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Obtaining comprehensive family health history (FHH) to inform colorectal cancer (CRC) risk management in primary care settings is challenging.To examine the effectiveness of a patient-facing FHH platform to identify and manage patients at increased CRC risk.Two-site, two-arm, cluster-randomized, implementation-effectiveness trial with primary care providers (PCPs) randomized to immediate intervention versus wait-list control.PCPs treating patients at least one half-day per week; patients aged 40-64 with no medical conditions that increased CRC risk.Immediate-arm patients entered their FHH into a web-based platform that provided risk assessment and guideline-driven decision support; wait-list control patients did so 12 months later.McNemar's test examined differences between the platform and electronic medical record (EMR) in rates of increased risk documentation. General estimating equations using logistic regression models compared arms in risk-concordant provider actions and patient screening test completion. Referral for genetic consultation was analyzed descriptively.Seventeen PCPs were randomized to each arm. Patients (n = 252 immediate, n = 253 control) averaged 51.4 (SD = 7.2) years, with 83% assigned male at birth, 58% White persons, and 33% Black persons. The percentage of patients identified as increased risk for CRC was greater with the platform (9.9%) versus EMR (5.2%), difference = 4.8% (95% CI: 2.6%, 6.9%), p.0001. There was no difference in PCP risk-concordant action [odds ratio (OR) = 0.7, 95% CI (0.4, 1.2; p = 0.16)]. Among 177 patients with a risk-concordant screening test ordered, there was no difference in test completion, OR = 0.8 [0.5,1.3]; p = 0.36. Of 50 patients identified by the platform as increased risk, 78.6% immediate and 68.2% control patients received a recommendation for genetic consultation, of which only one in each arm had a referral placed.FHH tools could accurately assess and document the clinical needs of patients at increased risk for CRC. Barriers to acting on those recommendations warrant further exploration.ClinicalTrials.gov NCT02247336 https://clinicaltrials.gov/ct2/show/NCT02247336.

Background Family history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs rendering the value of family history unknown. We evaluated the utility of incorporating family history information for genomic sequencing selection. Methods To ascertain the relationship between family histories on such population-level initiatives, we analysed whole genome sequences of 1750 research participants with no known pre-existing conditions, of which half received comprehensive family history assessment of up to four generations, focusing on 95 cancer genes. Results Amongst the 1750 participants, 866 (49.5%) had high-quality standardised family history available. Within this group, 73 (8.4%) participants had an increased family history risk of cancer (increased FH risk cohort) and 1 in 7 participants (n = 10/73) carried a clinically actionable variant inferring a sixfold increase compared with 1 in 47 participants (n = 17/793) assessed at average family history cancer risk (average FH risk cohort) (p = 0.00001) and a sevenfold increase compared to 1 in 52 participants (n = 17/884) where family history was not available (FH not available cohort) (p = 0.00001). The enrichment was further pronounced (up to 18-fold) when assessing only the 25 cancer genes in the American College of Medical Genetics (ACMG) Secondary Findings (SF) genes. Furthermore, 63 (7.3%) participants had an increased family history cancer risk in the absence of an apparent clinically actionable variant. Conclusions These findings demonstrate that the collection and analysis of comprehensive family history and genomic data are complementary and in combination can prioritise individuals for genomic analysis. Thus, family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs. Trial registration ClinicalTrials.gov NCT02791152. Retrospectively registered on May 31, 2016.

A family health history-based risk assessment is particularly valuable for guiding cancer screening and treatment strategies, yet an optimal implementation depends upon end-users' values and needs. This is not only true prior to disease development, but also for those already affected. The aim of this study is to explore perceptions of the value of knowing one's family health history (FHH)-based risk, experience using a patient-facing FHH tool and the potential of the tool for wider implementation. Twenty multi-ethnic Asian patients undergoing breast cancer treatment in Singapore completed an FHH-based risk assessment. Semi-structured one-on-one interviews were conducted and data were thematically analyzed. All participants were female and slightly more than half were Chinese. The acceptance and usage of an FHH risk assessment tool for cancers and its broader implementation was affected by a perceived importance of personal control over early detection, patient concerns of anxiety for themselves and their families due to risk results, concerns for genetic discrimination, adequacy of follow-up care plans and Asian cultural beliefs toward disease and dying. This study uniquely sheds light on the factors affecting Asian breast cancer patients' perceptions about undergoing an FHH-based risk assessment, which should inform steps for a broader implementation in Asian healthcare systems. Published version This research was funded by the Duke University School of Medicine and Duke-NUS Medical School, grant number Duke/Duke-NUS/RECA(Pilot)/2017/0033, and the APC was funded by the Duke University School of Medicine and Duke-NUS Medical School.

