Implementation, Adoption, and Utility of Family History in Diverse Care Settings

The purpose of this study is to address the key question of whether and how family health history (FHH) is adopted as a tool to more efficiently manage patients at risk for breast, colon, ovarian, and hereditary cancer syndromes as well as thrombophilia and coronary heart disease (CHD) and to provide evidence supporting clinical utility -- improved health behaviors in patients and physician screening recommendations. Five health care delivery organizations will participate in this demonstration project: Duke University, the Medical College of Wisconsin, the Air Force, Essentia Health, University of North Texas. Duke will serve as a coordinating center for this project (Pro00043372) as well as a site. Healthcare Effectiveness Data and Information Set (HEDIS) measures as intermediate clinical effectiveness measures for Coronary Heart Disease (CHD) and selected cancers as well as survey/formative data and electronic medical record (EMR) data will be used as outcomes measures. The research model is purposely designed to mimic clinical delivery as an important step toward widespread implementation and sustainability. In addition, a cost-effectiveness analysis comparing usual care to the FHH guided preventive health model will be used. The completion of this project will result in an optimal strategy for integration of FHH data collection and clinical decision support (CDS) tools into an EMR and demonstrate the utility of the FHH intervention among diverse primary care patients, their settings, their providers, and the health systems that deliver their care. Specific Aim 1: To optimize the collection of patient entered FHH in diverse clinical environments for coronary heart disease, thrombosis, and selected cancers Specific Aim 2: To export FHH data to an open clinical decision support (open CDS) platform and return CDS results to providers and patients (and to EMRs where relevant). To explore the integration of genetic risk and FHH data at selected sites. Specific Aim 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. Specific Aim 4: To take a leadership role in the dissemination of guidelines for a FHH intervention across in diverse practice settings. AAA Hypertension Lupus Multiple sclerosis Obesity Osteoporosis Thyroid disease Rheumatoid arthritis Blood clotting (6): Blood Clot (high risk features) Factor V PT mutation AT III deficiency Protein S deficiency Protein C deficiency Brain Disorders (5): Dementia Hemorrhagic stroke Ischemic stroke Macrocephaly Seizure Cancer/Adenomas (32): Bone cancer Brain cancer Breast cancer Colon cancer Adrenal cortex tumor Neuroendocrine tumor Paraganglioma Pheochromoctyoma Pituitary adenoma Medullary thyroid cancer Non-medullary (follicular or papillary) thyroid cancer Don't know type of thyroid cancer Thyroid nodule Other type of endocrine cancer Esophageal cancer Kidney cancer Leukemia Lipoma Liver cancer Muscle cancer Ovarian cancer Pancreatic cancer Prostate cancer Rectal cancer Retinoblastoma Melanoma Non-melanoma skin cancer Do not know type of skin cancer Small bowel cancer Stomach cancer Uterine cancer Other type of cancer Cardiovascular/heart/artery disease (5): Atrial fibrillation Carotid stenosis Heart attack/coronary artery disease Peripheral arterial disease Other heart disease Diabetes (3): Gestational diabetes Diabetes type I Diabetes type 2 Digestive Disorders (5): Colon polyp Crohn's disease Irritable bowel syndrome Ulcerative colitis Other digestive disorder Eye Disorder (3): Blindness Glaucoma Macular degeneration Hereditary Cancer Syndromes (22): Birt-Hogg-Dube syndrome Cowden syndrome Familial adenomatous polyposis Hereditary breast and ovarian cancer syndrome Hereditary diffuse gastric cancer Hereditary leiomyomatosis and renal cell carcinoma syndrome Hereditary melenoma Hereditary papillary renal cancer syndrome Hereditary paraganglioma-pheochromocytoma syndrome Hereditary retinoblastoma Juvenile polyposis Li-Fraumeni syndrome Lynch syndrome Mutyh-associated polyposis Malignant hyperthermia susceptibility Multiple endocrine neoplasia type 1 Multiple endocrine neoplasia type 2 Nevoid basal cell carcinoma syndrome Peutz-Jeghers syndrome Tuberous Sclerosis complex Von-Hippel Lindau syndrome Other hereditary cancer Hereditary Cardiovascular syndromes (10): Long qt Brugada Catecholaminergic polymorphic ventricular tachycardia Hypertrophic cardiomyopathy Dilated cardiomyopathy Left ventricular non-compaction syndrome Arrhythmogenic right ventricular dysplasia Ehlers-Danlos syndrome Marfan syndrome Other hereditary cardiovascular syndrome High cholesterol (2): Hyperlipidemia Familial hypercholesterolemia Kidney Disease (6): Cystic kidney disease Diabetic kidney disease Kidney nephrosis Nephritis Nephrotic syndrome Other kidney disease Liver Disease (6): Alpha 1 antitrypsinase deficiency Auto-immune hepatitis Hereditary hemochromatosis Primary biliary cirrhosis Sclerosing cholangitis Wilson's disease Lung Disease (3): Asthma COPD/chronic bronchitis/emphysema Other lung disease Psychological disorder (14): Addiction ADHD Autism Bipolar Depression Eating disorder Intellectual disability Obsessive compulsive disorder Panic disorder Personality disorder PTSD Schizophrenia Social phobia Sickle cell/thalassemia (3): Sickle cell disease Sickle cell trait Thalassemia ]]>
