Clinical utility of a Web-enabled risk-assessment and clinical decision support program.


PURPOSE: Risk-stratified guidelines can improve quality of care and cost-effectiveness, but their uptake in primary care has been limited. MeTree, a Web-based, patient-facing risk-assessment and clinical decision support tool, is designed to facilitate uptake of risk-stratified guidelines. METHODS: A hybrid implementation-effectiveness trial of three clinics (two intervention, one control). PARTICIPANTS: consentable nonadopted adults with upcoming appointments. PRIMARY OUTCOME: agreement between patient risk level and risk management for those meeting evidence-based criteria for increased-risk risk-management strategies (increased risk) and those who do not (average risk) before MeTree and after. MEASURES: chart abstraction was used to identify risk management related to colon, breast, and ovarian cancer, hereditary cancer, and thrombosis. RESULTS: Participants = 488, female = 284 (58.2%), white = 411 (85.7%), mean age = 58.7 (SD = 12.3). Agreement between risk management and risk level for all conditions for each participant, except for colon cancer, which was limited to those <50 years of age, was (i) 1.1% (N = 2/174) for the increased-risk group before MeTree and 16.1% (N = 28/174) after and (ii) 99.2% (N = 2,125/2,142) for the average-risk group before MeTree and 99.5% (N = 2,131/2,142) after. Of those receiving increased-risk risk-management strategies at baseline, 10.5% (N = 2/19) met criteria for increased risk. After MeTree, 80.7% (N = 46/57) met criteria. CONCLUSION: MeTree integration into primary care can improve uptake of risk-stratified guidelines and potentially reduce "overuse" and "underuse" of increased-risk services.Genet Med 18 10, 1020-1028.






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Publication Info

Orlando, Lori A, R Ryanne Wu, Rachel A Myers, Adam H Buchanan, Vincent C Henrich, Elizabeth R Hauser and Geoffrey S Ginsburg (2016). Clinical utility of a Web-enabled risk-assessment and clinical decision support program. Genet Med, 18(10). pp. 1020–1028. 10.1038/gim.2015.210 Retrieved from

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Lori Ann Orlando

Professor of Medicine

Dr. Lori A. Orlando, MD MHS MMCI is a Professor of Medicine and Director of the Precision Medicine Program in the Center for Applied Genomics and Precision Medicine at Duke University. She attended Tulane Medical Center for both medical school (1994-1998) and Internal Medicine residency (1998-2000). There she finished AOA and received a number of awards for teaching and clinical care from the medical school and the residency programs, including the Musser-Burch-Puschett award in 2000 for academic excellence. After completing her residency, she served as Chief Medical Resident in Internal Medicine (2001) and then completed a Health Services Research Fellowship at Duke University Medical Center (2002-2004). In 2004 she also received her MHS from the Clinical Research Training Program at Duke University and joined the academic faculty at Duke. In 2005 she received the Milton W. Hamolsky Award for Outstanding Junior Faculty by the Society of General Internal Medicine. Her major research interests are decision making and patient preferences, implementation research, risk stratification for targeting preventive health services, and decision modeling. From 2004-2009 she worked with Dr. David Matchar in the Center for Clinical Heath Policy Research (CCHPR), where she specialized in decision modeling, decision making, and technology assessments. In 2009 she began working with Dr. Geoffrey Ginsburg in what is now the Center for Applied Genomics and Precision Medicine (CAGPM) and in 2014 she became the director of the Center’s Precision Medicine Program. Since joining the CAGPM she has been leading the development and implementation of MeTree, a patient-facing family health history based risk assessment and clinical decision support program designed to facilitate the uptake of risk stratified evidence-based guidelines. MeTree was designed to overcome the major barriers to collecting and using high quality family health histories to guide clinical care and has been shown to be highly effective when integrated into primary care practices. This effort started with the Genomic Medicine Model, a multi-institutional project, whose goal was to implement personalized medicine in primary care practices. The success of that project has led to funding as part of NHGRI’s IGNITE (Implementing Genomics in Clinical Practice) network. She is currently testing methods for integrating patient preferences and decision making processes into clinical decision support recommendations for patients and providers to facilitate management of patients’ risk for chronic disease using mHealth and other behavioral interventions.


Rebekah Ryanne Wu

Adjunct Associate Professor in the Department of Medicine

Dr. Wu is an internal medicine physician and health services researcher. Her main research interest is studying the implementation of precision medicine applications to improve clinical care. She is involved in projects currently looking at a patient-facing family history risk assessment tool, MeTree, which provides individualized risk stratification and clinical decision support recommendations to clinicians and patients. In addition she is also involved in a large scale sequencing program in Singapore looking at the intersection of family health history and genomics to better understand how these data elements can complement one another and create more precise risk predictions.  She is a member of NHGRI's IGNITE network as a co-investigator on a multi-site pragmatic clinical trial of the impact of pharmacogenetic testing on management of depression and acute, and chronic pain.  She is the implementation science advisor for the VA's Pharmacogenomic Testing for Veterans (PHASER) program, which is working to complete preemptive PGx testing on up to 250,000 Veterans by 2024.


Rachel Myers

Research Scientist, Senior

I am a bioinformatician cross trained as biostatistician and research scientist with the Department of Medicine Clinical Research Unit. In this role, I manage the Bioinformatics and Clinical Analytics Team, a team of bioinformaticians, biostatisticians, and data scientists in supporting the data and quantitative research needs of the Department of Medicine. 

I am interested in genomic translational research and enjoy studying all aspects of genomic translation, from the discovery of new signatures and biomarkers of drug or infection exposure through the implementation of genomics interventions in the clinical setting. I enjoy the complex analysis of "Omic datasets and generating new knowledge. My favorite part of the analysis is when all the clinical and 'omic datasets come together and I can start exploring the data. On the other end of the spectrum, I enjoy watching data accumulate for a clinical trial and preparing for testing the primary and secondary endpoints as well as designing new ways to use the data. 


Elizabeth Rebecca Hauser

Professor of Biostatistics & Bioinformatics

The incorporation of personalized medicine to all areas of human health represents a turning point for human genetics studies, a point at which the discoveries made have real implications for clinical medicine.  It is important for students to gain experience in how human genetics studies are conducted and how results of those studies may be used.  As a statistical geneticist and biostatistician my research interests are focused on developing and applying statistical methods to search for genes causing common human diseases.  My research programs combine development and application of statistical methods for genetic studies, with a particular emphasis on understanding the joint effects of genes and environment. 

These studies I work on cover diverse areas in biomedicine but are always collaborative, with the goal of bringing robust data science and statistical methods to the project.  Collaborative studies include genetic and ‘omics studies of cardiovascular disease, health effects of air pollution, genetic analysis of adherence to an exercise program, genetic analysis in evaluating colon cancer risk, genetic analysis of suicide, and systems biology analysis of Gulf War Illness.

Keywords: human genetics, genetic association, gene mapping, genetic epidemiology, statistical genetics, biostatistics, cardiovascular disease, computational biology, diabetes, aging, colon cancer, colon polyps, kidney disease, Gulf War Illness, exercise behavior, suicide


Geoffrey Steven Ginsburg

Adjunct Professor in the Department of Medicine

Dr. Geoffrey S. Ginsburg's research interests are in the development of novel paradigms for developing and translating genomic information into medical practice and the integration of personalized medicine into health care.

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