Master of Science in Clinical Informatics: Class of 2019
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.
Applied Clinical Informatics II: Computer Science Fundamentals and Data Analytics; Challenges in Informatics Quality and Safety
In this course students learn database concepts, design, development, implementation, and administration that is specifically targeted towards healthcare environments. Healthcare data integrity, data quality, and data security are emphasized. Management of data structure and content for compliance with standards, regulations (including HIPAA and HITECH), and accrediting agencies are detailed. Students examine strategies and technologies for data storage, controlling access, protecting confidentiality, archiving and backing up, and restoring massive amounts of healthcare data. This course provides students the opportunity to analyze the various types of healthcare data and explore the challenges related to modeling, collecting, using and analyzing each main type of healthcare data. This course explores different strategies for representing data, information and knowledge, including required and emerging standards for coding, nomenclature, and their associated taxonomies and ontologies. It also examines how these standards are used to create tools for mining, analyzing, interpreting and sharing information for a variety of clinical and administrative purposes throughout the healthcare system.
Applied Clinical Informatics II: Computer Science Fundamentals and Data Analytics; Challenges in Informatics Quality and Safety.
In this course students learn advanced health data science concepts necessary to design, develop, implement and administer new technologies specifically targeted towards improving healthcare outcomes. Healthcare data integrity, data quality, and data portability are emphasized. Management of data structure and content for compliance with existing and emerging standards for interoperability and big data set creation, are highlighted. Students examine strategies and technologies for data storage, data exchange, data normalization, and data re-use for research, quality improvement and patient care. This course explores different strategies for representing data, information and knowledge, including required and emerging standards for coding and nomenclature, and their associated taxonomies and ontologies. It also examines how these standards are used in tools for mining, analyzing, interpreting and sharing information for a variety of clinical and administrative purposes throughout the healthcare system. This course provides students the opportunity to explore state of the art health care technologies such as electronic phenotyping, cloud-based real-time clinical decision support, natural language processing using artificial intelligence, machine learning tools used to analyze large data sets in distributed research networks.
Applied Clinical Informatics IV: Computer Information System Implementation and Planning; Capstone project
This course provides the student an opportunity to apply the principles, concepts and skills acquired in the Master of Science in Applied Clinical Informatics to a specific topic or issue they wish to examine. The capstone project can be a research project studying a specific topic in clinical informatics or participation in a health care organization (HCO) project designed to develop. implement or improve a clinical informatics process or technology. The project must reflect a synthesis of skills and knowledge from core course work, but at the same time represent a practical application which can be completed in a one semester time frame. Students will identify and define the nature and scope of the capstone project in consultation with department faculty and HCO management. Projects are jointly approved by HCO management and the faculty of the BMI department.