Quantitative Approaches to Understand Diseases with Electronic Health Record Data
Session Description: Electronic Health Records (EHRs) capture information on a large number of people, including diverse and vulnerable individuals, and they can be used to conduct health studies. We will discuss various examples of how the quantitative disciplines of biostatistics, biomedical informatics and epidemiology have been applied to gain insights into diseases with EHR data.
Session Goals: As a result of this session, participants will be able to
- Introduce electronic health records and their value for research
- Describe the limitations and difficulty associated with using electronic health record data
- Provide examples of biomedical informatics, biostatistics and epidemiologic studies that have used electronic health record data
- Highlight results from past and ongoing studies of asthma, age-related macular degeneration and multiple sclerosis in diverse populations derived from electronic health records
- Inspire attendees to consider training and careers in quantitative health disciplines
Speakers/Chair:
Speaker and Chair: Blanca E. Himes, PhD, Associate Professor of Informatics, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA.
Biomedical Informatics Approaches to Study Asthma Disparities with Electronic Health Records
Speaker: Farren B. S. Briggs, PhD, ScM, Assistant Professor, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH.
Epidemiologic Insights Derived from Electronic Health Record Data
Speaker: Dana C. Crawford, PhD, Professor, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH.
Genetics Studies Enabled by Electronic Health Record Biobanks
Speaker: Rebecca A. Hubbard, PhD, Professor of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA.
Biostatistics Methods to Understand Patterns in Electronic Health Records
