1. Digitalisierung, Evidenzbasierte Medizin, Prävention und Forschungskompetenz: Die Rolle der Medical Data Sciences im neuen Medizin-Curriculum
- Author
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Timmer, Antje, Weberschock, Tobias, Rothenbacher, Dietrich, Varghese, Julian, Berger, Ursula, Schlattmann, Peter, Dugas, Martin, Kopp-Schneider, Annette, Winter, Alfred, and Binder, Harald
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medical curriculum ,epidemiology ,biometry ,medical informatics ,education ,recommendations ,evidence-based medicine ,science education ,digitalization ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Infectious and parasitic diseases ,RC109-216 - Abstract
Medical data sciences receive attention as digitalization and artificial intelligence (AI) pose new challenges and opportunities in health care. Specific methods and skills derived from the data sciences have been driving essential developments in almost if not all areas of health care and medical research over many years. For example, evidence-based medicine is now a pillar of medical practice, combined with a stronger focus on reproducible and valid research and an improved understanding of scientific methods. The increased role of the data sciences in medical research and practice is reflected in the revised national competency-based learning objectives catalog for medicine (NKLM 2.0). Digitalization, research skills, evidence medicine and health promotion and prevention are integral parts of the curriculum from start to end. They relate to all subjects and topics in an interprofessional manner. This increase in relevance of the data sciences clearly calls for improved competencies in the clinico-theoretical disciplines previously combined as interdisciplinary domain 1 (Q1). Epidemiology, medical biometry and medical informatics will now contribute expertise throughout the whole course of studying medicine. These disciplines deal with digitalization, medical research competence, evidence-based medicine, and prevention. In addition, disease-related learning and many aspects of therapy, diagnostics, communication, and management benefit from cooperation with dedicated instruction in the medical data sciences by didactically trained experts. This article aims to support faculties and subject representatives during the implementation period of the NKLM 2.0 and beyond regarding data science skills. Tables provide an overview of essential learning objectives in epidemiology, biometry, and medical informatics across the different phases of the curriculum. In addition, we give recommendations for cooperation with other subject representatives. By this we wish to contribute to improving the medical curriculum based on graduate profile-oriented interdisciplinary-integrative teaching.
- Published
- 2022
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