{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:55Z","timestamp":1747216195471,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685335"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":[[2024,8,22]]},"abstract":"<jats:p>Clinical decision support systems (CDSS) can efficiently support doctors in coping with ever-increasing amounts of data by providing evidence-based recommendations for medical decisions. To integrate the systems into the medical workflow and provide patient-specific recommendations for action in the context of personalized medicine, it is essential to tailor the systems to the context of use. This study aims to present an overview of factors influencing medical decision-making that CDSS must consider. Our approach involves the systematic identification and categorization of contextual factors relevant to medical decision-making. Through extensive literature research and a structured card-sorting workshop, we systematized 774 context factors and mapped them into a model. This model includes six primary entities: the treating physician, the patient, the patient\u2019s family, disease treatment, the physician\u2019s institution, and professional colleagues, each with their relevant context categories. The developed model could serve as a foundation for communication between developers and physicians, supporting the creation of more context-sensitive CDSS in the future. Ultimately, this could enhance the utilization of CDSS and improve patient care.<\/jats:p>","DOI":"10.3233\/shti240769","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:41:15Z","timestamp":1724409675000},"source":"Crossref","is-referenced-by-count":1,"title":["Mapping Medical Context: Workshop-Driven Clustering of Influencing Factors on Medical Decision-Making"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7054-4059","authenticated-orcid":false,"given":"Katharina","family":"Schuler","sequence":"first","affiliation":[{"name":"Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technical University Dresden, Dresden, Germany"}]},{"given":"Martin","family":"Sedlmayr","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technical University Dresden, Dresden, Germany"}]},{"given":"Brita","family":"Sedlmayr","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technical University Dresden, Dresden, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240769","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T10:41:16Z","timestamp":1724409676000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240769","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}