Use of a patient-entered family health history tool with decision support in primary care: impact of identification of increased risk patients on genetic counseling attendance.

Several barriers inhibit collection and use of detailed family health history (FHH) in primary care. MeTree, a computer-based FHH intake and risk assessment tool with clinical decision support, was developed to overcome these barriers. Here, we describe the impact of MeTree on genetic counseling (GC) referrals and attendance. Non-adopted, English speaking adults scheduled for a well-visit in two community-based primary-care clinics were invited to participate in an Implementation-Effectiveness study of MeTree. Participants' demographic characteristics and beliefs were assessed at baseline. Immediately after an appointment with a patient for whom GC was recommended, clinicians indicated whether they referred the patient and, if not, why. The study genetic counselor kept a database of patients with a GC recommendation and contacted those with a referral. Of 542 patients completing MeTree, 156 (29 %) received a GC recommendation. Of these, 46 % (n = 72) were referred and 21 % (n = 33) underwent counseling. Patient preferences, additional clinical information unavailable to MeTree, and an incomplete clinician evaluation of the FHH accounted for the 85 patients clinicians chose not to refer. Although MeTree identified a significant proportion of patients for whom GC was recommended, persistent barriers indicate the need for improved referral processes and patient and physician education about the benefits of GC.

Public health genetic counselors: activities, skills, and sources of learning.

Specialization within genetic counseling is apparent, with 29 primary specialties listed in the National Society of Genetic Counselors' 2012 Professional Status Survey (PSS). PSS results show a steady proportion of genetic counselors primarily involved in public health, yet do not identify all those performing public health activities. Little is known about the skills needed to perform activities outside of "traditional" genetic counselor roles and the expertise needed to execute those skills. This study aimed to identify genetic counselors engaging in public health activities, the skills used, and the most influential sources of learning for those skills. Participants (N = 155) reported involvement in several public health categories: (a) Education of Public and/or Health Care Providers (n = 80, 52 %), (b) Population-Based Screening Programs (n = 70, 45 %), (c) Lobbying/Public Policy (n = 62, 40 %), (d) Public Health Related Research (n = 47, 30 %), and (e) State Chronic Disease Programs (n = 12, 8 %). Regardless of category, "on the job" was the most common primary source of learning. Genetic counseling training program was the most common secondary source of learning. Results indicate that the number of genetic counselors performing public health activities is likely higher than PSS reports, and that those who may not consider themselves "public health genetic counselors" do participate in public health activities. Genetic counselors learn a diverse skill set in their training programs; some skills are directly applicable to public health genetics, while other public health skills require additional training and/or knowledge.

Implementing family health history risk stratification in primary care: impact of guideline criteria on populations and resource demand.

UNLABELLED: The Genomic Medicine Model aims to facilitate patient engagement, patient/provider education of genomics/personalized medicine, and uptake of risk-stratified evidence-based prevention guidelines using MeTree, a patient-facing family health history (FHH) collection and clinical decision support (CDS) program. Here we report the number of increased risk (above population-level risk) patients identified for breast/ovarian cancer, colon cancer, hereditary syndrome risk, and thrombosis; the prevalence of FHH elements triggering increased-risk status; and the resources needed to manage their risk. STUDY DESIGN: hybrid implementation-effectiveness study of adults with upcoming well-visits in 2 primary care practices in Greensboro, NC. PARTICIPANTS: 1,184, mean age = 58.8, female = 58% (N = 694), non-white = 20% (N = 215). Increased Risk: 44% (N = 523). RECOMMENDATIONS: genetic counseling = 26% (N = 308), breast MRI = 0.8% (N = 10), breast chemoprophylaxis = 5% (N = 58), early/frequent colonoscopies = 19% (N = 221), ovarian cancer screening referral = 1% (N = 14), thrombosis testing/counseling = 2.4% (N = 71). FHH elements: 8 FHH elements lead to 37.3% of the increased risk categorizations (by frequency): first-degree-relative (FDR) with polyps age ≥60 (7.1%, N = 85), three relatives with Lynch-related cancers (5.4%, N = 65), FDR with polyps age <60 (5.1%, N = 61), three relatives on same side of family with same cancer (4.9%, N = 59), Gail score ≥1.66% (4.9%, N = 58), two relatives with breast cancer (one ≤age 50) (4.1%, N = 49), one relative with breast cancer ≤age 40 (4.1%, N = 48), FDR with colon cancer age ≥60 (1.7%, N = 20). MeTree identifies a high percentage of individuals in the general primary care population needing non-routine risk management/prevention for the selected conditions. Implementing risk-stratification in primary care will likely increase demand for related-resources, particularly colon screening and GC. Understanding the prevalence of FHH elements helps predict resource needs and may aid in guideline development.

Providing patient education: impact on quantity and quality of family health history collection.

