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Clinical decision support systems (CDSS) aim to mitigate medication-related harm, particularly originating from contraindications. However, many CDSS provide limited benefit, as they focus largely on singular risk situations such as drug-drug interactions and often generate alerts of limited clinical relevance. Comprehensive integration of contraindications into CDSS may support more clinically meaningful alerts. The aim of this work was the development and analysis of machine-readable contraindication lists, including drug-clinical condition, drug-kidney function and drug-drug (group) contraindications, for integration into CDSS and real-world data analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We extracted and operationalized contraindications, based on Summaries of Product Characteristics (SmPCs), of the 688 most prescribed drugs in Germany, leveraging common medical coding systems. Moreover, we analyzed extracted contraindications based on operationalizability, overall frequency and frequencies within different contraindication categories.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In total, we extracted 4676 contraindications, of which 2129 (45.5%) were deemed operationalizable. Of these 2129 contraindications, 1652 (77.6%) were attributed to drug-clinical condition, 83 (3.9%) to drug-kidney, 140 (6.6%) to drug-drug group and 254 (11.9%) to drug-drug. The most frequently mentioned contraindicated risk situations were \u2018severe liver insufficiency\u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200974, 3.5%), \u2018pregnancy\u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200966, 3.1%), \u2018MAO-inhibitors\u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200944, 2.1%), and \u2018shock\u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200944, 2.1%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our results show, that drug-clinical condition contraindications are listed far more frequently in SmPCs than other contraindication categories. Focusing on clinical condition-related contraindications within CDSS could improve the detection of clinically relevant contraindications in routine data and enhance medication safety. The clinical applicability is currently being evaluated in the INTERPOLAR study.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00228-026-04142-3","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T06:03:39Z","timestamp":1784181819000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and analysis of a SmPC-based machine-readable dataset of contraindications for clinical decision support and real-world data 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All other authors declare no conflicts of interest.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Ethics and consent to participate declarations","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"209"}}