There is a significant difference between real-world data and real-world evidence. This programme defines RWD as data relating to patient health status and/or the delivery of healthcare routinely collected from a variety of sources. These sources may include registries, electronic health records, medical claims/billing data, patient-generated data from in-home health status, mobile devices and other sources. RWE is defined in the programme as the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD. Furthermore, the programme states that evaluating RWE, in the context of regulatory decision-making, depends not only on the evaluation of the methodologies used to generate the evidence but also on the reliability and relevance of the underlying RWD.
Development and use of data standards for RWD and RWE will play as crucial a role as they do for traditional study data and analysis. For instance, the programme mentions a common format, with an appropriate moniker – the Common Data Model (CDM) – where terminologies, vocabularies, coding schemes and data standards will be evaluated along with other critical aspects of data reliability and relevance. Implementation of ontologies such as LOINC and Linked Data should also play an important role in this programme. Collaboration with internal and external stakeholders is also noted during development of strategies for implementation and finding gaps with any of these data standards and ontologies when developing RWD-/RWE-driven solutions.
Please visit the FDA website or use this link to learn more about this programme. To learn more about some of the data science concepts underlying RWD and RWE, take a look at the wonderful papers across many of the Streams from the recent PhUSE US Connect in Baltimore. Check out the update from David R. Bobbitt on the CDISC Blue Ribbon Commission or peruse some of the other Standards & Governance Stream papers relating to RWD. Check your calendars for any regional Single Day Events with discussions on these topics. Better yet, take it to the next level and volunteer for one of the PhUSE Working Groups and come work with them at the upcoming PhUSE Computational Science Symposium this June.