Importance to FDA

Real-world data (RWD) are data relating to a patient’s health status and/or the delivery of health care routinely collected from a variety of sources. Examples of RWD include data derived from electronic health records (EHRs), medical claims, registry, patient-generated data, and data gathered from mobile devices and other digital health technlogies (DHTs). Real-world evidence (RWE) refers to clinical evidence about the usage and potential benefits or risks of an FDA-regulated product derived from analysis of RWD.

In addition to the Agency’s use of the FDA Sentinel and Biologics Effectiveness and Safety (BEST) systems, FDA is collaborating with the Medical Device Innovation Consortium (MDIC) to build the National Evaluation System for health Technology (NEST) with the purpose of driving the improvement of quality and efficient use of RWD to inform medical device development and evaluation throughout the entire product life cycle. The NEST coordinating center (NESTcc) helps researchers quickly access, link, and synthesize data from different sources across the medical device landscape.

Recognizing the potential value of RWD, FDA is committed to exploring the use of RWE in regulatory decision-making, including its ability to provide fit-for-purpose clinically meaningful information about the safety and effectiveness of drug and biological products. As part of its efforts under the 21st Century Cures Act (Public Law 114-225), FDA established the RWE program to explore the use of RWE in regulatory decision-making. As a result, FDA has published draft guidance and launched supporting projects that will provide insight into how RWD and RWE can play a role in supporting the evaluation of the safety and effectiveness of drug and biological products.

Examples

FDA is advancing use of RWE in several ways:

• Funding RWE demonstration projects, including Randomized Controlled Trials Duplicated using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology (RCT-DUPLICATE). RCT-DUPLICATE attempts to duplicate the results of recently completed randomized controlled clinical trials relevant to regulatory decision-making using RWE, based on health insurance claims data.
• Funding the Source Data Capture from Electronic Health Records (EHRs): Using Standardized Clinical Research Data (OneSource) Project. OneSource is a collaboration between investigators at the University of California, San Francisco (UCSF), Stanford University and FDA. The goal of this project is to develop methods and tools to automate the flow of structured EHR data into external systems and thereby reduce operating costs, save time, and improve data quality for clinical trials. The OneSource project provides an approach to transmit structured data from the UCSF EHR system to a clinical trial electronic data capture (EDC) system. In this approach, the Electronic Case Report Forms (eCRFs) for a phase II clinical trial, (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And molecular Analysis 2 (I-SPY 2 TRIAL)) are populated. OneSource leverages standards from Health Level 7 (HL7) and Clinical Data Interchange Standards Consortium (CDISC) for the capture and transmission of clinical research data.
• Participating in studies focused on understanding how RWD may be able to inform regulatory decisions. One objective is to facilitate the use of RWD to learn about the safety and efficacy of FDA-approved oncology drugs in populations generally under-represented in clinical trials and exploring the potential use of real-world endpoints of response and progression.
• Using the BEST system and health insurance claims data in collaboration with the U.S. Centers for Medicare & Medicaid Services to evaluate the effectiveness of annual influenza vaccines.
• Collaborating with NESTcc to generate evidence across the medical device product lifecycle by leveraging RWE and applying advanced analytics to data tailored to the unique data needs and innovation cycles of medical devices.
• Supporting projects exploring analytic methods that inform RWE, such as Targeted Maximum Likelihood Estimation, for drawing conclusions between the occurrence and causes of an event (i.e., causal inference).
• Conducting projects through the Sentinel Innovation Center, that incorporate data science innovations such as natural language processing and machine learning to expand access to and use of electronic health record data for medical product surveillance.
• Awarding funding for four FDA cooperative agreement grants (from among 31 applications) to explore the use of RWD in generating RWE for regulatory decision-making.
• Evaluating how to use novel data sources to obtain better marketplace, safety, and quality data on cannabis-derived products including cannabidiol (CBD) to help inform regulatory policy development.
• Assessing disparities in occurrence and outcomes of adverse drug events in minority populations using real world administrative claims and EHR.
• Assessing the association between baseline patient characteristics (e.g., organ impairment) and patients outcomes using EHR data.