Organization Publishes Frameworks for Real-World Evidence Studies of Medical Devices

The National Evaluation System for health Technology Coordinating Center (NESTcc), an initiative of the Medical Device Innovation Consortium (MDIC), has published the NESTcc Data Quality Framework and the NESTcc Methods Framework. Developed by subcommittees of experts from health systems, academia, industry, and the U.S. Food and Drug Administration (FDA), the frameworks provide guiding principles for medical device ecosystem stakeholders to ensure data quality and research methodologies in real-world evidence (RWE) studies.
 
“The publication of the Data Quality and Methods Frameworks marks a key milestone for NESTcc and reflects the commitment of our team and subcommittees to transform clinical data into high-quality evidence,” said Robbert Zusterzeel, M.D., Ph.D., MPH, director of the NESTcc Data Network at MDIC. “We’re excited to share this important work to help inform clinical and regulatory decision-making and support the health outcomes of people using medical devices.”
 
Developed from draft versions circulated for public comment last year, these frameworks will evolve in future iterations to incorporate additional data sources for data quality assessment, real-world evidence examples, and further methodology best practices. Both frameworks build upon existing bodies of work and leverage NESTcc subcommittee members’ experience from similar initiatives, including PCORnet, FDA’s Sentinel Initiative, and MDEpiNet.
 
The Data Quality Framework focuses primarily on the use of EHR data in the clinical care setting, and considers topics including data governance, characteristics, capture and transformation, as well as curation of data. Within the framework, the NESTcc Data Quality Maturity Model addresses the varying stages of an organization’s capacity to support these domains, which allows collaborators to indicate progress toward achieving optimal data quality.
 
“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, Ph.D., chair and professor of the Department of Population Health Sciences at Duke University School of Medicine and interim executive director at Duke Clinical Research Institute. “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 Methods Framework is applicable to many different data sources and defines the key components of a study protocol for the evaluation of medical devices. The document promotes overall study design, outlining principles to follow and evidentiary requirements for core elements including disease and device information, target population and patient selection, study outcomes and procedures, sample size, and monitoring and statistical analysis plans.
 
“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, Ph.D., S. James Adelstein Professor of Health Care Policy for the Department of Health Care Policy at Harvard Medical School, and professor for the Department of Biostatistics at Harvard T.H. Chan School of Public Health. “These design practices for medical device evaluation aim to illuminate the path towards advances in safety, effectiveness, innovation, and transparency.”
 
The NESTcc Data Quality and Methods Frameworks are accessible in the MDIC Resource Library and on the NESTcc Data Quality and Methods webpage.
 
In 2016, the FDA awarded the Medical Device Innovation Consortium $3 million in seed funding to establish the National Evaluation System for health Technology Coordinating Center (NESTcc). The Coordinating Center seeks to support the sustainable generation and use of timely, reliable, and cost-effective Real-World Evidence (RWE) throughout the medical device lifecycle, using Real-World Data (RWD) that meets robust methodological standards and is generated in the course of clinical care and everyday life by patients, providers, or payers, and for the purpose of enhancing regulatory and clinical decision-making.