{"version":"6.9","hitCount":1,"request":{"queryString":"EXT_ID:41483296 AND SRC:MED","internalQuery":"EXT_ID:41483296 AND SRC:MED","resultType":"CORE","cursorMark":"*","pageSize":25,"sort":"","synonym":false},"resultList":{"result":[{"id":"41483296","source":"MED","pmid":"41483296","doi":"10.1007/s11096-025-02076-x","title":"Impact of an AI-powered hospital admission prediction dashboard to guide medication reconciliation in the emergency department: a retrospective before-after study.","authorString":"Maathuis J, Veldhuis A, Egbers JB, Geerdink J, Karapinar-Çarkit F, van den Bemt PMLA, Hulshof EC, Kingma JS.","authorList":{"author":[{"fullName":"Maathuis J","lastName":"Maathuis","firstName":"J","initials":"J","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Healthcare & Information Technology, Hospital Group Twente, Almelo & Hengelo, The Netherlands."}]}},{"fullName":"Veldhuis A","lastName":"Veldhuis","firstName":"A","initials":"A","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Healthcare & Information Technology, Hospital Group Twente, Almelo & Hengelo, The Netherlands."}]}},{"fullName":"Egbers JB","lastName":"Egbers","firstName":"J B","initials":"JB","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Emergency Medicine, Hospital Group Twente, Almelo & Hengelo, The Netherlands."}]}},{"fullName":"Geerdink J","lastName":"Geerdink","firstName":"J","initials":"J","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Healthcare & Information Technology, Hospital Group Twente, Almelo & Hengelo, The Netherlands."}]}},{"fullName":"Karapinar-Çarkit F","lastName":"Karapinar-Çarkit","firstName":"F","initials":"F","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Centre, Maastricht, The Netherlands."},{"affiliation":"Department of Clinical Pharmacy, CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands."}]}},{"fullName":"van den Bemt PMLA","lastName":"van den Bemt","firstName":"P M L A","initials":"PMLA","authorId":{"type":"ORCID","value":"0000-0003-1418-5520"},"authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Clinical Pharmacy and Pharmacology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands."}]}},{"fullName":"Hulshof EC","lastName":"Hulshof","firstName":"E C","initials":"EC","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Clinical Pharmacy, Hospital Group Twente, Almelo and Hengelo, The Netherlands."}]}},{"fullName":"Kingma JS","lastName":"Kingma","firstName":"J S","initials":"JS","authorAffiliationDetailsList":{"authorAffiliation":[{"affiliation":"Department of Clinical Pharmacy, Hospital Group Twente, Almelo and Hengelo, The Netherlands. j.kingma@zgt.nl."}]}}]},"authorIdList":{"authorId":[{"type":"ORCID","value":"0000-0003-1418-5520"}]},"journalInfo":{"issue":"3","volume":"48","journalIssueId":4193515,"dateOfPublication":"2026 Jun","monthOfPublication":6,"yearOfPublication":2026,"journal":{"medlineAbbreviation":"Int J Clin Pharm","title":"International journal of clinical pharmacy","essn":"2210-7711","issn":"2210-7703","nlmid":"101554912","isoabbreviation":"Int J Clin Pharm"},"printPublicationDate":"2026-06-01"},"pubYear":"2026","pageInfo":"801-807","abstractText":"<h4>Introduction</h4>Medication reconciliation (MR) in the emergency department (ED) is essential to ensure medication safety, especially for patients admitted to the hospital. However, performing MR for all ED patients, including those discharged, can be inefficient. To optimize prioritization, an artificial intelligence (AI)-powered hospital admission prediction dashboard was introduced.<h4>Aim</h4>The primary aim of this study was to evaluate the effect of an artificial intelligence (AI) powered hospital admission prediction dashboard on the proportion of patients admitted to the hospital with an MR performed in the ED. The secondary aim was to assess its effect on the proportion of patients discharged from the ED with an MR.