Clinical Research Informatics

Dr. Richesson is a Professor of Informatics at the University of Michigan School of Medicine, Department of Learning Health Sciences. She works with a number of different clinical research networks and pragmatic clinical trials, and supports the development and use of data standards.Dr. Andrews is an Associate Professor of Informatics in the School of Information at the University of South Florida. His scholarship focuses on a issues related to health information behaviors, particularly in the context of genetics, and terminologies in healthcare and research.Ms. Fultz Hollis is an informatician at Oregon Health & Science University in Portland, Oregon USA. She is particularly interested in all aspects of sharing research data accurately and with care for the quality and respect for data use and ownership. She has been extremely active for the past 8 years in editing and evaluating biomedical informatics research forthe International Medical Informatics Association (IMIA Yearbook) and for the AMIA Summit and AMIA Annual conferences.

• Introduction to Clinical Research Informatics.- From Notations to Data: The Digital Transformation of Clinical Research.- Methodological Foundations of Clinical Research.- The Clinical Research Environment.- Biorepositories.- Study Protocol Representation.- Clinical Research Information Systems.- Public Policy Issues in Clinical Research Informatics.- Integrating EHR and Research.- Data Quality in Clinical Research.- Research Data Governance, Roles, and Infrastructure.- Informatics Approaches to Patient Recruitment.- Patient Registries for Clinical Research.- Managing clinical data/research data and phenotyping/cohort discovery.- Patient Reported Outcome Data.- Laboratory Data [New].- Molecular, Genetic and other -omic Data.- Data Sharing: Clinical Trials Registries, Results Databases, and Research Data Repositories.- Knowledge Representation and Ontologies.- Developing and Promoting Data Standards for Clinical Research.- Non-Hypothesis Driven Research: Data mining and knowledge Discovery.- Advancing Clinical Research through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning.- Back to the Future: The Evolution of Pharmacovigilance in the Age of Digital Healthcare.- The Evolving Role of Consumers.- Research in Open Environments.- Research in Context of Learning Health Systems.