The Department of Medical Informatics and Clinical Epidemiology (DMICE) is one of 27 academic departments in the School of Medicine at Oregon Health & Science University (OHSU). The DMICE programs are recognized internationally for their accomplishment and innovation.
The Biomedical Informatics program welcomes prospective students interested in our graduate program to learn more about our specialized tracks in clinical informatics, and bioinformatics & computational biology.
The Health Informatics accreditor of OHSU's Biomedical Informatics Graduate Program is the Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM). The accreditation for masters degree in health informatics has been reaffirmed through 2023. All inquiries about the program’s accreditation status should be directed by mail to CAHIIM, 200 East Randolph Street, Suite 5100, Chicago, IL, 60601; by phone at (312) 235-3255; or by email at email@example.com.
Department of Medical Informatics & Clinical Epidemiology leadership transition
William (Bill) Hersh, M.D., (picture above on left) professor of medical informatics & clinical epidemiology, is stepping down as chair of the Department of Medical Informatics & Clinical Epidemiology (DMICE), OHSU School of Medicine, after more than 25 years of leadership. Dean David Jacoby has appointed Karen Eden, Ph.D., professor of medical informatics & clinical epidemiology (picture above on right) as interim chair, effective Jan. 2.
College Undergraduate Biomedical Informatics and Data Science Internship Program
The Department of Medical Informatics and Clinical Epidemiology (DMICE) will be hosting its College Undergraduate Biomedical Informatics and Data Science Internship Program again in the summer of 2023.
The application period for Summer 2023 will be until February 28, 2023. All applicants will be notified final decisions by April 28, 2023.
Annual Update in Clinical Informatics
Information and registration available now for short course (the 3rd Annual Update in Clinical Informatics). The course closes February 2023:
DMICE Informatics Conference
Please contact Lynne Schwabe by email for WebEx links.
Thursday, January 19, 2023
11:30 am to 12:30 pm (Pacific)
This event will be virtual only – see webex information above.
Please join DMICE for a presentation by:
“Optimizing Gender Inclusive Care through the Electronic Health Record”
This presentation will provide an overview of gender diversity and gender inclusive care. Particular attention will be provided to the electronic health record and how it intersects with patient experience and clinical care delivery. Initiatives and improvements within the electronic health record over the last decade will be explored while also naming the challenges that remain to sustain progress. This will be an informal presentation that encourages questions and discussion along the way.
Christina Milano, MD (she/they)
Associate Professor of Family Medicine, School of Medicine
Oregon Health & Sciences University
Dr. Milano is an associate professor of Family Medicine and Medical Director of the OHSU Transgender Health Program.
Amy Penkin, LCSW (she/her)
Clinical Program Manager
Transgender Health Program
Oregon Health & Sciences University
Amy Penkin is a licensed clinical social worker who has overseen the clinical and operational activities of the Transgender Health Program since its inception in 2015.
Thursday, January 26, 2023
11:30 am to 12:30 pm
In-Person: BICC 124(Theater)
Virtual Attendance offered via Webex (info above)
Nicole G. Weiskopf, PhD
Associate Director, Health and Clinical Informatics Program
Health and Clinical Informatics
This presentation will be based on a paper recently accepted by JAMIA.
Title: “Healthcare Utilization is a Collider: An introduction to collider bias in EHR data reuse”
Authors: Nicole G. Weiskopf, David A. Dorr, Christie Jackson, Harold Lehmann, Caroline A. Thompson
Collider bias is a common threat to internal validity in clinical research, but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. This talk will focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data.