NIH defines implementation science as the study of “strategies to adopt and integrate evidence-based health interventions and change practice patterns within specific settings.” It answers questions such as, “Why do evidence-based interventions lose effectiveness over time in real-world settings?” and “How do interventions need to be applied to various settings to maintain effectiveness?” When interventions shown to be efficacious in the controlled environment of clinical trials are applied to the real-world where there is greater population variability and the inability to maintain control over environment and patient response, it is important to evaluate for maintained effectiveness.
Implementation science frequently employs hybrid study designs. Hybrid designs provide a structure for the complex process of collecting two entirely different types of information: implementation (how well the intervention is taken up by the clinical sites) and effectiveness (the clinical impact of the intervention). The choice between Type I, II or III hybrid designs depends upon the amount of underlying effectiveness data. Studies of interventions with limited effectiveness data are best suited to a Type I design where primary outcomes are effectiveness and secondary outcomes focus on implementation. The reverse is true for Type III studies with Type II studies being somewhere in between. All hybrid studies have a phased study design: pre-implementation, implementation and post-implementation.
The Duke Center for Precision Medicine has developed two hybrid implementation-effectiveness trials (one on-going) that evaluate implementation and effectiveness of a web-based risk stratification tool, MeTree, within the primary care setting. We are evaluating MeTree in ethnically and geographically diverse settings to understand how changes to setting and population affect implementation efforts. Our current study is based on the RE-AIM framework and the Weiner organizational model of innovation implementation. Both qualitative and quantitative measures are used to understand both generalizable and specific barriers to implementation across clinics. At the end of the study, clinic-type templates will be developed to serve as road maps for broader implementation of similar IT tools.
The Duke Center for Precision Medicine is also collaborating on a clinical trial evaluating implementation of MeTree within the Veteran Affairs Healthcare System. Primary care providers are randomized to immediate or 12-month delayed MeTree report availability. The primary outcome measured is difference in rates of identification of patients at increased risk for colorectal cancer (CRC) and differences in referral for increased risk screening and genetic counseling for CRC risk.
Deploying a genomic-medicine risk assessment model for diverse primary care populations and settings
Family health history (FHH), a critical component of genomic medicine that is essential for both identifying individuals at risk for hereditary conditions and for contextualizing results of genetic testing, continues to be broadly underutilized and underappreciated in clinical care. Barriers to adequate data collection and synthesis are numerous and cross all clinical stakeholders: patients, providers, and health systems. Significantly, they include the pervasive view that FHH is unimportant except in select cases and that it rarely contributes to clinical decision making. With this perspective, few providers have been willing to allocate precious time to collect detailed FHHs or to learn the complex algorithms required to synthesize FHH data into actionable care plans. However, in studies of systematic FHH-based risk assessments in unselected populations, 25% of patients meet risk criteria for (actionable) hereditary conditions. FHH-based risk assessment programs have emerged to address these barriers, but as designed do not meet the needs of low literacy, low resource populations. The goal of this proposal is to develop a scalable end-to-end solution for risk assessment and management that meets the needs of low resource settings. Our central hypothesis is that combining FHH-driven risk assessment, a literacy-enhanced interface, family engagement (through social networking platforms for data gather and risk sharing), and a genetic testing delivery system, will create a solution that engages and increases the proportion of diverse patients who are identified as at increased risk, who undergo testing, and, when appropriate, who initiate cascade screening among relatives. In this proposal we will define and deploy this new care delivery model as the “Genomic medicine Risk Assessment Care for Everyone” (GRACE). To this end we will 1) develop and deploy the model using pre-implementation assessments at clinical sites with highly diverse patient populations to select the most appropriate integration options and pathways for both patients and providers; and 2) perform a randomized implementation-effectiveness pragmatic hybrid trial to assess implementation and effectiveness outcomes relevant to these diverse populations. Outcomes will include reach, uptake, clinical utility, accessibility, genetic testing frequency, genetic testing results, and cost-effectiveness.
