NESTcc Unveils Frameworks on Quality Data, Methodologies for Device RWE
The National Evaluation System for health Technology Coordinating Center (NESTcc), a public-private partnership, late Wednesday released two frameworks related to data quality and research methods as part of its work to enable and support the use of real-world evidence (RWE) to better understand certain medical devices.The data quality framework is based mostly on electronic health record (EHR) data in the clinical care setting, while the methods framework applies those looking to design, execute and evaluate research studies based on real-world data (RWD). Future iterations of the frameworks will include other data sources for data quality assessment and further RWE examples and best practices.
"The data quality framework serves as a guide for organizations that wish to collaborate with NESTcc to ensure the quality of their data related to medical devices," said Lesley Curtis, chair and professor in the Department of Population Health Sciences at Duke University School of Medicine. "The overarching goal of the framework is to enable the effective capture and use of device-related clinical information, which will ultimately, and most importantly, enable better care for patients."
The 23-page data quality framework describes principles that can guide health systems and other clinical organizations in forming policies and procedures for using RWD and RWE, as well as further information on the characteristics of satisfactory data, data capture and transformation, and best practices in data curation.
“The next iteration of this Framework will include the NESTcc Data Quality Self-Evaluation, a checklist which charts the specific actions that organizations can take to move between stages of the maturity model,” the data quality framework says.
In addition, the 38-page methods framework discusses 12 key components of a study protocol, including details on target population and patient selection, study design and control of confounders, monitoring plans and the statistical analysis plan.
“The methods framework outlines constructs and the importance of the pre-specification of a study protocol to support the rigorous design and execution of RWD research,” said Sharon-Lise Normand, professor of health care policy at Harvard Medical School.
The NESTcc subcommittee developing the framework also developed a template that focused on the justification and clarification as to how confounders, variables related to both medical device use and outcomes, will be controlled.
“Randomization that can control for both measured and unmeasured confounders is one approach. In the absence of randomization, regression, matching, or other statistical tools attempt to provide statistical control of the measured confounders,” the framework says.
Moving forward, both frameworks will be updated based on key findings and lessons learned from NESTcc’s RWE test cases, which address the feasibility of device stakeholders working with RWD sources. The test cases also will help identify areas where NESTcc “could play a role in reducing transaction costs (e.g., contracting, IRB, data sharing agreements, publication policies etc.),” the center said.
NESTcc Data Quality and Methods Frameworks