Family health history (FHH) is the most useful means of assessing risk for common chronic diseases. The odds ratio for risk of developing disease with a positive FHH is frequently greater than 2, and actions can be taken to mitigate risk by adhering to screening guidelines, genetic counselling, genetic risk testing, and other screening methods. Challenges to the routine acquisition of FHH include constraints on provider time to collect data and the difficulty in accessing risk calculators. Disease-specific and broader risk assessment software platforms have been developed, many with clinical decision support and informatics interoperability, but few access patient information directly. Software that allows integration of FHH with the electronic medical record and clinical decision support capabilities has provided solutions to many of these challenges. Patient facing, electronic medical record, and web-enabled FHH platforms have been developed, and can provide greater identification of risk compared with conventional FHH ascertainment in primary care. FHH, along with cascade screening, can be an important component of population health management approaches to overall reduction of risk.

Objective Investigate sociodemographic differences in the use of a patient-facing family health history (FHH)-based risk assessment platform. Methods In this large multisite trial with a diverse patient population, we evaluated the relationship between sociodemographic factors and FHH health risk assessment uptake using an information technology (IT) platform. The entire study was administered online, including consent, baseline survey, and risk assessment completion. We used multivariate logistic regression to model effect of sociodemographic factors on study progression. Quality of FHH data entered as defined as relatives: (1) with age of onset reported on relevant conditions; (2) if deceased, with cause of death and (3) age of death reported; and (4) percentage of relatives with medical history marked as unknown was analyzed using grouped logistic fixed effect regression. Results A total of 2,514 participants consented with a mean age of 57 and 10.4% minority. Multivariate modeling showed that progression through study stages was more likely for younger (p-value = 0.005), more educated (p-value = 0.004), non-Asian (p-value = 0.009), and female (p-value = 0.005) participants. Those with lower health literacy or information-seeking confidence were also less likely to complete the study. Most significant drop-out occurred during the risk assessment completion phase. Overall, quality of FHH data entered was high with condition's age of onset reported 87.85%, relative's cause of death 85.55% and age of death 93.76%, and relative's medical history marked as unknown 19.75% of the time. Conclusion A demographically diverse population was able to complete an IT-based risk assessment but there were differences in attrition by sociodemographic factors. More attention should be given to ensure end-user functionality of health IT and leverage electronic medical records to lessen patient burden.

This paper describes the implementation outcomes associated with integrating a family health history–based risk assessment and clinical decision support platform within primary care clinics at four diverse healthcare systems. A type III hybrid implementation-effectiveness trial. Uptake and implementation processes were evaluated using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. One hundred (58%) primary care providers and 2514 (7.8%) adult patients enrolled. Enrolled patients were 69% female, 22% minority, and 32% Medicare/Medicaid. Compared with their respective clinic’s population, patient-participants were more likely to be female (69 vs. 59%), older (mean age 57 vs. 49), and Caucasian (88 vs. 69%) (all p values

APOL1 risk alleles are associated with increased cardiovascular and chronic kidney disease (CKD) risk. It is unknown whether knowledge of APOL1 risk status motivates patients and providers to attain recommended blood pressure (BP) targets to reduce cardiovascular disease.Multicenter, pragmatic, randomized controlled clinical trial.6650 individuals with African ancestry and hypertension from 13 health systems.APOL1 genotyping with clinical decision support (CDS) results are returned to participants and providers immediately (intervention) or at 6 months (control). A subset of participants are re-randomized to pharmacogenomic testing for relevant antihypertensive medications (pharmacogenomic sub-study). CDS alerts encourage appropriate CKD screening and antihypertensive agent use.Blood pressure and surveys are assessed at baseline, 3 and 6 months. The primary outcome is change in systolic BP from enrollment to 3 months in individuals with two APOL1 risk alleles. Secondary outcomes include new diagnoses of CKD, systolic blood pressure at 6 months, diastolic BP, and survey results. The pharmacogenomic sub-study will evaluate the relationship of pharmacogenomic genotype and change in systolic BP between baseline and 3 months.To date, the trial has enrolled 3423 participants.The effect of patient and provider knowledge of APOL1 genotype on systolic blood pressure has not been well-studied. GUARDD-US addresses whether blood pressure improves when patients and providers have this information. GUARDD-US provides a CDS framework for primary care and specialty clinics to incorporate APOL1 genetic risk and pharmacogenomic prescribing in the electronic health record.ClinicalTrials.govNCT04191824.

