iMpact Seminar February 7, 2023
Professor of Learning Health Sciences, University of Michigan Medical School
Computable Phenotyping in Pragmatic Clinical Trials and Learning Health Systems
Learning Health Systems thrive with use of real-world data from electronic health record (EHR) systems in both observational and interventional research to generate real-world evidence. Computable phenotypes are specified definitions that can be used to identify patients with particular clinical conditions through computerized queries to EHR systems or data repositories using defined data elements, codes, and logical expressions. Computable phenotypes can facilitate research and learning by supporting the identification of patient populations, the delivery of clinical interventions, and the assessment of outcomes. The sharing and re-use of computable phenotypes can enhance the efficiency of pragmatic research and the dissemination of evidence-based interventions into real-world settings. This talk will discuss current platforms for identifying existing computational phenotypes as well as challenges and strategies for their implementation and validation in learning health systems.
Rachel Richesson is a Professor in the Department of Learning Health Sciences, School of Medicine, University of Michigan. She holds a PhD and MS in Health Informatics and a Masters of Public Health from the University of Texas.