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Casey Lynnette Overby, Ph.D.

Photo of Dr. Casey Lynnette Overby, Ph.D.

Assistant Professor of Medicine

Research Interests: public health genomics and data science; knowledge-based methods; comparative effectiveness research with electronic health record data; clinical and shared decision support for precision medicine applications ...read more

Background

Dr. Casey Overby is Assistant Professor in the Division of General Internal Medicine with a joint position in the Division Health Sciences Informatics in the Department of Medicine at the Johns Hopkins University. She completed pre-doctoral National Library of Medicine biomedical informatics training and National Human Genome Research Institute genome sciences training fellowships at the University of Washington. She also completed a post-doctoral National Library of Medicine informatics training fellowship at Columbia University. As an informatics researcher, Dr. Overby's interests intersect at public health genomics and biomedical informatics. She is currently developing applications that support translation of genomic research to clinical and population-based healthcare settings and delivering health information and knowledge to the public. Dr. Overby is also developing knowledge-based approaches to use Big Data such as electronic health record data for population health. 

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Titles

  • Assistant Professor of Medicine

Departments / Divisions

Education

Degrees

  • B.S., University of Michigan (Michigan) (2004)
  • Master of Biotechnology, University of Pennsylvania (Pennsylvania) (2006)
  • Ph.D., University of Washington School of Medicine - Seattle (Washington) (2011)

Research & Publications

Research Summary

As an informatics researcher, Dr. Overby's interests intersect at public health genomics and biomedical informatics. She is currently developing applications that support translation of genomic research to clinical and population-based health care settings and delivering health information and knowledge to the public. Dr. Overby is also developing knowledge-based approaches to use big data such as electronic health record data for population health.

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