Protocol for implementation of family health history collection and decision support into primary care using a computerized family health history system.
Repository Usage Stats
BACKGROUND: The CDC's Family History Public Health Initiative encourages adoption and increase awareness of family health history. To meet these goals and develop a personalized medicine implementation science research agenda, the Genomedical Connection is using an implementation research (T3 research) framework to develop and integrate a self-administered computerized family history system with built-in decision support into 2 primary care clinics in North Carolina. METHODS/DESIGN: The family health history system collects a three generation family history on 48 conditions and provides decision support (pedigree and tabular family history, provider recommendation report and patient summary report) for 4 pilot conditions: breast cancer, ovarian cancer, colon cancer, and thrombosis. All adult English-speaking, non-adopted, patients scheduled for well-visits are invited to complete the family health system prior to their appointment. Decision support documents are entered into the medical record and available to provider's prior to the appointment. In order to optimize integration, components were piloted by stakeholders prior to and during implementation. Primary outcomes are change in appropriate testing for hereditary thrombophilia and screening for breast cancer, colon cancer, and ovarian cancer one year after study enrollment. Secondary outcomes include implementation measures related to the benefits and burdens of the family health system and its impact on clinic workflow, patients' risk perception, and intention to change health related behaviors. Outcomes are assessed through chart review, patient surveys at baseline and follow-up, and provider surveys. Clinical validity of the decision support is calculated by comparing its recommendations to those made by a genetic counselor reviewing the same pedigree; and clinical utility is demonstrated through reclassification rates and changes in appropriate screening (the primary outcome). DISCUSSION: This study integrates a computerized family health history system within the context of a routine well-visit appointment to overcome many of the existing barriers to collection and use of family history information by primary care providers. Results of the implementation process, its acceptability to patients and providers, modifications necessary to optimize the system, and impact on clinical care can serve to guide future implementation projects for both family history and other tools of personalized medicine, such as health risk assessments.
Ambulatory Care Facilities
Decision Making, Computer-Assisted
Electronic Health Records
Medical History Taking
Medical Records Systems, Computerized
Primary Health Care
Total Quality Management
Published Version (Please cite this version)10.1186/1472-6963-11-264
Publication InfoOrlando, Lori Ann; Hauser, Elizabeth Rebecca; Christianson, C; Powell, KP; Buchanan, AH; Chesnut, Blair; ... Ginsburg, Geoffrey Steven (2011). Protocol for implementation of family health history collection and decision support into primary care using a computerized family health history system. BMC Health Serv Res, 11. pp. 264. 10.1186/1472-6963-11-264. Retrieved from http://hdl.handle.net/10161/13544.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
More InfoShow full item record
Professor 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.
Professor of Biostatistics and Bioinformatics
My research interests are focused on developing and applying statistical methods to search for genes causing common human diseases. Recent work has been in the development of statistical methods for genetic studies and in identifying optimal study designs for genetic studies of complex traits. As application of these methods to specific diseases has progressed it has become apparent that etiologic and genetic heterogeneity is a major stumbling block in the research for genes for common diseases.
Associate Professor of Medicine
Dr. Lori A. Orlando, MD MHS is an Associate 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 fo
Alphabetical list of authors with Scholars@Duke profiles.
Showing items related by title, author, creator, and subject.
Stoertz, Aaron (2011)Health system strengthening is now recognized as a pressing global health priority. Motivated and productive health workers are a critical component of health systems. Low and middle-income countries need many more health ...
Optimizing linkage and retention to hypertension care in rural Kenya (LARK hypertension study): study protocol for a randomized controlled trial. Akwanalo, CO; Binanay, CA; Bloomfield, Gerald; Delong, AK; Finkelstein, Eric Andrew; Fuster, V; Hogan, JW; ... (16 authors) (Trials, 2014-04-27)BACKGROUND: Hypertension is the leading global risk factor for mortality. Hypertension treatment and control rates are low worldwide, and delays in seeking care are associated with increased mortality. Thus, a critical component ...
Economic evaluation of access to musculoskeletal care: the case of waiting for total knee arthroplasty. Bolognesi, Michael Paul; Hug, KT; Koenig, L; Mather, Richard Charles III; Nunley, RM; Orlando, Lori Ann; Watters, TS (BMC Musculoskelet Disord, 2014-01-18)BACKGROUND: The projected demand for total knee arthroplasty is staggering. At its root, the solution involves increasing supply or decreasing demand. Other developed nations have used rationing and wait times to distribute ...