The outcome of this research will be a demonstration that family health history (FHH) risk data can be used efficiently to deliver more effective healthcare in geographically and ethnically diverse clinical care environments. Although FHH is a standard component of the medical interview its widespread adoption is hindered by three major barriers: (1) a dearth of standard collection methods;(2) the absence of health care provider access to complete FHH information;and (3) the need for clinical guidance for the interpretation and use of FHH. In addition, the time constraints of the busy provider and poor integration of FHH with paper medical records or electronic medical records (EMR) impede its widespread use. We hypothesize that patient- driven and electronic collection of FHH for risk stratification will promote more informed decision-making by patients and providers, and improves adherence to risk-stratified preventive care guidelines. We will use an implementation sciences approach to integrate an innovative FHH system that collects FHH from patients. Intermountain Healthcare will provide the information technology expertise with EMR design to develop an innovative solution to a storage model standard for FHH data as well as a centralized standards-compliant open clinical decision support (OpenCDS) rule development architecture to analyze FHH and to generate evidence-based, individualized, disease risk, preventive care recommendations for both patients and providers. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essential Health, and the Marshfield Clinic. The study will take place in 'real world'clinical, socio-cultural, and demographically diverse (rural, underserved, academic, family medicine) care clinics (n=34) in 6 states (CA, ID, MN, NC, ND, WI) that include genomic medicine 'early adopter'and 'nave'sites, as well as those that are EMR-enabled and others that are not. Using a cluster randomized controlled pragmatic hybrid type III implementation-effectiveness observational study design, we hypothesize we can demonstrate uptake of the Genomic Medicine Model and its clinical and personal utility. We will recruit a minimum of 7000 English or Spanish speaking adults over a 3-year period and we will capture process metrics and outcomes that are measured in the course of usual care. Our goals are: 1)To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers, 2) to export FHH data to an OpenCDS platform and return CDS results to providers and patients (and to EMRs where relevant) and to explore the integration of genetic risk and FHH data at selected sites, 3) to assess the clinical and personal utility of FHH using a pragmatic observational study design to assess reach, adoption, integrity, exposure, and sustainability, and to capture, analyze, and report effectiveness outcomes at each stakeholder level: patient, provider, and clinic/system, and 4) to take a leadership role in the dissemination of guidelines for FHH intervention across in diverse practice settings.

Public Health Relevance

The proposed study uses pragmatic trial methodology to improve public health by populating a web-based clinical decision support system with patient-entered family health history and electronic health record clinical data to generate personalized preventive care recommendations for cancer, thrombosis, and cardiovascular disease prevention during primary care visits.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZHG1)
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Madden, Ebony B
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Duke University
Schools of Medicine
United States
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Orlando, Lori A; Sperber, Nina R; Voils, Corrine et al. (2017) Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group. Genet Med :
Sperber, Nina R; Carpenter, Janet S; Cavallari, Larisa H et al. (2017) Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network. BMC Med Genomics 10:35
Weitzel, Kristin Wiisanen; Alexander, Madeline; Bernhardt, Barbara A et al. (2016) The IGNITE network: a model for genomic medicine implementation and research. BMC Med Genomics 9:1
Wu, R Ryanne; Myers, Rachel A; McCarty, Catherine A et al. (2015) Protocol for the ""Implementation, adoption, and utility of family history in diverse care settings"" study. Implement Sci 10:163
Orlando, Lori A; Buchanan, Adam H; Hahn, Susan E et al. (2013) Development and validation of a primary care-based family health history and decision support program (MeTree). N C Med J 74:287-96