<h4>Method</h4>This retrospective before-after study was conducted at Hospital Group Twente and included ED visits between March 15 and December 31 in both 2023 and 2024. In the pre-intervention period, MR was strived for any patient, including patients discharged directly from the ED (i.e. potentially unnecessary MR as the risk for errors is low) and for admitted patients (i.e. correct MR). In 2024, the post-intervention period, a set of Extreme Gradient Boosting (XGBoost) models was trained on historical data (2015-2022) and integrated into a real-time dashboard to prioritize patients with the highest admission probability to perform MR. Primary outcome was the proportion of patients with correct MR. Secondary outcome was the proportion of patients with a potentially unnecessary MR. Chi-square test was used to compare proportions before and after implementation of the dashboard.<h4>Results</h4>The study included 25,505 ED visits. Pre-intervention 12,743 ED visits were included with 5,252 MRs performed. Post-intervention 12,762 ED visits were included with 4,882 MRs. After implementing the dashboard, the proportion of patients with correct MR increased from 86.4 to 89.0% (p = 0.0002), and the proportion of patients with potentially unnecessary MR decreased from 17.9 to 12.6% (p < 0.0001).<h4>Conclusion</h4>The AI-powered hospital admission prediction dashboard improved the prioritization of MR in the ED. The proportion of patients with potentially unnecessary MR remains substantial and requires further improvement.","affiliation":"Department of Healthcare & Information Technology, Hospital Group Twente, Almelo & Hengelo, The Netherlands.","publicationStatus":"ppublish","language":"eng","pubModel":"Print-Electronic","pubTypeList":{"pubType":["Journal Article"]},"meshHeadingList":{"meshHeading":[{"majorTopic_YN":"N","descriptorName":"Humans"},{"majorTopic_YN":"N","descriptorName":"Medication Errors","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"PC","qualifierName":"prevention & control"}]}},{"majorTopic_YN":"Y","descriptorName":"Patient Admission","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"ST","qualifierName":"standards"},{"majorTopic_YN":"N","abbreviation":"TD","qualifierName":"trends"},{"majorTopic_YN":"N","abbreviation":"SN","qualifierName":"statistics & numerical data"}]}},{"majorTopic_YN":"N","descriptorName":"Patient Discharge"},{"majorTopic_YN":"N","descriptorName":"Retrospective Studies"},{"majorTopic_YN":"Y","descriptorName":"Artificial Intelligence","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"TD","qualifierName":"trends"}]}},{"majorTopic_YN":"N","descriptorName":"Adult"},{"majorTopic_YN":"N","descriptorName":"Aged"},{"majorTopic_YN":"N","descriptorName":"Middle Aged"},{"majorTopic_YN":"Y","descriptorName":"Emergency Service, Hospital","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"ST","qualifierName":"standards"},{"majorTopic_YN":"N","abbreviation":"TD","qualifierName":"trends"}]}},{"majorTopic_YN":"N","descriptorName":"Female"},{"majorTopic_YN":"N","descriptorName":"Male"},{"majorTopic_YN":"Y","descriptorName":"Medication Reconciliation","meshQualifierList":{"meshQualifier":[{"majorTopic_YN":"N","abbreviation":"MT","qualifierName":"methods"},{"majorTopic_YN":"N","abbreviation":"ST","qualifierName":"standards"}]}}]},"keywordList":{"keyword":["Artificial intelligence","Emergency Department","Medication Reconciliation","Patient Prioritization","Hospital Admission Prediction"]},"subsetList":{"subset":[{"code":"IM","name":"Index Medicus"}]},"fullTextUrlList":{"fullTextUrl":[{"availability":"Subscription required","availabilityCode":"S","documentStyle":"doi","site":"DOI","url":"https://doi.org/10.1007/s11096-025-02076-x"}]},"commentCorrectionList":{"commentCorrection":[{"source":"MED","id":"41627662","reference":"Int J Clin Pharm. 2026 Jun;48(3):1189. doi: 10.1007/s11096-026-02100-8.","type":"Erratum in","orderIn":1}]},"isOpenAccess":"N","inEPMC":"N","inPMC":"N","citedByCount":0,"hasReferences":"Y","hasTextMinedTerms":"N","hasDbCrossReferences":"N","hasPDF":"N","hasBook":"N","hasSuppl":"N","hasLabsLinks":"N","hasData":"N","license":"cc by-nc-nd","authMan":"N","epmcAuthMan":"N","nihAuthMan":"N","hasTMAccessionNumbers":"N","dateOfCompletion":"2026-05-14","dateOfCreation":"2026-01-03","dateOfRevision":"2026-05-14","electronicPublicationDate":"2026-01-03","firstPublicationDate":"2026-01-03","firstIndexDate":"2026-01-04","hasEvaluations":"N"}]}}