{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:32Z","timestamp":1755219812250,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686080"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>This paper reviews clinical decision support systems (CDSS) challenges in minimizing medication errors. Challenges such as alert fatigue, irrelevant alerts, and high override rates hinder CDSS alerts\u2019 effectiveness. We explored the optimization of CDSS alerts from selected frameworks that address critical issues like alert specificity, sensitivity, and clinician engagement from socio-technical factors, specifically the human, organization, process, and technology (HOPT)-fit framework, the theoretical domains framework (TDF), and the guideline implementation with a decision support (GUIDES) framework. Guided by the five rights principle and a four-stage validation strategy, our analysis emphasizes tailoring alerts using patient-specific and context-sensitive data to enhance alert specificity, sensitivity, and clinician engagement. This strategy offers a practical pathway for enhancing CDSS alerts\u2019 precision and clinical relevance to improve patient outcomes.<\/jats:p>","DOI":"10.3233\/shti250853","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:33:08Z","timestamp":1754566388000},"source":"Crossref","is-referenced-by-count":0,"title":["A Review on Cdss Alerts Optimization: Balancing Precision and Relevance"],"prefix":"10.3233","author":[{"given":"Zaib Un Nisa","family":"Khosa","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, University Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryati Mohd","family":"Yusof","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, University Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250853","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:33:08Z","timestamp":1754566388000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250853"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250853","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}