Improving identification and healthcare for patients with Inherited Cancer Syndromes: Evidence-based EMR implementation using a web-based computer platform
From the earliest recognition of families with a high rate of cancer over 100 years ago, researchers have been focused on the genetic underpinnings of inherited cancers; however, identification remains a significant challenge due to persistent barriers across patient, provider and health system stakeholders, despite recent advances in the development of electronic medical records (EMR) and risk prediction tools that use family health history (FHH) information. Innovations in bioinformatic technology hold great promise in overcoming many of these barriers, particularly with the development of FHH applications that collect and analyze family data, and SMART-FHIR capabilities that can integrate third party apps with the EMR. MeTree, a patient facing risk assessment platform for 23 hereditary cancer syndromes with integrated education and evidence-based clinical decision support, is one such program that served as the backbone of the Implementing Genomics in Practice (IGNITE) network’s FHH clinical utility study, where it demonstrated improvements in the identification of those at risk, yet, also highlighted ongoing challenges particularly around undergoing genetic counseling and testing, and awareness of risk. We submit that these barriers can be overcome and that we can significantly improve identification and management of those at risk for hereditary cancer syndromes by bringing together a single clinical care model that contains: a patient-facing risk assessment platform integrated into the EMR, automated risk calculation with clinical decision support for patients and physicians for multiple hereditary cancer syndromes, systematic assessment of risk across a variety of clinic settings, guidance and education on family health history, genomics, risk management, and cascade screening, and an implementation sciences framework to allow us to build a novel and scalable clinical care paradigm for hereditary cancer risk assessment and risk management. To do this we will: 1) deploy a care delivery model that will facilitate systematic risk assessment for hereditary cancers in diverse clinical environments (in primary care and cancer care clinics at two different medical centers ) in a randomized controlled trial of 4000 patients; 2) improve access to genetic healthcare providers who provide counseling, testing and follow up management for participants at risk for hereditary cancer syndromes by deploying the care delivery model in the cancer genetic counseling clinics in a randomized controlled trial of 300 patients; and 3) explore the feasibility of our care delivery model to improve case ascertainment for two underappreciated at-risk populations: a) Family engagement with cascade testing after genetic testing and b) Cancer patients with germline variants identified by clinical tumor genome sequencing.
ADOPT-PGx: A Depression and Opioid Pragmatic Trial in Pharmacogenetics
Pain and depression are conditions that impact substantial proportions of the US population. Finding safe and effective drug therapies for both conditions is challenging. In the case of treatment for acute and chronic pain, the challenge is finding effective therapy while minimizing adverse effects or opioid addiction (and the ensuing consequences). For depression, there are few clinically relevant predictors of successful treatment leading to multiple trials of inadequate therapy for some patients. Both opioid and antidepressant prescriptions can be guided by pharmacogenetics (PGx) data based on existing guidelines from the Clinical Pharmacogenetics Implementation Consortium (CPIC). A pilot study conducted in IGNITE I in patients with chronic pain supports the potential benefit of a genotype-guided approach to pain therapy. Existing studies of tailored antidepressant therapy are small and often industry-sponsored but suggest the genotype-guided approach is superior to usual care. We propose a randomized clinical trial that enrolls patients into three PGx-guided therapy scenarios: acute post-surgical pain, chronic pain, and depression. For each scenario, participants will be randomized to genotype-guided drug therapy versus usual approaches to drug therapy selection (hereafter referred to as usual care). Changes in patient reported outcomes representing pain and depression control using standard PROMIS scales define the primary endpoints. Secondary analyses include safety endpoints, changes in overall well-being, and economic impact represented by differences in healthcare utilization.