BACKGROUND: Family health history (FHH) is an underutilized tool in primary care to identify and risk-stratify individuals with increased cancer risk. OBJECTIVE: Evaluate the influence of patient education on quantity and quality of FHH entered into a primary care-based software program, and impact on the program's cancer risk management recommendations. DESIGN: Two primary care practices within a larger type II hybrid implementation-effectiveness controlled clinical trial. PARTICIPANTS: English speaking non-adopted patients with a well visit appointment December 2012-March 2013. INTERVENTIONS: One to two weeks prior to their well visit appointment, participants entered their FHH into the program. PARTICIPANTS were then provided educational materials describing key FHH components. They were instructed to use the interval to collect additional FHH information. Patients then returned for their scheduled appointment, and updated their FHH with any new information. MAIN MEASURES: Percentage per pedigree of relatives meeting individual quality criteria. Changes made after patient education and changes to recommendations for surveillance, chemoprevention or genetic counseling referral. KEY RESULTS: Post patient education, pedigrees exhibited a greater percentage (per pedigree) of: deceased relatives with age at death (84 vs. 81 % p = 0.02), deceased relatives with cause of death (91 vs. 87 % p = 0.02), relatives with a named health condition (45 vs. 42 % p = 0.002), and a greater percentage of relatives with high quality records (91 vs. 89 % p = 0.02). Of 43 participants with pedigree changes that could trigger changes in risk stratified prevention recommendations, 12 participants (28 %) received such changes. CONCLUSIONS: Patient education improves FHH collection and subsequent risk stratification utilized in providing actionable evidence-based care recommendations for cancer risk management.

Quality of family history collection with use of a patient facing family history assessment tool.

BACKGROUND: Studies have shown that the quality of family health history (FHH) collection in primary care is inadequate to assess disease risk. To use FHH for risk assessment, collected data must have adequate detail. To address this issue, we developed a patient facing FHH assessment tool, MeTree. In this paper we report the content and quality of the FHH collected using MeTree. DESIGN: A hybrid implementation-effectiveness study. Patients were recruited from 2009 to 2012. SETTING: Two community primary care clinics in Greensboro, NC. PARTICIPANTS: All non-adopted adult English speaking patients with upcoming appointments were invited to participate. INTERVENTION: Education about and collection of FHH with entry into MeTree. MEASURES: We report the proportion of pedigrees that were high-quality. High-quality pedigrees are defined as having all the following criteria: (1) three generations of relatives, (2) relatives' lineage, (3) relatives' gender, (4) an up-to-date FHH, (5) pertinent negatives noted, (6) age of disease onset in affected relatives, and for deceased relatives, (7) the age and (8) cause of death (Prim Care31:479-495, 2004.). RESULTS: Enrollment: 1,184. Participant demographics: age range 18-92 (mean 58.8, SD 11.79), 56% male, and 75% white. The median pedigree size was 21 (range 8-71) and the FHH entered into MeTree resulted in a database of 27,406 individuals. FHHs collected by MeTree were found to be high quality in 99.8% (N = 1,182/1,184) as compared to <4% at baseline. An average of 1.9 relatives per pedigree (range 0-50, SD 4.14) had no data reported. For pedigrees where at least one relative has no data (N = 497/1,184), 4.97 relatives per pedigree (range 1-50, SD 5.44) had no data. Talking with family members before using MeTree significantly decreased the proportion of relatives with no data reported (4.98% if you talked to your relative vs. 10.85% if you did not, p-value < 0.001.). CONCLUSION: Using MeTree improves the quantity and quality of the FHH data that is collected and talking with relatives prior to the collection of FHH significantly improves the quantity and quality of the data provided. This allows more patients to be accurately risk stratified and offered appropriate preventive care guided by their risk level. TRIAL NUMBER: NCT01372553.

Collection of family health history for assessment of chronic disease risk in primary care.

BACKGROUND: Family health history can predict a patient's risk for common complex diseases. This project assessed the completeness of family health history data in medical charts and evaluated the utility of these data for performing risk assessments in primary care. METHODS: Family health history data were collected and analyzed to determine the presence of quality indicators that are necessary for effective and accurate assessment of disease risk. RESULTS: More than 99% of the 390 paper charts analyzed contained information about family health history, which was usually scattered throughout the chart. Information on the health of the patient's parents was collected more often than information on the health of other relatives. Key information that was often not collected included age of disease onset, affected side of the family, and second-degree relatives affected. Less than 4% of patient charts included family health histories that were informative enough to accurately assess risk for common complex diseases. LIMITATIONS: Limitations of this study include the small number of charts reviewed per provider, the fact that the sample consisted of primary care providers in a single geographic location, and the inability to assess ethnicity, consanguinity, and other indicators of the informativeness of family health history. CONCLUSIONS: The family health histories collected in primary care are usually not complete enough to assess the patient's risk for common complex diseases. This situation could be improved with use of tools that analyze the family health history information collected and provide risk-stratified decision support recommendations for primary care.

Development and validation of a primary care-based family health history and decision support program (MeTree).