Participant 3 Month SurveyParticipant 12 Month SurveyParticipant Baseline SurveyCollecting Family Health HistoryEducation Worksheet MeTreeConsent to Participate in a Research StudyPopulation: Study subjects will consist of providers and patients at several diverse primary care clinics at Duke University, Medical College of Wisconsin, Essentia Institute of Rural Health, and University of North Texas Health Science Center. Providers: All providers in the participating clinics will be selected to be included in the study. ORCA survey participants will be providers, nurses, and administrators at the participating clinics which are involved in MeTree implementation. Key informants will be providers, nurses, and schedulers at each participating clinic which is involved in MeTree implementation. Patients: All adult English or Spanish speaking patients scheduled for non-acute visits within 3 weeks will be invited to enroll when they schedule an appointment with their physician. We will also accept physician referred participants. Since this proposal focuses upon prevention and not disease management strategies, those with a study disease (breast, or colon cancer, hereditary cancer syndromes, or CHD) will not be excluded from enrollment but will be excluded from analyses relevant to their disease. We anticipate enrolling 1000 participants at a minimum (to achieve significance for effectiveness measures), but as an observational study will continue to enroll as many as are interested in order to maximize our ability to assess differences across settings, populations, and socio-demographic factors. To reach this goal we need to enroll ~200 participants from each intervention clinic (assumes 2 of the 7 clinics currently selected will be dedicated controls). Assuming 20% enrollment (from MeTree™ pilot -Pro00018641), we anticipate being able to enroll at least 1400 participants. All documents (consent forms, educational brochures, surveys, etc) will be translated as appropriate to Spanish-language version. The MeTree software program user interface will also be translated into Spanish with a choice for the participant to have it displayed in English or Spanish.]]>
In 2002 the CDC launched the Family History Public Health Initiative, founded upon the principle that family history is an underutilized but effective tool for risk stratification. Among the stated goals were to develop tools to enhance family health history (FHH) collection and to evaluate whether FHH-based strategies work in practice. Because primary care providers account for the majority of care encounters in the US they are a natural choice as partners to study the implementation of FHH into care delivery and medical decision-making. FHH assessments have clearly been shown to identify persons at higher risk for common chronic disease, enabling preemptive and preventive steps, including lifestyle changes, health screenings, testing, and early treatment as appropriate(1). More recently Qureshi has shown prospectively the potential to identify presymptomatic individuals at elevated risk for common, chronic diseases and activate them to modify their risks(2) - an enormous opportunity to improve public health by implementing risk-based screening and prevention strategies. Yet, although FHH is a standard component of the medical interview and professional guidelines recommend screening strategies based upon FHH, its widespread adoption is hindered by three major barriers: (1) standard collection methods; (2) health care provider access to FHH information; and (3) clinical guidance for interpretation and use of FHH. The Rationale for Using FHH Tools. FHH is underutilized by practitioners and therefore represents a significant missed opportunity for risk stratification(3): a systematic review found a 46-78% improvement in data recording by FHH tools as compared with the use of standard practice(4). FHH tools show excellent concordance with structured pedigree interviews and the gold standard three-generation pedigree(5). In a study of 1124 primary care patients not only was medical record documentation insufficient in two-thirds of charts for FHH assessment of six common diseases, but also 23% had no evidence of risk in their medical record yet had a moderate or strong risk for at least one disease as assessed by the Family Healthware™ tool(6). FHH collection, analysis, and risk stratification can be performed efficiently and effectively using a variety of software platforms that have the potential to overcome the barriers created by a reliance on physicians to gather, record, and analyze FHH. Implementation of automated FHH linked to clinical decision support (CDS) is feasible in the community setting as shown by use of HughesRiskApps in over 25,000 individuals, leading to referral of 3.6% of patients for breast and ovarian cancer genetic counseling and consideration of genetic testing(7). In our own experience using the MeTree™ FHH tool, the mean completion time by 1320 primary care patients was 23 minutes, and 35% were classified as having strong or moderate risk for at least one of five common diseases. It is absolutely clear that to elicit information for a comprehensive FHH is a significant time commitment making it clear that patients, not physicians, need to serve as the main locus for data input. Electronic Medical Records and FHH. The American Health Information Community (AHIC) Personalized Health Care (PHC) Workgroup, part of the U.S. Department of Health and Human