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data, however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project’s strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common, however, operationalization of each differed in accordance with each study’s implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.

(1) Background: Clinical decision support (CDS) is a vitally important adjunct to the implementation of pharmacogenomic-guided prescribing in clinical practice. A novel CDS was sought for the APOL1, NAT2, and YEATS4 genes to guide optimal selection of antihypertensive medications among the African American population cared for at multiple participating institutions in a clinical trial. (2) Methods: The CDS committee, made up of clinical content and CDS experts, developed a framework and contributed to the creation of the CDS using the following guiding principles: 1. medical algorithm consensus, 2. actionability, 3. context-sensitive triggers, 4. workflow integration, 5. feasibility, 6. interpretability, 7. portability, and 8. discrete reporting of lab results. (3) Results: Utilizing the principle of discrete patient laboratory and vital information, a novel CDS for APOL1, NAT2, and YEATS4 was created for use in a multi-institutional trial based on a medical algorithm consensus. The alerts are actionable and easily interpretable, clearly displaying the purpose and recommendations with pertinent laboratory results, vitals and links to ordersets with suggested antihypertensive dosages. Alerts were either triggered immediately once a provider starts to order relevant antihypertensive agents or strategically placed in workflow-appropriate general CDS sections in the electronic health record (EHR). Detailed implementation instructions were shared across institutions to achieve maximum portability. (4) Conclusions: Using sound principles, the created genetic algorithms were applied across multiple institutions. The framework outlined in this study should apply to other disease-gene and pharmacogenomic projects employing CDS.

Background The presence of hereditary cancer syndromes in cancer patients can have an impact on current clinical care and post-treatment prevention and surveillance measures. Several barriers inhibit identification of hereditary cancer syndromes in routine practice. This paper describes the impact of using a patient-facing family health history risk assessment platform on the identification and referral of breast cancer patients to genetic counselling services. Methods This was a hybrid implementation-effectiveness study completed in breast cancer clinics. English-literate patients not previously referred for genetic counselling and/or gone through genetic testing were offered enrollment. Consented participants were provided educational materials on family health history collection, entered their family health history into the platform and completed a satisfaction survey. Upon completion, participants and their clinicians were given personalized risk reports. Chart abstraction was done to identify actions taken by patients, providers and genetic counsellors. Results Of 195 patients approached, 102 consented and completed the study (mean age 55.7, 100 % women). Sixty-six (65 %) met guideline criteria for genetic counseling of which 24 (36 %) were referred for genetic counseling. Of those referred, 13 (54 %) participants attended and eight (33 %) completed genetic testing. On multivariate logistic regression, referral was not associated with age, cancer stage, or race but was associated with clinical provider (p = 0.041). Most providers (71 %) had higher referral rates during the study compared to prior. The majority of participants found the experience useful (84 %), were more aware of their health risks (83 %), and were likely to recommend using a patient-facing platform to others (69 %). Conclusions 65 % of patients attending breast cancer clinics in this study are at-risk for hereditary conditions based on current guidelines. Using a patient-facing risk assessment platform enhances the ability to identify these patients systematically and with widespread acceptability and recognized value by patients. As only a third of at-risk participants received referrals for genetic counseling, further understanding barriers to referral is needed to optimize hereditary risk assessment in oncology practices. Trial Registration NIH Clinical Trials registry, NCT04639934. Registered Nov 23, 2020 -- Retrospectively registered.

