Applied Clinical Informatics I: Fundamentals of Biomedical Informatics; Data Acquisition and Management; Clinical Decision Support.

This course presents an overview of biomedical informatics theories, methods, and techniques. The main features of each division of the field of biomedical informatics (bioinformatics, translational informatics, imaging informatics, clinical informatics and public health informatics) are described and analyzed. Social, economic, ethical, cultural, environmental, historical, and other factors driving the development and implementation of clinical informatics are described and discussed. The student is then introduced to important structural and technical concepts of health care data. Students get hands-on experience on how to analyze a healthcare problem and model its data effectively using appropriate work flow and data modeling techniques. The third major component of this course covers clinical decision support as a technology-mediated process by which patient information and characteristics are captured, matched to an algorithm, and used to guide patient care. Students learn the basic principles and advanced concepts of clinical decision support, benefits as well as the drawbacks of these systems, and how these are used support the practice of evidence based medicine. Important design principles such as signal-to-noise ratios, alert fatigue, and usability are also covered.