Services (HHS) Personalized Health Care Initiative, has put significant effort into developing standards for incorporating FHH into the electronic medical record (EMR). However, there are important roadblocks to realizing the full potential of FHH: for example, for the ~150 EMR vendors, FHH information is primarily recorded as free text and no EMRs have graphical pedigree drawing functions. In addition, among the few structured data sets, none are compliant with the AHIC core data set standard(8). Highlighting the fact that EMRs do not provide a solution to FHH capture, in query of data from the EPIC EMR at Medical College of Wisconsin in ~ 721,000 patient encounters 85% lacked a FHH, and only 1% of records had recorded three generation data (Dimmock D, personal communication). Stand alone software packages may provide the needed functionalities such as pedigree-drawing, and algorithms but none of these are interoperable with EMRs and EMR vendors avoid linking one-off programs to their own packages. A key point is that CDS (see below) capabilities remain limited in most EMRs and are virtually non-existent for FHH. In the current proposal we will use a modular approach whereby FHH collection is centralized, key data elements are exported to a program that applies specific algorithms, and the data are then returned to the EMR where CDS strategies can be applied(8). Clinical Decision Support. CDS is a critical prerequisite to realizing the full potential of the EMR to facilitate evidence-based medicine(9). HHS, the CDC, and the Secretary's Advisory Committee on Genetics, Health, and Society have designated Health Information Technology and CDS as priorities for achieving the goals of personalized medicine(10-12). The goal of CDS is "to provide the right information, to the right person, in the right format, through the right channel, at the right point in workflow to improve health and health care decisions and outcomes" and a roadmap has been developed to achieve this goal(11). A systematic review found that adoption of CDS significantly improved clinical practice with a 94% success rate when CDS provided computer-generated recommendations at the point of decision-making and was integrated into the clinical workflow(13). This proposal will develop an open source risk-stratified CDS system that can be accessed by diverse EMRs. We will adopt the standards for FHH to exchange, integrate, manage, and share key data elements that were developed and approved by HL-7 (Health Level 7)(14). Clinical utility of FHH. Qureshi et al.,(2) recently implemented systematic collection of FHH for cardiovascular risk assessment in 24 family practices in the UK using a pragmatic cluster randomized controlled trial design, and demonstrated a highly significant (40%) increase in identification of individuals at high risk. Surprisingly, given that the study was not powered to detect a difference in health behaviors, there was also a highly significant increase in successful smoking reduction or cessation in the intervention group compared to controls. This was the first rigorously designed prospective study to show that systematic collection and use of FHH in a primary care setting can improve risk stratification and health behaviors for CHD and provides an important proof of concept for the work in this proposal. However, in general, the gold standard of a randomized clinical trial (RCT) has not been achieved for FHH. Challenges faced by RCTs including feasibility, expense, and applicability to 'real world' situations, make comparative effectiveness research (CER) and pragmatic implementation trials an appealing solution. CER broadens the scope of methodologies to include not only RCTs but also decision analysis and observational studies(15). Clinical utility of an FHH intervention can be established using measurable outcomes that include clinician and patient behaviors, as well as mechanisms that facilitate these behaviors(16). Provider behaviors include the use of optimal decision-making, counseling the patient, and direct the use of medical services. Mechanisms that promote these behaviors include perceived value of FHH and risk, competencies to collect and discuss FHH, and education. Patient behaviors include increased or reduced screening and use of preventative services, and improved lifestyle behaviors (e.g., diet, exercise, and smoking cessation). Mechanisms such as patients' perceived value of FHH, their ability to obtain the information, family communication, and especially patients' risk perception affect patient behaviors. This approach was highlighted in two studies at the NIH State-of-the-Science Conference - one showed improvement in mammography screening, breast self-examination and clinical breast examination with systematic collection of FHH in a primary care/general population setting(17). The other, the Family History Impact Trial(18) coupled one-time tailored messages to computerized FHH in 3786 healthy primary care patients and assessed self-reported behavior change at 6 months. Those in the intervention group showed increased fruit and vegetable consumption and improved physical activity(19). Use of a touch-screen kiosk in a comprehensive cancer clinic was associated with increases in cancer screening and prevention behaviors and communication with family members(20). References: Heald B, Edelman E, Eng C. 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