There are challenges to using clinical decision support to effectively communicate genomic data to clinicians. Our long-term goal is to understand the effect of using a clinical decision support system (CDSS) to facilitate effectively communicating genomic data on physician confidence to make informed health decisions. The specific objective of this proposal is to develop and evaluate the CDSS. Our approach will engage approximately 10 health IT champions who are members of the University of Maryland Medical System Clinical Decision Support Committee and 50 stakeholders including clinical research coordinators, endocrinologists, interventional cardiologists, diabetes educators, nurse practitioners, medical assistants, administrative staff, and other clinical professionals participating in two exemplar personalized medicine (PM) programs at the University of Maryland. Exemplar programs are the Personalized Diabetes Medicine Program that aims to identify and diagnose patients with highly penetrant genetic forms of diabetes and provide customized treatment, and the implementation arm of the Pharmacogenomics of Anti-Platelet Intervention Study, that aims to translate CYP2C19 genotype results into actionable antiplatelet prescribing decisions. The central hypothesis is that stakeholders involved in these PM programs will have better attitudes about our ability to effectively communicate genomic data to physicians after deploying the CDSS, than before. We are qualified to develop a CDSS for effectively communicating genomic data given our track record in developing genomic CDS. We are also uniquely situated to conduct this research given our experience studying genomic CDS with physicians and our ability to explore the CDSS in the context of two ongoing exemplar PM implementation programs. We will complete the following aims to test our hypothesis:
Aim 1 we will employ user-centered approaches to design a prototype CDSS;
Aim 2 we will formalize genomic knowledge for integration into the electronic health record (EHR);
and Aim 3 we will integrate the finalized CDSS with the EHR. We will use a pre-/post- study design to assess the attitudes of health IT champions and stakeholders involved in exemplar PM programs. At the start and conclusion of the project, we will measure attitudes using a survey instrument informed by Normalization Process Theory that focuses on the work individuals and groups have to do for a new technology or practice to become embedded and sustained in routine practice. Major contributions of this work will be: (a) a prototype CDSS designed to effectively communicate genomic data to physicians;(b) executable genomic CDS knowledge and configured resources for use within our prototype CDSS;and (c) an answer for our central hypothesis that provides insight into stakeholder attitudes about our ability to effectively communicate genomic data to physicians after deploying the CDSS. This work is a key research area of interest of AHRQ, specifically, using health IT to improve healthcare decision-making through use of integrated data and knowledge management.

Public Health Relevance

Being able to customize healthcare based on each person's unique genetic makeup could enable an era of personalized medicine that would improve prevention, diagnosis and treatment for many types of health conditions. Achieving this vision requires developing computerized tools that help doctors and patients make sense of individual genomic data to show them how to use that data to make better healthcare decisions. This project will develop and test such a clinical decision support system.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Exploratory/Developmental Grants (R21)
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Health Care Technology and Decision Science (HTDS)
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Randhawa, Gurvaneet
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University of Maryland Baltimore
Internal Medicine/Medicine
Schools of Medicine
United States
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Cutting, Elizabeth; Banchero, Meghan; Beitelshees, Amber L et al. (2016) User-centered design of multi-gene sequencing panel reports for clinicians. J Biomed Inform 63:1-10
Cutting, Elizabeth M; Overby, Casey L; Banchero, Meghan et al. (2015) Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project. AMIA Annu Symp Proc 2015:466-74