":"2024052019465029300_ocae076-B18","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41746-020-0221-y","article-title":"An overview of clinical decision support systems: benefits, risks, and strategies for success","volume":"3","author":"Sutton","year":"2020","journal-title":"NPJ Digit Med"},{"issue":"4","key":"2024052019465029300_ocae076-B19","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1197\/jamia.M2311","article-title":"Turning off frequently overridden drug alerts: limited opportunities for doing it safely","volume":"15","author":"van der Sijs","year":"2008","journal-title":"J Am Med Inform Assoc"},{"issue":"9","key":"2024052019465029300_ocae076-B20","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat Med"},{"key":"2024052019465029300_ocae076-B21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/B978-0-12-818438-7.00002-2","volume-title":"Artificial Intelligence in Healthcare","author":"Bohr","year":"2020"},{"issue":"4","key":"2024052019465029300_ocae076-B22","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc Neurol"},{"key":"2024052019465029300_ocae076-B23","doi-asserted-by":"crossref","first-page":"1224347","DOI":"10.3389\/fonc.2023.1224347","article-title":"Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis","volume":"13","author":"Oehring","year":"2023","journal-title":"Front Oncol"},{"issue":"3","key":"2024052019465029300_ocae076-B24","doi-asserted-by":"crossref","first-page":"375","DOI":"10.17219\/acem\/115083","article-title":"The ways of using machine learning in dentistry","volume":"29","author":"Machoy","year":"2020","journal-title":"Adv Clin Exp Med"},{"issue":"5","key":"2024052019465029300_ocae076-B25","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1016\/j.cmi.2019.09.009","article-title":"Machine learning for clinical decision support in infectious diseases: a narrative review of current applications","volume":"26","author":"Peiffer-Smadja","year":"2020","journal-title":"Clin Microbiol Infect"},{"issue":"3","key":"2024052019465029300_ocae076-B26","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1097\/XEB.0000000000000050","article-title":"Guidance for conducting systematic scoping reviews","volume":"13","author":"Peters","year":"2015","journal-title":"Int J Evid Based Healthc"},{"issue":"7","key":"2024052019465029300_ocae076-B27","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"key":"2024052019465029300_ocae076-B28","article-title":"The use of artificial intelligence to improve medication-related clinical decision support systems: a scoping review protocol","author":"Graafsma","year":"2023","journal-title":"Open Science Framework"},{"issue":"17","key":"2024052019465029300_ocae076-B29","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1093\/ajhp\/60.17.1750","article-title":"Nature of preventable adverse drug events in hospitals: a literature review","volume":"160","author":"Kanjanarat","year":"2003","journal-title":"Am J Health Syst Pharm"},{"issue":"9","key":"2024052019465029300_ocae076-B30","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1345\/aph.1H658","article-title":"Systematic review of the incidence and characteristics of preventable adverse drug events in ambulatory care","volume":"41","author":"Thomsen","year":"2007","journal-title":"Ann Pharmacother"},{"key":"2024052019465029300_ocae076-B31","author":"ASReview LAB Developers","year":"2024"},{"issue":"2","key":"2024052019465029300_ocae076-B32","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1038\/s42256-020-00287-7","article-title":"An open source machine learning framework for efficient and transparent systematic reviews","volume":"3","author":"van de Schoot","year":"2021","journal-title":"Nat Mach Intell"},{"issue":"1","key":"2024052019465029300_ocae076-B33","doi-asserted-by":"crossref","first-page":"e0227742","DOI":"10.1371\/journal.pone.0227742","article-title":"Error rates of human reviewers during abstract screening in systematic reviews","volume":"15","author":"Wang","year":"2020","journal-title":"PLoS One"},{"key":"2024052019465029300_ocae076-B34","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.artmed.2016.02.001","article-title":"Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs","volume":"68","author":"Beaudoin","year":"2016","journal-title":"Artif Intell Med"},{"key":"2024052019465029300_ocae076-B35","first-page":"229","article-title":"Prediction-based threshold for medication alert","volume":"192","author":"Kawazoe","year":"2013","journal-title":"Stud Health Technol Inform"},{"issue":"2","key":"2024052019465029300_ocae076-B36","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1093\/jamia\/ocw171","article-title":"Screening for medication errors using an outlier detection system","volume":"24","author":"Schiff","year":"2017","journal-title":"J Am Med Inform Assoc"},{"issue":"11","key":"2024052019465029300_ocae076-B37","doi-asserted-by":"crossref","first-page":"e19489","DOI":"10.2196\/19489","article-title":"Machine learning approach to reduce alert fatigue using a disease