Clinical trials are seen as the benchmark for deriving clinical data demonstrating the safety and effectiveness of medical devices. However, they are not the sole method of accumulating clinical evidence. Real-World Data (RWD), which typically comes from routine healthcare or non-interventional studies such as registries, offers another viable evidence source. By collecting and analyzing RWD, manufacturers can generate Real-World Evidence (RWE) that supports claims regarding the safety and effectiveness of medical devices, crucial in regulatory decision-making.
The U.S. Food and Drug Administration (FDA) defines RWD as data related to patient health status and/or healthcare delivery that’s routinely gathered from various sources including electronic health records, medical claims, and product or disease registries. RWD may also encompass data from digital health technologies. The concept extends to usage data from medical devices during routine clinical settings, even if such usage deviates from approved indications. Systematic analysis of such data can potentially advocate for expanded device indications or inspire new innovations.
RWE, as defined by the FDA, is the clinical evidence regarding the use and potential benefits or risks of a medical product derived from analyzing RWD. It thus plays a key role in justifying the safety and effectiveness of medical devices.
In 2017, the FDA issued guidance on using RWE in regulatory decision-making for medical devices. This encompasses evaluating whether the collected RWD (and resultant RWE) are sufficient for decision-making purposes. Major factors in this assessment include the relevance and reliability of data. Relevance relates to the data’s depth in capturing device usage, exposures, and outcomes in the target population, and its amenability to robust clinical and statistical analysis. Reliability hinges on how the data is collected and assured, looking at factors like data entry timeliness and adherence to verification procedures.
FDA emphasizes the need for this data to be pertinent to specific questions about the device and captured in a manner ensuring quality and integrity. The quality of RWE can be enhanced using electronic data capture (EDC) solutions, like Greenlight Guru Clinical, that facilitate high-standard data collection from various sources including surveys and patient reported outcomes.
MedTech companies can deploy high-quality RWE at various stages of the product lifecycle—ranging from prototype testing and clinical trials through to market release and post-market surveillance, influencing numerous aspects like study designs, trial comparisons, and post-approval studies. RWE can also support petitions for device reclassification, public health monitoring, and signal expansions for device use or innovation.
While RWE presents numerous advantages such as real-world device performance insights and more versatile patient engagement methods, several potential drawbacks must be considered. For instance, RWD might lead to confounding due to biased sample comparisons, may suffer from quality issues or data gaps, and lacks standardized formatting which impacts reproducibility.
Comprehending and preemptively addressing these challenges—through meticulous study protocol and data analysis planning—is crucial whether RWD is collected retrospectively or prospectively. Moreover, essential quantifiable data conducive for statistical analysis should be emphasized over potentially misleading free text data. Effective eCRF design ensuring guided data entry may also enhance data quality and relevance.
Ultimately, for MedTech companies looking to navigate the complexities of collecting and utilizing RWD and RWE, tools like Greenlight Guru’s EDC solutions offer specialized functionalities tailored for the industry, supporting meticulous compliance with both U.S. and EU regulatory standards. By embracing such integrated solutions, companies can optimize the collection and management of crucial data that enhances device safety, effectiveness, and innovation in the medical device field.