Background:Treatment of a first-time anterior shoulder dislocation (FTASD) is sensitive to patient preferences. The operative or nonoperative management debate provides an excellent opportunity to learn how surgeons apply patient preferences in treatment decisions.Purpose:To determine how patient preferences (repeat dislocation risk, recovery difficulties, fear of surgery, treatment costs) and surgeon factors influence a surgeon’s treatment plan for FTASD.Study Design:Cross-sectional study.Methods:Eight clinical vignettes of hypothetical patients with FTASD (including age, sex, and activity level) were presented to members of the Magellan Society. A second set of matched vignettes with patient preferences and clinical variables were also presented. The vignettes represented scenarios in which evidence does not favor one treatment over another. Respondents were asked how they would manage each hypothetical case. Respondents also estimated the risk of redislocation for the nonoperative cases for comparison with the published rates. Finally, respondents completed a Likert-scale questionnaire to determine their perceptions on factors influencing their decisions.Results:A total of 103 orthopaedic surgeons completed the survey; 48% practiced in an academic hospital; 79% were in practice for 10 years or longer; and 75% had completed a sports medicine fellowship. Patient preferences were the single most important factor influencing treatment recommendation, with activity type and age also important. Just 62% of the surgeon estimates of the risk of redislocation were consistent with the published rates. The inclusion of patient preferences to clinical variables changed treatment recommendations in 62.5% of our hypothetical cases. Respondents rated patient treatment preference as the leading factor in their treatment decision making.Conclusion:Patient preferences were important when deciding the appropriate treatment for FTASD. Respondents were inconsistent when applying evidence in their decision making and estimates of recurrent instability. Decision support tools that deliver patient preferences and personalized evidence-based outcome estimates improve the quality of decision making at the point of care.

Background Risk assessment is a precision medicine technique that can be used to enhance population health when applied to prevention. Several barriers limit the uptake of risk assessment in health care systems; and little is known about the potential impact that adoption of systematic risk assessment for screening and prevention in the primary care population might have. Here we present results of a first of its kind multi-institutional study of a precision medicine tool for systematic risk assessment. Methods We undertook an implementation-effectiveness trial of systematic risk assessment of primary care patients in 19 primary care clinics at four geographically and culturally diverse healthcare systems. All adult English or Spanish speaking patients were invited to enter personal and family health history data into MeTree, a patient-facing family health history driven risk assessment program, for 27 medical conditions. Risk assessment recommendations followed evidence-based guidelines for identifying and managing those at increased disease risk. Results One thousand eight hundred eighty-nine participants completed MeTree, entering information on N = 25,967 individuals. Mean relatives entered = 13.7 (SD 7.9), range 7–74. N = 1443 (76.4%) participants received increased risk recommendations: 597 (31.6%) for monogenic hereditary conditions, 508 (26.9%) for familial-level risk, and 1056 (56.1%) for risk of a common chronic disease. There were 6617 recommendations given across the 1443 participants. In multivariate analysis, only the total number of relatives entered was significantly associated with receiving a recommendation. Conclusions A significant percentage of the general primary care population meet criteria for more intensive risk management. In particular 46% for monogenic hereditary and familial level disease risk. Adopting strategies to facilitate systematic risk assessment in primary care could have a significant impact on populations within the U.S. and even beyond. Trial registration Clinicaltrials.gov number NCT01956773, registered 10/8/2013.

In the current marketplace, there are now more than a dozen commercial companies providing pharmacogenetic tests. Each company varies in the panel of genes they test and the variants they are able to screen for. The reports generated by these companies provide phenotypic interpretations of pharmacogenes and clinically actionable gene–drug interactions based on internally curated data and proprietary algorithms. The freedom to choose the types of evidence to include versus exclude in interpreting genomics has created reporting discrepancies in the industry. The case report presented here reveals the discordant phenotype analysis provided by two pharmacogenetic testing companies. The uncertainty and unnecessary distress experienced by the patient highlights the need for consensus in phenotype reporting within the industry.

Background Translating pharmacogenetic research findings that have shown clinical efficacy into sustainable, routine clinical care at the institutional level requires strong evidence of improved patient outcomes bolstered by equitable reimbursement and a sound financial analysis. Although extensive research on the clinical value of pharmacogenetics has been completed, adoption into clinical practice lags due to a lack of evidence of clinical effectiveness and limited reimbursement. Methods The Sustainability Working Group within the NHGRI IGNITE I Network conducted an online survey of funded and non-funded IGNITE members to determine which genes they are researching, which have been translated into clinical practice, and how tests are billed. Data from the online surveys was consolidated and analyzed with results being tabulated for key findings. Due to the limited sample size, statistical analysis was forgone and results should be considered directional in nature. Results Fifteen out of twenty (75%) online survey responses were received and analyzed from IGNITE member sites delivering clinical care. Forty different genes were identified as being used for either research or clinical care. Thirty-two different genes were reported as being used clinically, an average of 6.9 genes were reported per site. Twenty-two and twenty-one genes were reported as being billed to third party payers or patients respectively. Although the survey did not ask whether sites submitting for reimbursement received payment, Medicare and Medicaid only reimburse for 6 of the 40 (15%) genes being tested. Of the 40 genes, 18 are rated by CPIC as having A/B level of evidence with the remainder being rated as C/D or having no rating. Approximately 32% more A/B rated genes were being reported clinically than non-A/B. Conclusion Adoption of pharmacogenetic testing continues to lag even at sites where leading experts conduct research and have the capability to report tests clinically. Clinical research that supports CPIC A level of evidence is important for provider and payer support. Adoption of pharmacogentic testing must also be justified financially, reimbursement is one key factor, and more health economic studies are needed in order to capture the value created by preventing drug-gene adverse events, emergency room visits, and hospitalizations.

