Accelerating Evidence Generation to Fulfill the Promise of Genomic MedicinePosted on by
This blog post is a summary of a recently published paper in Genetics in Medicine. Genomic tests should demonstrate analytical and clinical validity and clinical utility prior to wider adoption in clinical practice. However, clinical utility remains elusive for many such tests. A recent collaborative review of systematic reviews that compared the analytic and clinical validity and clinical utility of genomic tests identified potentially important clinical applications of genomics. However, most had significant methodological weaknesses that precluded any conclusions about clinical utility. All in all, the authors “found a very limited body of evidence about the effect of using genomic tests on health outcomes.” So how can we accelerate evidence generation in the face of rapid changes in genomic technologies and applications?
In this paper, we proposed three building blocks to accelerate data collection on clinical utility of promising genomic tests and technologies. The three blocks are risk-sharing arrangements for rapid evidence generation, leveraging existing health-data networks for rapid evidence generation, and stakeholder engagement for rapid evidence generation. A summary of how the model works is presented in the accompanying table and discussed below.
First, for promising genomic technologies we propose temporary coverage coupled with risk sharing agreements (RSA) while clinical utility evidence is being accumulated. The expense of demonstrating the test’s analytical and clinical validity would remain with the manufacturer, but payers and manufacturers share the financial risk of coverage of promising technologies that may or may not prove to have clinical utility. Payers and manufacturers should predefine and agree on what constitutes “sufficient” clinical utility and the timeline for making the evaluation for each individual test. Coverage would be withdrawn when subsequent evidence shows tests do not provide sufficient clinical utility or expanded if evidence suggests sufficient clinical utility. Coverage would enable patient access to these technologies as part of routine care while withdrawal of coverage could lead to substitution, that is, use of alternative tests instead. Longitudinal data on use of genomic technologies and subsequent treatments could be routinely captured by administrative and electronic health record (EHR) databases. Multiple health plans and health-care organizations participating in an RSA for a single genomic technology would allow rapid generation of evidence for that technology among larger populations.
Second, if promising technologies are covered by health plans, electronic health-care data related to their use, and subsequent use of health services, could accrue through routine care delivery and payment, creating a learning health-care system that generates knowledge for evidence-based clinical practice and health system changes. Existing data networks, such as the National Patient-Centered Clinical Research Network (PCORnet) could be leveraged, augmented with genomic information to track the use of genomic technologies and monitor clinical outcomes in millions of people.
Third, endorsement and engagement from key stakeholders will be needed to establish this collaborative model for rapid evidence generation; all stakeholders will benefit from better information regarding the clinical utility of these technologies. This collaborative model can create a multipurpose and reusable national resource that generates knowledge from data gathered as part of routine care to drive evidence-based clinical practice and health system changes.
Given rapid progress in the field, we believe that now is the time to establish a sustainable and structured mechanism for rapid generation of evidence on clinical utility of genomic-based technologies. Rapid evidence generation is achievable by risk-sharing agreements between stakeholders, leveraging health-data networks with augmented genomic data to track the use of genomic technologies and monitor clinical outcomes in millions of people, and engaging stakeholders to drive patient-centered research priorities. This collaborative model can create a national resource in which data gathered in the course of routine care are used to generate evidence on clinical utility of promising genomic tests and technologies.
More details on this proposal can be found in the published paper. As always, we invite our readers to comment and send feedback.
|Current paradigm||Proposed paradigm|
|Evidence of clinical utility||Absent for many genomic technologies as randomized controlled trials are not an economically feasible design in this context||Evidence development is possible through 3 building blocks for a collaborative model: (1) risk-sharing between payers and manufacturers to enable temporary coverage of some genomic tests; (2) leveraging existing data networks with necessary advances for integrating genomic information; and (3) endorsement and engagement from key stakeholders|
|Insurance coverage||No insurance coverage of many genomic technologies due to lack of clinical utility evidence
No market access or low utilization of many genomic technologies for manufacturers/laboratories
|Temporary coverage of ‘promising’ genomic tests that have proven analytical and clinical validity with early evidence of impact for clinical care
Risk-sharing between payers and manufacturers/laboratories
|Efforts to generate evidence on
|Disease-, study-specific efforts.
Numerous networks and consortiums but limited scope and funds and a long time before evidence is produced
|Accruing data real-time among large populations within a single, large health data network
Collaborative model that needs a coordinating center with collaborating institutions that may be funded by public and private agencies including NIH
|Data elements necessary for determining clinical utility||Patient demographics, identifiers||Present in EHR data if used||Present in insurance claims and EHR data|
|Genomic test order, utilization, results||Poorly captured in some EHR systems if used||Advances are needed to (1) better capture tests performed including specific billing codes, (2) test results to be made easily accessible in electronic health data, (3) common data model to aggregate data from different health systems and insurers|
|Subsequent treatments / management||Incomplete records in EHR systems if used||Insurance claims and EHR data contain fairly complete medical and pharmacy utilization records|
|Stakeholder collaboration||Depending on studies||Collaboration between payers, manufacturers/laboratories, provider groups, patients, academics/researchers|
- Page last reviewed:August 14, 2017
- Page last updated:August 14, 2017
- Content source: