Table 3.

Potential solutions for advancing use of RWE.

Area of focus	Challenges	Opportunities	Potential solutions
I. Relevance and rigor of RWE for payers	Payers view many RWE studies as lacking relevance and timeliness for coverage or utilization management decisions. RWE study methods not perceived as sufficiently rigorous and transparent by payers.	Payers are willing to engage with test developers and researchers in study planning activities but need to know how to discern clinically valid tests among avalanche of new NGS tests. Multiple publicly available best practices/recommendations for designing, conducting, and reporting RWE studies. Several groups have published RWE evaluation tools specifically for payers.	1Require test developers to show evidence of analytic and clinical validity, as well as clinical flowchart of how new NGS test is hypothesized to affect health outcomes. 2Develop transparent engagement processes to ensure payer information needs are reflected in study design decisions. 3Convene multistakeholder panels to tailor existing best practices and evaluation tools to NGS.
II. Incentives for RWE development	Limited number of studies outside of oncology, PGx, and perinatal/pediatrics examples of RWE directly affecting coverage decisions for NGS. RWE to demonstrate clinical utility of NGS is undersupplied by the market.	Numerous groups building data infrastructure to support RWE studies, particularly in oncology but also in population-based data networks and learning healthcare systems. FDA is supporting the use of RWE to make regulatory decisions for in vitro diagnostics. Federal efforts to require data standardization and interoperability such as HITECH and value-based payments such as MACRA* should also facilitate access to RWD. There are a growing number of learning healthcare systems focused on genomics and publicly subsidized data networks that can reduce research study costs.	4Encourage industry, federal, and nonprofit funding to conduct and publish RWE studies to demonstrate the relative benefits and harms of NGS testing for patient and consumer subgroups in existing data networks. 5Support FDA efforts such as MDIC by providing use cases to demonstrate how RWE can inform regulatory and payer decision making for NGS. 6Include genomic data in meaningful use requirements. 7Conduct RWE studies in networks such as Intermountain, Geisinger, Innovation Health, Sanford, University of Vermont, and PCORnet.
III. Educational needs regarding both RWE and NGS	Some payers lack experience and expertise to use RWE study results in their local context. Some payers lack knowledge on how to evaluate clinical utility of NGS tests. Some clinicians lack knowledge about when to order tests and how to interpret results.	Investments in RWE methods training and professional hiring are growing rapidly in both public and private sectors. Industry, nonprofit, and governmental organizations are investing in genomics literacy and NGS education.	8Provide training in observational study methods and pragmatic clinical trials for payers and other stakeholders on NGS. 9Encourage both just-in-time training based on adult learning principles and in-person training at professional and industry meetings.
IV. Standards for testing and reporting NGS data	Results of NGS tests are complex, and laboratory procedures for test validation and reporting lack transparency. The same test performed by different labs can produce different results.	Federal and private sector groups (eg, professional societies such as CAP AMP, ACMG†) are addressing these problems. Data sharing is required for NIH grants including genomics and promoted by nonprofits such as Global Alliance for Genomics and Health.	10Labs that follow published guidelines or consensus recommendation statements for NGS assay and bioinformatics pipeline validation could receive higher reimbursement rates to incentivize data sharing. 11Support ClinGen efforts to define the clinical relevance of genes and variants for use in precision medicine and research. 12Support federal efforts to standardize procedures for NGS reanalysis and variant reinterpretation and require clinical labs to have policies and protocols in place to support reporting any reclassifications that may affect clinical management.
V. Standards for genomic data representation in EHR	Genomic data integration in the EHR is not a top priority for major vendors, yet solutions require their leadership. Genetic data are not aggregated and stored in a structured manner accessible to researchers and clinicians; EHRs lack comprehensive clinical data and patient-generated data.	Innovative NGS companies and federally funded networks such as eMERGE are developing standardized protocols for genomic data integration across multiple EHR systems. Nonprofits have developed data exchange standards such as FHIR (Fast Healthcare Interoperability Resources) that better support CDS and unify how genomic variant data are accessed. Artificial intelligence (ie, natural language processing, machine learning, deep learning)-based methods reduce the need for manual curation and enable integration with other digital data sources.	13Evaluate clinic-genomic interoperable applications using an implementation science framework. 14Recognize that most payers have limited understanding of these methods and will require educational efforts to trust and apply RWE study results developed with cutting-edge methods.
VI. Standards for NGS evidence review by payers	Payer evidence requirements for NGS coverage decision making not clearly communicated to or understood by test developers.	FDA-CMS parallel review process and MolDx program (CED) is an opportunity to develop necessary evidence for federal payers. Stakeholders worked through nonprofits such as Center for Medical Technology Policy to recommend coverage for NGS tumor profiling based on number of genes.	15Encourage public and private payer support of coverage with evidence development for tests with promising evidence of clinical utility. 16Convene a multistakeholder group including payers to define methodological requirements for demonstration of clinical utility for specific clinical contexts (eg, PGx, rare disease, cancer, etc).
VII. Partnerships	No single stakeholder has the resources to develop clinical utility data independently.	Numerous examples of both public and private NGS implementation and data-sharing partnerships, including learning healthcare systems partnering with industry.	17Engage stakeholders in research question priority setting and study design. 18Conduct clinical utility studies in these settings and publish results.
VIII. Role of RWD in support of OBCs for NGS	OBC implementation is limited by data barriers and lack of outcome measures. Most OBCs are not publicly disclosed, so there is no shared learning.	Payers are demonstrating increased receptivity to, and use of, OBCs for new and/or expensive interventions. Precompetitive collaboration to develop an OBC implementation framework has been identified as a critical unmet need. Companies are beginning to share results of NGS-related OBCs.	19Support risk-based market access and inform evidence claims for NGS by developing an NGS-specific framework for evaluating and implementing OBCs. 20Work with stakeholders to develop NGS-specific outcome measures.
IX. Narrow definition of how value of NGS is measured	RWE necessary for value-based frameworks and payment models, but the patient and other perspectives (personal utility, reduction in uncertainty) are often missing.	Numerous multistakeholder groups have developed approaches for customizing value assessments for payers, patients, and other decision makers. Growing evidence that patients may become key drivers of RWE use by payers.	21Build on work by ISPOR‡ and other non-profits to demonstrate how NGS aligns with expanded definition of value. 22Track developments in value-based payment models as critical factors influencing how RWE is being used by payers. 23Promote inclusion of patient-reported outcomes in value assessments.

CDS indicates clinical decision support; CMS, Centers for Medicare & Medicaid Services; EHR, electronic health record; FDA, US Food and Drug Administration; MDIC, Medical Device Innovation Consortium; NGS, next-generation sequencing; OBC, outcomes-based contracting; PGx, pharmacogenetic testing; RWD, real-world data; RWE, real-world evidence.

^*Health Information Technology for Economic and Clinical Health, Medicare Access and CHIP Reauthorization Act of 2015.

^†College of American Pathology, Association for Molecular Pathology, American College of Genetics and Genomics.

^‡International Society for Pharmacoeconomics and Outcomes Research.