Improving disease risk prediction and tailoring preventive interventions to patient risk factors is one of the primary goals of precision medicine. Family health history is the traditional approach to quickly gather genetic and environmental data relevant to the patient. While the utility of family health history is well-documented, its utilization is variable, in part due to lack of patient and provider knowledge and incomplete or inaccurate data. With the advances and reduced costs of sequencing technologies, comprehensive sequencing tests can be performed as a risk assessment tool. We provide an overview of each of these risk assessment approaches, the benefits and limitations and implementation challenges.

● Nongenetic (epigenetic) mechanisms can change gene activity, but have resulted in few medical applications so far. ● Many barriers impede the integration of family health history into electronic health records (EHR) would be ideal. ● New EHR standards and applications may facilitate integration of family health history into EHR records. ● Polygenic risk scores using common genetic variants may have moderate utility in risk stratification. ● Polygenic risk scores probably will not replace family health history for risk prediction in the foreseeable future. ● Third-party genomic direct to consumer reports based on SNP analysis often lack medical utility and may generate false positive conclusions. ● The clinical utility of genomic sequencing for adult onset disease has yet to be proven, although it has shown utility for rare pediatric diseases.

Purpose: Research on genomic medicine integration has focused on applications at the individual level, with less attention paid to implementation within clinical settings. Therefore, we conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify system-level factors that played a role in implementation of genomic medicine within Implementing GeNomics In PracTicE (IGNITE) Network projects. Methods: Up to four study personnel, including principal investigators and study coordinators from each of six IGNITE projects were interviewed using a semi-structured interview guide that asked interviewees to describe study site(s), progress at each site, and factors facilitating or impeding project implementation. Interviews were coded following CFIR inner-setting constructs. Results: Key barriers included: (1) limitations in integrating genomic data and clinical decision support tools into electronic health records; (2) physician reluctance towards genomic research participation and clinical implementation due to a limited evidence base; (3) inadequate reimbursement for genomic medicine; (4) communication among and between investigators and clinicians; (5) lack of clinical and leadership engagement. Conclusion: Implementation of genomic medicine is hindered by several system-level barriers to both research and practice. Addressing these barriers may serve as important facilitators for studying and implementing genomics in practice.

Purpose Implementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing GeNomics In PracTicE (IGNITE) Network’s efforts to promote: 1) a broader understanding of genomic medicine implementation research; and 2) the sharing of knowledge generated in the network. Methods To facilitate this goal the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide their approach to: identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross network analyses. Results CMG identified ten high-priority CFIR constructs as important for genomic medicine. Of those, eight didn’t have standardized measurement instruments. Therefore, we developed four survey tools to address this gap. In addition, we identified seven high-priority constructs related to patients, families, and communities that did not map to CFIR constructs. Both sets of constructs were combined to create a draft genomic medicine implementation model. Conclusion We developed processes to identify constructs deemed valuable for genomic medicine implementation and codified them in a model. These resources are freely available to facilitate knowledge generation and sharing across the field.

Background Colorectal cancer is the fourth most commonly diagnosed cancer in the United States. Approximately 3–10% of the population has an increased risk for colorectal cancer due to family history and warrants more frequent or intensive screening. Yet, Methods In this ongoing randomized controlled trial, primary care providers at the Durham Veterans Affairs Health Care System and the Madison VA Medical Center are randomized to immediate intervention or wait-list control. Veterans are eligible if assigned to enrolled providers, have an upcoming primary care appointment, and have no conditions that would place them at increased risk for colorectal cancer (such as personal history, adenomatous polyps, or inflammatory bowel disease). Those with a recent lower endoscopy (e.g. colonoscopy, sigmoidoscopy) are excluded. Immediate intervention patients put their family health history information into a web-based platform, MeTree, which provides both patient- and provider-facing decision support reports. Wait-list control patients access MeTree 12 months post-consent. The primary outcome is the risk-concordant colorectal cancer screening referral rate obtained via chart review. Secondary outcomes include patient completion of risk management recommendations (e.g. colonoscopy) and referral for genetic consultation. We will also conduct an economic analysis and an assessment of providers’ experience with MeTree clinical decision support recommendations to inform future implementation efforts if the intervention is found to be effective. Discussion This trial will assess the feasibility and effectiveness of patient-collected family health history linked to decision support to promote risk-appropriate screening in a large healthcare system such as the Department of Veterans Affairs. Trial registration ClinicalTrials.gov, NCT02247336. Registered on 25 September 2014.