INTRODUCTION: Family health history is a strong predictor of disease risk. To reduce the morbidity and mortality of many chronic diseases, risk-stratified evidence-based guidelines strongly encourage the collection and synthesis of family health history to guide selection of primary prevention strategies. However, the collection and synthesis of such information is not well integrated into clinical practice. To address barriers to collection and use of family health histories, the Genomedical Connection developed and validated MeTree, a Web-based, patient-facing family health history collection and clinical decision support tool. MeTree is designed for integration into primary care practices as part of the genomic medicine model for primary care. METHODS: We describe the guiding principles, operational characteristics, algorithm development, and coding used to develop MeTree. Validation was performed through stakeholder cognitive interviewing, a genetic counseling pilot program, and clinical practice pilot programs in 2 community-based primary care clinics. RESULTS: Stakeholder feedback resulted in changes to MeTree's interface and changes to the phrasing of clinical decision support documents. The pilot studies resulted in the identification and correction of coding errors and the reformatting of clinical decision support documents. MeTree's strengths in comparison with other tools are its seamless integration into clinical practice and its provision of action-oriented recommendations guided by providers' needs. LIMITATIONS: The tool was validated in a small cohort. CONCLUSION: MeTree can be integrated into primary care practices to help providers collect and synthesize family health history information from patients with the goal of improving adherence to risk-stratified evidence-based guidelines.

Patient and primary care provider experience using a family health history collection, risk stratification, and clinical decision support tool: a type 2 hybrid controlled implementation-effectiveness trial.

BACKGROUND: Family health history (FHH) is the single strongest predictor of disease risk and yet is significantly underutilized in primary care. We developed a patient facing FHH collection tool, MeTree, that uses risk stratification to generate clinical decision support for breast cancer, colorectal cancer, ovarian cancer, hereditary cancer syndromes, and thrombosis. Here we present data on the experience of patients and providers after integration of MeTree into 2 primary care practices. METHODS: This was a Type 2 hybrid controlled implementation-effectiveness study in 3 community-based primary care clinics in Greensboro, NC. All non-adopted adult English speaking patients with upcoming routine appointments were invited. Patients were recruited from December 2009 to the present and followed for one year. Ease of integration of MeTree into clinical practice at the two intervention clinics was evaluated through patient surveys after their appointment and at 3 months post-visit, and physician surveys 3 months after tool integration. RESULTS: Total enrollment =1,184. Average time to complete MeTree = 27 minutes. Patients found MeTree: easy to use (93%), easy to understand (97%), useful (98%), raised awareness of disease risk (85%), and changed how they think about their health (86%). Of the 26% (N = 311) asking for assistance to complete the tool, age (65 sd 9.4 vs. 57 sd 11.8, p-value < 0.00) and large pedigree size (24.4 sd 9.81 vs. 22.2 sd 8.30, p-value < 0.00) were the only significant factors; 77% of those requiring assistance were over the age of 60. Providers (N = 14) found MeTree: improved their practice (86%), improved their understanding of FHH (64%), made practice easier (79%), and worthy of recommending to their peers (93%). CONCLUSIONS: Our study shows that MeTree has broad acceptance and support from both patients and providers and can be implemented without disruption to workflow.

Primary care physicians' awareness, experience and opinions of direct-to-consumer genetic testing.

The purpose of this study was to assess primary care physicians' awareness, experience, opinions and preparedness to answer patients' questions regarding direct-to-consumer (DTC) genetic testing. An anonymous survey was mailed to 2,402 family and internal medicine providers in North Carolina. Of the 382 respondents, 38.7% (n = 148) were aware of and 15% (n = 59) felt prepared to answer questions about DTC genetic tests. Respondents aged 50 or older were more likely to be aware of DTC genetic testing than those less than 40 years old (OR = 2.42). Male providers were more likely to feel prepared to answer questions than female providers (OR = 2.65). Among respondents who reportedly were aware, family practitioners were more likely than internists (OR = 3.30) to think DTC testing was clinically useful, and 18.9% had patients ask questions or bring in test results. The small percent of physicians who were aware of DTC genetic testing or felt prepared to answer questions about it suggests that education of providers will be necessary if testing becomes more widespread.

Educational needs of primary care physicians regarding direct-to-consumer genetic testing.

To assess the educational needs of North Carolina primary care physicians (PCPs) about direct-to-consumer (DTC) genetic testing, surveys were mailed to 2,402 family and internal medicine providers in North Carolina. Out of 382 respondents, 323 (85%) felt unprepared to answer patient questions and 282 (74%) reported wanting to learn about DTC genetic testing. A total of 148 (39%) were aware of DTC genetic testing. Among these, 63 (43%) thought DTC genetic testing was clinically useful. PCPs who felt either unprepared to answer patient questions (OR = 0.354, p = 0.01) or that DTC genetic testing was clinically useful (OR = 5.783, p = 0.00) were more likely to want to learn about DTC genetic testing. PCPs are interested in learning about DTC genetic testing, but are mostly unaware of DTC testing and feel unprepared to help patients with DTC testing results. Familiar and trusted channels that provide the information and tools PCPs need to help answer patient's questions and manage their care should be used when creating educational programs.

Protocol for implementation of family health history collection and decision support into primary care using a computerized family health history system.

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.