Machine learning for medication error detection: a scoping review

Machine learning for medication error detection: a scoping review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Machine learning for medication error detection: a scoping review Félicien Hêche, Sohrab Ferdowsi, Anthony Yazdani, Sara Sansaloni-Pastor, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8919709/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Medication errors remain a substantial public health concern, and existing measures, such as workforce training, have achieved only partial success. Advances in data availability and computational methods have led to increasing use of machine learning (ML) to support medication safety. This scoping review synthesizes and categorizes ML-based approaches to medication error detection or prediction. Materials and Methods Following Preferred Reporting Items for Systematic Reviews and Meta- Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, PubMed, Embase, and Web of Science were searched for studies published between 2015 and April 2025. Two reviewers indepen- dently performed study selection using predefined eligibility criteria, and data extraction followed a structured extraction framework. Results Twenty-two studies met the inclusion criteria. Two dominant ML pipelines were identified. Most studies focused on prescription-related errors, relying on structured clinical data and tree-based models. A smaller group addressed medication-administration errors using unstructured multimodal data, such as images or video, analyzed with neural networks and multi-stage detection pipelines. Discussion ML shows substantial potential for medication error detection, particularly in prescription-focused workflows that align well with existing clinical processes. However, the evi- dence remains fragmented, with limited generalizability, inconsistent labeling, and scarce real-world evaluation. No studies addressed medication errors in clinical research settings, such as clinical trials, despite their distinct workflows and safety implications. Conclusion Advancing ML-based medication error detection will require high-quality multicenter datasets, rigorous and transparent validation, and deeper exploration of underused data modalities, including free text. Machine learning Medication errors Scoping review Full Text Additional Declarations The authors declare potential competing interests as follows: S.S-P. works for Actelion Pharmaceuticals Ltd. The other authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8919709","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":594053636,"identity":"cbb6c2d9-3d89-4c02-9863-38c449915cef","order_by":0,"name":"Félicien Hêche","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBAC9gbmhgMMBkCWBAMDMwODDZgEggM4tfAcYETRkkacFggLouUwTAKPFvbGxgM/CmzyGKSbD34uqDifuJ2d/eIDhpo7uLXwHGw42GOQVswgcyxZesaZ24k7m3mKDRiOPcOpxV4iseEwg8HhxAaJHDNm3rbbiRsO86RJMDYcxqmFR/4hSMt/oJb8b8y8/86BtKT/wKsFbKDBAZAtbMy8DQeAWtiPMeDVwpMI8ktyYptEmrE0z7FkY6AtzBIJx/BoYT98+MOPP3aJ/RLJDz/z1NjJbjh//OGHDzW4tcABG5IxBgwJhDWgAPYHJGoYBaNgFIyCYQ4AympZ2IrZlEAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-3425-8189","institution":"University of Geneva","correspondingAuthor":true,"prefix":"","firstName":"Félicien","middleName":"","lastName":"Hêche","suffix":""},{"id":594053637,"identity":"42a2261d-9a25-4d29-a81f-6451a1e8fe8d","order_by":1,"name":"Sohrab Ferdowsi","email":"","orcid":"https://orcid.org/0000-0003-3768-6408","institution":"University of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Sohrab","middleName":"","lastName":"Ferdowsi","suffix":""},{"id":594053638,"identity":"e82329fe-6449-434c-843c-3414b24aa142","order_by":2,"name":"Anthony Yazdani","email":"","orcid":"https://orcid.org/0000-0003-3309-6128","institution":"University of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Yazdani","suffix":""},{"id":594053639,"identity":"dd764904-9b42-4c41-950c-926e4eaa9a44","order_by":3,"name":"Sara Sansaloni-Pastor","email":"","orcid":"","institution":"Actelion Pharmaceuticals Ltd","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Sansaloni-Pastor","suffix":""},{"id":594053640,"identity":"09b32717-bd13-493e-81e7-95a84b67769d","order_by":4,"name":"Douglas Teodoro","email":"","orcid":"https://orcid.org/0000-0001-6238-4503","institution":"University of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Douglas","middleName":"","lastName":"Teodoro","suffix":""}],"badges":[],"createdAt":"2026-02-19 17:20:40","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8919709/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8919709/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"The authors declare potential competing interests as follows: S.S-P. works for Actelion Pharmaceuticals Ltd. The other authors declare no competing interests.","formattedTitle":"\u003cp\u003eMachine learning for medication error detection: a scoping review\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Geneva","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, Medication errors, Scoping review","lastPublishedDoi":"10.21203/rs.3.rs-8919709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8919709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eMedication errors remain a substantial public health concern, and existing measures, such as workforce training, have achieved only partial success. Advances in data availability and computational methods have led to increasing use of machine learning (ML) to support medication safety. This scoping review synthesizes and categorizes ML-based approaches to medication error detection or prediction.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003e Following Preferred Reporting Items for Systematic Reviews and Meta- Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, PubMed, Embase, and Web of Science were searched for studies published between 2015 and April 2025. Two reviewers indepen- dently performed study selection using predefined eligibility criteria, and data extraction followed a structured extraction framework.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwenty-two studies met the inclusion criteria. Two dominant ML pipelines were identified. Most studies focused on prescription-related errors, relying on structured clinical data and tree-based models. A smaller group addressed medication-administration errors using unstructured multimodal data, such as images or video, analyzed with neural networks and multi-stage detection pipelines.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eML shows substantial potential for medication error detection, particularly in prescription-focused workflows that align well with existing clinical processes. However, the evi- dence remains fragmented, with limited generalizability, inconsistent labeling, and scarce real-world evaluation. No studies addressed medication errors in clinical research settings, such as clinical trials, despite their distinct workflows and safety implications.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAdvancing ML-based medication error detection will require high-quality multicenter datasets, rigorous and transparent validation, and deeper exploration of underused data modalities, including free text.\u003c/p\u003e","manuscriptTitle":"Machine learning for medication error detection: a scoping review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 12:43:13","doi":"10.21203/rs.3.rs-8919709/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c067342d-7ffc-47ab-a6ee-03159cae248d","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-20T12:43:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 12:43:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8919709","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8919709","identity":"rs-8919709","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}