Guest Column | May 10, 2023

Speed Up, Collect More, And Reach Further: Using RWD To Optimize Your Clinical Trials

By Ellen Loo, senior director, product management (real-world data), Freenome


It used to be that collecting and organizing clinical trial data was an overwhelming, expensive, and time-intensive process. With the universal adoption of electronic health records (EHR) across healthcare systems, researchers now have access to digitized, multidimensional patient data.

From a patient’s health status and medical history to longitudinal health outcomes, integrating real-world data (RWD) — data captured during a patient’s normal course of care outside of a controlled clinical trial — is at the forefront of modernizing how clinical trials are designed and executed.

The benefits have been so promising, the FDA now recognizes RWD — and corresponding real-world evidence (RWE) — as valid tools for monitoring the safety of products post-market and, in some cases, for making regulatory approval decisions.

At Freenome, we first integrated the use of RWD with our multiomics platform in the Sanderson Study, a clinical study launched in 2022 for the early detection of multiple cancers. RWD enables us to conduct the Sanderson Study in the most efficient way and fine-tune the precision of our multi-cancer screening test for all populations.

Along the way, we’ve discovered several benefits RWD can bring to any clinical research program.

Optimizing Study Design

Researchers can use RWD to optimize study design, so the study can generate statistically significant, unbiased results with fewer subjects. With a more comprehensive data set provided through RWD for each patient, researchers are able to apply advanced statistical methods to match cases more effectively to controls. These methods can identify certain factors in a control patient that suggest greater propensity for developing particular conditions that mirror a case. For example, a patient undergoing a diagnostic workup for cirrhosis may have a greater propensity for developing liver cancer. Employing these insights in case-control matching reduces bias from confounding variables as well as the number of patients needed for a study.

Assessing Site Feasibility And Accelerating Patient Recruitment

Identifying sites with sufficient populations that meet a study’s inclusion and exclusion criteria is fundamental for any successful recruitment effort. There are specialized data companies and CROs that have developed networks of potential sites and can provide a view of EHR data as well as linked claims data to provide more insight into specific site populations.

Access to EHR data also provides near real-time data on patients, making it easier to identify those who meet study inclusion and exclusion criteria. For example, through its EHR data, a site can identify and track patients with newly diagnosed conditions or those with more challenging criteria, such as a specific stage of disease.

Freenome partners with some of the most advanced data experts to help with patient identification and recruitment by identifying relevant patients at scale. In addition, direct data partnerships between sponsors and sites also foster collaboration and unify efforts to advance innovative clinical research.

Once patients who meet eligibility criteria are identified, sites can use their established communication channels to assist with recruiting patients, further streamlining the process, and optimizing study resources.

Enabling More Efficient And Comprehensive Data Collection

RWD, such as medical histories, medical and pharmacy claims, registry data, and physicians’ notes, can help stitch together a more comprehensive longitudinal view of the patient beyond static and time-limited data points collected in traditional studies. With appropriate patient consent, study sponsors can obtain RWD on study patients directly from sites or through third-party data sources. While the former approach allows for access to richer data that is often only found in unstructured reports and physician notes, the latter is more accessible at scale. Patient privacy can be maintained through the latter approach by leveraging third-party generated tokens to link the RWD to study data. The integration of RWD allows researchers to leverage a more complete clinical picture as evidence. Site staff could manually compile this data, but it’s often impractical or cost-prohibitive to capture such breadth of data at scale.

Enabling Diversity In Study Population

Historically, underserved communities, minority racial groups, rural populations, and those with multiple health conditions have been underrepresented in clinical research. For observational studies, RWD offers a means to include patient populations in research that may otherwise not enroll as frequently in clinical studies. Specifically, RWD captures these patients’ real-world engagement with the healthcare system and when analyzed appropriately — in accordance with the FDA’s guidance for demonstrating data relevance and reliability — can be used as evidence of clinical outcomes. The diversity RWD can bring to observational research can help ensure innovative diagnostics and therapeutics are relevant and available to more diverse populations.

Overcoming RWD Challenges

Despite the benefits, making RWD usable is not without challenges. The quality of RWD is dependent upon the data captured, how it was entered, and the process used to curate and prepare it for analysis. There is significant variation in these methodologies across data sources, which can lead to errors and inconsistencies that can affect the validity of clinical results, particularly when the data is merged from multiple sources.

Managing this requires a deep understanding of RWD informatics and strict standardization protocols for how the data is processed and interpreted. Although some RWD use cases such as assessing site feasibility can tolerate minimal data inaccuracy, others such as supporting statistical analysis for an FDA submission require meeting a high bar for data precision. In the latter case, organizations leveraging RWD will need to ensure they are using relevant, trusted data sources and validated study variables, while maintaining data lineage and quality from collection through curation to analysis. Having clinical informaticists and biostatisticians on your team who are well-versed in navigating the unique attributes of RWD is key to success. If this capability isn’t available in-house, there are a multitude of vendors who offer this expertise.

RWD is becoming increasingly accessible, enabling researchers to improve execution of clinical research programs. As it does, it’s important for life sciences organizations to hone the capabilities necessary to take advantage of RWD in clinical trials, either through in-house development or through partners. Doing so will best position researchers to advance the development of medical devices and therapies that improve health for all patients.

About The Author:

Ellen Loo leads RWD initiatives at Freenome. She is responsible for applying RWD to the development of Freenome’s multiomics blood tests for the early detection of cancer. In addition, Ellen drives Freenome’s data-driven population health management products to help healthcare organizations implement early cancer detection programs. Prior to Freenome, Ellen led precision medicine strategy and programs with the U.S. Oncology Network and McKesson Corporation and served as a management consultant to life sciences companies at McKinsey & Company and L.E.K. Consulting. Ellen holds an MBA from Harvard Business School and a B.S. in computer science from Stanford University.