medication-related clinical decision support system: model development and validation","volume":"8","author":"Poly","year":"2020","journal-title":"JMIR Med Inform"},{"issue":"8","key":"2024052019465029300_ocae076-B38","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1093\/jamia\/ocab071","article-title":"Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders","volume":"28","author":"Hogue","year":"2021","journal-title":"J Am Med Inform Assoc"},{"issue":"3","key":"2024052019465029300_ocae076-B39","doi-asserted-by":"crossref","first-page":"ooab083","DOI":"10.1093\/jamiaopen\/ooab083","article-title":"Predicting inpatient pharmacy order interventions using provider action data","volume":"4","author":"Balestra","year":"2021","journal-title":"JAMIA Open"},{"issue":"1","key":"2024052019465029300_ocae076-B40","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s40264-021-01123-6","article-title":"Hybrid method incorporating a rule-based approach and deep learning for prescription error prediction","volume":"45","author":"Lee","year":"2022","journal-title":"Drug Saf"},{"issue":"5","key":"2024052019465029300_ocae076-B41","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1093\/jamia\/ocab292","article-title":"The potential for leveraging machine learning to filter medication alerts","volume":"29","author":"Liu","year":"2022","journal-title":"J Am Med Inform Assoc"},{"issue":"12","key":"2024052019465029300_ocae076-B42","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1093\/jamia\/ocz135","article-title":"Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting","volume":"26","author":"Segal","year":"2019","journal-title":"J Am Med Inform Assoc"},{"issue":"11","key":"2024052019465029300_ocae076-B43","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1093\/jamia\/ocaa154","article-title":"A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error","volume":"27","author":"Corny","year":"2020","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"2024052019465029300_ocae076-B44","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s12911-017-0430-8","article-title":"Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system","volume":"17","author":"Ancker","year":"2017","journal-title":"BMC Med Inform Decis Mak"},{"issue":"2","key":"2024052019465029300_ocae076-B45","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1197\/jamia.M1809","article-title":"Overriding of drug safety alerts in computerized physician order entry","volume":"13","author":"van der Sijs","year":"2006","journal-title":"J Am Med Inform Assoc"},{"key":"2024052019465029300_ocae076-B46","first-page":"100346","article-title":"Artificial intelligence in the field of pharmacy practice: a literature review","volume":"12","author":"Chalasani","year":"2023","journal-title":"Explor Res Clin Soc Pharm"},{"issue":"10","key":"2024052019465029300_ocae076-B47","doi-asserted-by":"crossref","first-page":"e40238","DOI":"10.2196\/40238","article-title":"Artificial intelligence applications in health care practice: scoping review","volume":"24","author":"Sharma","year":"2022","journal-title":"J Med Internet Res"},{"issue":"1","key":"2024052019465029300_ocae076-B48","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","year":"2019","journal-title":"BMC Med"},{"issue":"4","key":"2024052019465029300_ocae076-B49","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s43681-022-00138-8","article-title":"AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry","volume":"2","author":"Belenguer","year":"2022","journal-title":"AI Ethics"},{"key":"2024052019465029300_ocae076-B50","author":"Parliament","year":"2022"},{"issue":"1","key":"2024052019465029300_ocae076-B51","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann Intern Med"},{"issue":"10181","key":"2024052019465029300_ocae076-B52","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1016\/S0140-6736(19)30037-6","article-title":"Reporting of artificial intelligence prediction models","volume":"393","author":"Collins","year":"2019","journal-title":"Lancet"},{"issue":"5","key":"2024052019465029300_ocae076-B53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pmed.1001221","article-title":"Reporting and methods in clinical prediction research: a systematic review","volume":"9","author":"Bouwmeester","year":"2012","journal-title":"PLoS Med"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/31\/6\/1411\/57769029\/ocae076.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/31\/6\/1411\/57769029\/ocae076.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T19:48:07Z","timestamp":1716234487000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/31\/6\/1411\/7654019"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,19]]},"references-count":53,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,4,19]]},"published-print":{"date-parts":[[2024,5,20]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocae076","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,6,1]]},"published":{"date-parts":[[2024,4,19]]}}}