Clinical utility of a Web-enabled risk-assessment and clinical decision support program.
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2016-10
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Abstract
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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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 https://hdl.handle.net/10161/11787.
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Rebekah Ryanne Wu
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
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
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
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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