Research as we know it today, done in isolation and seemingly protected from real-world evidence (RWE), may one day soon be the exception vs the norm, as our ability to amass and extract insights from RWE grows. It’s time that researchers and other industry stakeholders acknowledge the power of using different data sources in a complementary manner to tackle some of health care’s most difficult problems.
Real-world evidence (RWE) is playing an increasingly important role in life science research due to its potential to enhance understanding of treatment effects, disease burden, patient safety, and indications for use. These benefits became especially clear when the COVID-19 pandemic created unprecedented urgency in the scientific community and drove researchers to quickly leverage accessible patient data from sources outside of formal research studies.
Using RWE alongside traditional research methods in a complementary manner, while not the traditional approach, ultimately delivered the rapid insights needed to better understand the SARS-CoV-2 virus and accelerate the development of life-saving vaccines and treatments for millions of patients.
Although the pandemic showed that RWE can jumpstart, and even direct, research efforts, conventional peer-reviewed medical research remains at the heart of the investigational journey. These experiments take longer to conduct, verify, and publish; however, they are also carefully executed and well-controlled, offering the scientific rigor and repeatability necessary to confidently align clinical practice with the latest medical evidence.
Both sources of data have distinct advantages and offer an increased level of understanding to ensure patients receive the best possible care, but they mustn’t be thought of as mutually exclusive in surfacing new clinical discoveries.
Real-world Data Considerations
The FDA acknowledges the value of real-world data sources such as electronic health records (EHRs), patient disease registries, wearable devices, genomic data sets, medical claims registries, and social determinants of health (SDOH). Regulators, however, also have very specific requirements around how these sources can be used as evidence to drive conclusions. Specifically, the data must be high quality, meaning they are complete, transparent, nonbiased, and able to be codified to industry standards for broad use.
Once these standards are met, stakeholders can apply data analytics to produce evidence for clinical interpretation. We’ve seen this process work as intended in supporting new drug indications and in surfacing evidence backing the use of a completely new drug, as was the case for the COVID-19 vaccine.
Making real-world data regulatory grade is no easy feat, which creates a hurdle to realizing the full potential of these critical insights efficiently and at scale. Fortunately, emerging technology that leverages AI and natural language processing is helping to accelerate the transformation of real-world data into useable evidence.
As advances in this space continue to mature and become more accessible, widely used, and cost-effective, the power of RWE in driving research and fueling medical discoveries will only grow. Today, we’ve barely scratched the surface in realizing the value of massive troves of health data and there is much more opportunity ahead.
The Need for Trust
The potential for RWE is vast because it is derived from patterns found across a very broad data set, but peer-reviewed literature is a much more controlled exercise from the start. The medical community inherently trusts in these carefully calculated experiments, as do the patients they serve. Implementing the resulting medical evidence into practice is therefore a more straightforward, albeit longer, process.
The quality control measures that define peer-reviewed medical research are widely regarded as the best way of ensuring medical treatments are safe and effective for patients. This rigor is necessary and valuable to ensure evidence can stand the test of time, but today we know this care and stringency must be balanced with the need for speed, particularly when patients’ lives hang in the balance.
The tension between rigor and speed was best evidenced by the spotlight on preprints at the height of the pandemic, which both challenged and reinforced the skepticism stakeholders had about medical research that had yet to be peer-reviewed. RWE became a major feeder for preprint work, with observational studies gleaning insights from COVID-19 patient outcomes, contagion trends, and more. In the end, peer-reviewed research has a more defined role than ever before, after the crucible of COVID-19, but the utility of RWE became much clearer as well.
As an industry, it would be advantageous to foster an environment in which these 2 research methods can work hand-in-hand. For example, it is now possible for RWE to help redirect formal research when appropriate or to retrospectively expand upon previously published research, creating entirely new data models for discovery and therapeutic development.
With these and many other potential applications in mind, trust remains a cornerstone of clinical discovery that can’t be overlooked. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system is commonly used to assess the quality of evidence in new medical research.
Creating a similar system uniquely tailored for RWE could provide the transparent framework the scientific community needs to embrace these data sources. Such a system would help in developing and presenting summaries of evidence and providing a systematic approach for making clinical practice recommendations or offering guidance for more focused research. With developments like this already in the works, we may see RWE rapidly evolve to produce meaningful, vetted insights in which stakeholders can be confident.
As technology and data analytics capabilities advance, so too will the exciting field of RWE. Similarly, the established research process of peer review is evolving with the increased emergence of preprints and new strategies for incorporating discoveries into practice faster. It’s time that researchers and other industry stakeholders acknowledge the power of using different data sources in a complementary manner to tackle some of health care’s most difficult problems.
Researchers often cite a period of 17 years from when new medical discoveries are made to when they are adopted into clinical practice, often requiring several generations of publications. Imagine a COVID-19 vaccine only getting widely adopted in 2038. Modern medicine can do better: COVID-19 was a rich training ground where researchers cautiously set aside some long-held principles for the sake of accelerated discovery, but we can go so much further from here.
Research as we know it today, done in isolation and seemingly protected from RWE, may one day soon be the exception vs the norm, as our ability to amass and extract insights from RWE grows.