Family health history (FHH) is a key predictor of health risk and is universally important in preventive care. However, patients may not be aware of the importance of FHH, and thus, may fail to accurately or completely share FHH with health providers, thereby limiting its utility. In this study, we conducted an online survey of 294 young adults and employees based at a US university setting regarding their knowledge, sharing behaviors, and perceived importance of FHH, and use of electronic clinical tools to document and update FHH. We also evaluated two educational interventions (written and video) to promote knowledge about FHH and its importance to health. We found that 93% of respondents were highly aware of their FHH, though only 39% reported collecting it and 4% using an online FHH tool. Seventy-three percent of respondents, particularly women, had shared FHH with their doctor when prompted, and fewer had shared it with family members. Participants in the video group were significantly more likely to understand the benefits of FHH than those in the written group (p = 0.02). In summary, educational resources, either video or written, will be helpful to promote FHH collection, sharing, and use of online FHH tools.

“IGNITE provides an exemplar of this novel science and it has sparked an opportunity for community-based practices to begin implementing genomics in their own environments and for patients and advocates to form genomic programs designed to improve their own health.”

Conceptual frameworks are useful in research because they can highlight priority research domains, inform decisions about interventions, identify outcomes and factors to measure, and display how factors might relate to each other to generate and test hypotheses. Discovery, translational, and implementation research are all critical to the overall mission of genomic medicine and prevention, but they have yet to be organized into a unified conceptual framework. To fill this gap, our diverse team collaborated to develop the Genomic Medicine Integrative Research (GMIR) Framework, a simple but comprehensive tool to aid the genomics community in developing research questions, strategies, and measures and in integrating genomic medicine and prevention into clinical practice. Here we present the GMIR Framework and its development, along with examples of its use for research development, demonstrating how we applied it to select and harmonize measures for use across diverse genomic medicine implementation projects. Researchers can utilize the GMIR Framework for their own research, collaborative investigations, and clinical implementation efforts; clinicians can use it to establish and evaluate programs; and all stakeholders can use it to help allocate resources and make sure that the full complexity of etiology is included in research and program design, development, and evaluation.

Background. First-time anterior shoulder dislocations (FTASD) provide an opportunity to examine the value of integrating stated-preference data with decision modeling to differentiate between patients whose preferred management strategy involves operative or nonoperative treatment. The objective of this study was to evaluate the efficacy of a FTASD decision tool intervention with individual preference measurement compared with a text-based control in a randomized controlled trial. Methods. Two hundred respondents between 18 and 35 years of age at risk for experiencing an FTASD were enrolled from the orthopedic clinics and randomized to receive either an interactive decision tool intervention capable of eliciting patient preferences for treatment of an FTASD or a text-based control on shoulder dislocations and treatments. The primary outcome was preference for operative or nonoperative treatment choice. Secondary outcomes included the decisional conflict scale (DCS), stage of decision making, patient activation and engagement, awareness of preference sensitive decisions, knowledge retention, and instrument acceptability. Results. One hundred respondents were randomized to the intervention and 100 to the control. A total of 154 men and 46 women with an average age of 23.6 years completed the survey. Participants in the intervention group made treatment decisions that aligned more closely with evidence-based recommendations than those in the control group ( P = 0.016). Secondary outcomes showed no difference between intervention and control, excluding several DCS subscales. Discussion. An interactive, preference-based decision tool for treatment of FTASD affects patient decision making by guiding respondents toward treatment decisions that align more closely with evidence-based recommendations in the absence of a consultation with an orthopedic provider compared with a standard-of-care control tool. Additional study is needed to evaluate the long-term effects of this tool on treatment outcomes, patient adherence, and satisfaction. LEVEL OF EVIDENCE: 2