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Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project.

Overview of attention for article published in AMIA Annual Symposium Proceedings, November 2015
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Title
Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project.
Published in
AMIA Annual Symposium Proceedings, November 2015
Pubmed ID
Authors

Elizabeth M Cutting, Casey L Overby, Meghan Banchero, Toni Pollin, Mark Kelemen, Alan R Shuldiner, Amber L Beitelshees

Abstract

Delivering genetic test results to clinicians is a complex process. It involves many actors and multiple steps, requiring all of these to work together in order to create an optimal course of treatment for the patient. We used information gained from focus groups in order to illustrate the current process of delivering genetic test results to clinicians. We propose a business process model and notation (BPMN) representation of this process for a Translational Pharmacogenomics Project being implemented at the University of Maryland Medical Center, so that personalized medicine program implementers can identify areas to improve genetic testing processes. We found that the current process could be improved to reduce input errors, better inform and notify clinicians about the implications of certain genetic tests, and make results more easily understood. We demonstrate our use of BPMN to improve this important clinical process for CYP2C19 genetic testing in patients undergoing invasive treatment of coronary heart disease.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 17%
Researcher 4 17%
Student > Master 4 17%
Student > Bachelor 3 13%
Other 2 9%
Other 2 9%
Unknown 4 17%
Readers by discipline Count As %
Computer Science 6 26%
Engineering 3 13%
Biochemistry, Genetics and Molecular Biology 2 9%
Medicine and Dentistry 2 9%
Business, Management and Accounting 1 4%
Other 5 22%
Unknown 4 17%