Genetic testing to Understand and Address Renal Disease Disparities across the United States (GUARDD-US)
The promise of genomic medicine implementation transforming healthcare and improving health will not be realized until discoveries become relevant to and available for use by diverse populations and the clinicians who care for them. Our GeNYC team is prepared to help medical genomics become an innovative, collaborative discipline with inclusiveness, health and health equity at its core. We have decades of experience engaging diverse stakeholders- researchers, patients, clinicians, advocates and entrepreneurs- to conduct chronic disease prevention and control clinical trials in our large health system and network of safety net clinics. In our 16 trials recently completed or underway, 80% included primary care or community sites, with a mean 732 participants/study, 69% were African ancestry (AA) or Latino, 17% refused, and we retained 84%. We chose to harness this expertise to conduct genomic implementation research. At the heart of our team is a Genomics Stakeholder Board. Together, we conducted GUARDD, a multi-site pragmatic clinical trial (PCT) to study effects and challenges of incorporating genetic risk information into primary care. We tested AA adults with hypertension, without diabetes or chronic kidney disease (CKD) for APOL1 high-risk variants nearly exclusive to AAs, that increase kidney failure risk 10-fold. We recruited 2052 AA adults from 15 community and academic primary care practices in NYC, trained lay staff who returned APOL1 results to patients and alerted clinicians of results through clinical decision support in electronic health records (EHRs). Our co-primary outcome, systolic blood pressure (SBP) at 3m (retention rate 93%) reduced by 6mmHg in those told they had high-risk APOL1 genotypes vs. 3mmHg if told they were low-risk (p=0.008). We are now prepared and committed to join IGNITE II and conduct genomic implementation PCTs in diverse settings and populations. We brought a transdisciplinary team together for this purpose, and to study the impact of APO1 risk information on a broader phenotype of AA patients, including those with CKD, who have even higher risk for kidney failure and are often undiagnosed by primary care providers. We aim to: (1) Serve as an enhanced diversity site for IGNITE2, facilitating recruitment and retention of patients into genomic implementation PCTs; and (2) Conduct GUARDD-US, a network-wide PCT expanding GUARDD to AA patients without and with CKD and to other IGNITE sites with different patient and provider populations. We will randomize patients to APOL1 testing vs. waitlist control, and evaluate impact on SBP, renal diagnosis, cost effectiveness and psycho-behavioral outcomes, so results can inform decisions by clinicians, policymakers and payers. If successful, GeNYC may provide a robust framework for future endeavors to implement genomic medicine in diverse clinical practices, validate APOL1 risk-informed management of hypertensive AA patients at high risk of kidney failure, contribute to important efforts to eliminate racial, ethnic and ancestral disparities in health, and show that vulnerable populations can be the first to benefit from genomic discoveries. Read publication.
Electronic Medical Records and Genomics (eMERGE) network 3.0 family history supplement
The Electronic Medical Records and Genomics (eMERGE) network has been a leader in the development of methods to integrate genomic medicine into the electronic health record (EHR) with an overall goal of improving health outcomes by tailoring healthcare based on genomic information. This goal would be greatly enhanced by incorporating family health history (FHH) into the EHR, so that future health systems will benefit from integrated genomic risk prediction, test results and clinical decision support (CDS). Yet, FHH is used primarily by specialists rather than primary care providers and integration into EHRs has lagged behind other advances in genetic risk assessment, due to complex organization of health systems. The goal of this supplement proposal is three-fold: to utilize the CFIR method to assess and compare the capabilities and needs related to FHH integration across diverse (U.S. and international) healthcare systems; to create a comprehensive, customizable organizational guide for implementing FHH screening into the EHR; and to create a robust simulation environment that will be used to develop the necessary informatic infrastructure.
Development and clinical implementation pilot of an oncology-specific risk assessment tool in Singapore
This study saught to further develop and evaluate the implementation of a Duke-developed risk assessment tool, MeTree, within the National Cancer Center Singapore (NCCS). It leverages a larger collaborative effort between Duke’s Center for Applied Genomics and Precision Medicine and the SingHealth/Duke-NUS Institute of Precision Medicine to explore, develop, and pilot a prototype MeTree cancer module through collaboration with NCCS. This would involve 1) creating a MeTree integration plan for NCCS patients and providers that will optimize its value, 2) incorporating additional data elements and risk algorithms into MeTree as appropriate, 3) generating tailored clinical decision support for the patient and their oncologist to facilitate appropriate ordering and interpretation of genetic tests. This new MeTree cancer module will be evaluated in a mixed methods hybrid type II pilot at NCCS. Findings will guide expansion and development of other modules as well as provide the foundation for external funding opportunities. Read publication.
Implementation, Adoption, and Utility of Family History in Diverse Care Settings
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, Essentia 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 ‘naïve’ 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 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 open clinical decision support (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. Read publication.