Leading Clinical Trial Recruitment Strategies Centered Around Real-World Data - MedCity News

Last year, Walmart expanded its reach into real-world patient data through its entry into clinical trial recruitment. The retailer is joining the growing movement to break down health data silos and offers a solution to barriers that have plagued the industry for years – chief among them – poor recruitment, which is often cited as the main reason clinical trials fail. With drugstore chains Walgreens and CVS Health also launching clinical trial services in recent months, the three retail giants will gain access to significant patient data, including electronic health records (EHRs), lab results, retail pharmacy data and insurance claims . The data can provide valuable insights that lead to more informed and inclusive clinical trial design.

Real-world data helps trial sponsors better understand patients’ disease states, comorbidities, biomarkers, and treatments—important information that allows research teams to define patient groups more quickly and accurately. This can be reflected in a number of ways: When sponsors design studies with overly restrictive inclusion criteria, they may struggle to find enough patients to participate, resulting in prolonged recruitment or the need to modify their protocols. For example, oncology trials often have restrictive cutoff values ​​for laboratory values ​​such as bilirubin, albumin, and neutrophils. However, a recent study found that relaxing these requirements would dramatically increase the number of eligible patients while having little or no impact on efficacy and safety data.

At the other end of the spectrum, when recruitment criteria are too broad, it may be easier to find participants, but cohorts may have certain comorbidities that increase the likelihood of withdrawal from the study. In other cases, it may be useful to exclude certain populations that have shown a negative correlation with results that may reduce the efficacy of the drug to be tested.

Today’s technology gives sponsors and CROs access to anonymized and pseudo-anonymized data that allows them to interrogate patient populations while preserving patient privacy to intelligently build inclusion/exclusion criteria.

Walmart’s approach will likely be to define an optimized cohort and use data to identify and invite patients who might be good candidates for a particular study. The retailer also plans to use its access to underserved communities and rural areas to help expand and diversify clinical research participant pools. For example, a research team investigating a new treatment for hypertension might approach a major retail pharmacy to offer participation to patients currently receiving standard care for an ACE inhibitor. Real-world data can act as a one-way mirror: Study teams can see that a certain number of patients may benefit from a trial, and those patients can see that the trial is open to them. Meanwhile, strict parameters around the use of real-world data preserve the ethical and legal boundaries necessary for patient privacy and data quality.

The visibility of clinical trial availability can be invaluable to patients, especially those with rare diseases who often struggle to find clinical trials that are right for them. A key barrier to participation in research is “lack of awareness of opportunities,” according to a systematic review of studies that report the perspective of patients who have accepted or declined to participate in clinical trials. By partnering with healthcare providers who have access to real-world data, research teams empower patients to talk to their doctors about trials, allowing them to make informed decisions about their participation.

As with any technological advancement, there are challenges associated with using real-world data. Research teams and healthcare providers should continue to prioritize data privacy, an area that will likely receive more attention from regulatory agencies in the coming years. In addition, research teams must contend with the possibility of invisible biases, making it essential that they carefully interrogate the data for distortions. For example, some studies of women’s health may be prone to socioeconomic bias by virtue of data sets that are skewed toward higher-income women who are more likely to receive routine gynecological care.

Despite these challenges, real-world data have strong potential to improve clinical research. These developments in the life sciences industry hold tremendous promise for patients, providers, and sponsors alike. By enabling research teams to design and conduct more inclusive and/or relevant studies, real-world data can improve the standard of care and ultimately improve patient outcomes.

Photo: elenabs, Getty Images

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