Lack of diversity in clinical trials isn’t just a recruiting problem—it’s a data problem
Publication: MedCity NewsPublished: May 24, 2022
To achieve diversity goals in research requires a multifaceted approach that makes clinical trial participation easier and uses analytics to help identify the right patients to recruit for a clinical trial.
One of the greatest challenges facing clinical trials today is the timely recruitment of participants, with 40% of cancer trials failing to reach targets for accrual. The reasons for this include, but are not limited to, a lack of awareness and education about the option to be in research trials, limited proximity to research sites, and an inability to participate due to the impact of the protocol requirements on daily life already burdened by disease.
These obstacles have helped energize the decentralized clinical trials (DCT) movement catalyzed by Covid-19, which caused many trials to rapidly pivot to remote participation models out of necessity and brought greater visibility about this problem to the general public. The primary goals of DCT designs and strategies are to increase the pool of available trial participants including those from diverse populations. DCT models achieve these goals by reducing or eliminating the need for patients to travel long distances to the research centers that run trials and to ease the burden of continued participation with flexible, local and home-based data collection technologies and methods.
To help ensure representative diversity in clinical research across therapeutic areas, a bill has recently been introduced into legislation, the Diverse and Equitable Participation in Clinical Trials (DEPICT) Act. However, achieving diversity goals in research is a multifaceted problem. It not only involves new recruitment strategies but requires real-time information and the ability to rapidly reallocate resources, bring on new partners, and adapt quickly to trends and risk-based strategies, making data and real-time analytics essential ingredients.
This legislation comes at an opportune time as the life sciences industry sees digitization efforts in clinical development gain greater traction and momentum. These types of technical advancements, such as modern clinical data platforms, risk-based quality management approaches and advanced analytics have been “emerging” for many years and are now being rapidly adopted and implemented at scale. This pivotal shift presents a tremendous opportunity for life science organizations to gain efficiency, create better medicines and transform the patient experience.
How a modern data infrastructure impacts trial decision-making
Today more than ever, clinical development is a data-driven business. As development pipelines shift toward more targeted and precision medicines, the volume and variety of data sources involved are increasing exponentially including more genomic, biomarker and physiological measurement data streams. With clinical trials evolving so rapidly, many of the existing processes in development will no longer scale to handle the diversification of data now available today and coming rapidly in the future.
One method of wrangling these diverse data streams is with a data infrastructure and ecosystem designed for digital trials. This begins with a modern clinical and operational data platform that automates the data pipeline across source systems and serves as a foundation for all data.
With one source of truth available for clinical teams, operational insights like diversity metrics versus goals for recruitment and retention are readily available and can be shared across extended clinical teams, including outsourced partners. Researchers can spend less time trying to figure out what has happened in a trial as data comes from many different sources that lag behind each other, and instead, focus on actions to meet desired outcomes and goals.
A modern data and analytics platform provides value across the entire development value chain and delivers improved cycle times that speed development. Currently, many roles that review clinical data are still tasked with manually accessing and downloading data spread across multiple systems, including those from outsourced partners.
A 2019 study on Data Strategy and Transformation conducted by the Tufts Center for the Study of Drug Development showed that 75% of the 140-plus life sciences companies surveyed are still using Excel and SAS as primary methods of integrating and combining data. These manual approaches cause trial delays, rework and significant opportunity costs to all stakeholders including trial participants.
A modern data and analytics platform addresses these challenges by automating manual processes including ingestion, mapping and standardizing data—providing clinical teams with continuous insights to ensure data integrity and monitor participant trends.
Analytics as a core decision-making competency
For operations teams, real-time analytics on key measures such as screening, enrollment and protocol compliance enable teams to be nimble and support fast and data-driven decision-making. For DCT designs and technologies to deliver on their promise of improving recruitment and retention challenges, including those in diverse populations, they must work well within an existing clinical data ecosystem.
This includes real-time interoperability with a foundational data infrastructure that combines data from all trial systems and sources, delivering valuable insights continuously. Not only do analytics play a role in identifying the right patients and recruiting them to enroll in a trial, they also allow for adapting to current trends such as storms, flu outbreaks and more recently, the Covid-19 pandemic.
The Covid-19 pandemic showcased how critical real-time data and analytics are for effective trial operations and collaboration across stakeholders. While some trials were paused due to site closures in pandemic hot spots, others were able to continue by making rapid pivots to more remote data capture, centralized monitoring and targeted analytics to ensure safety and compliance.
Examples of using analytics for rapid decision-making include combining publicly available data sources about Covid cases by city with upcoming study dosing visits by sites. By comparing these two data streams, clinical teams were able to effectively deploy resources including home healthcare workers to areas with the highest number of Covid cases. This helped keep enrolled patients in the study and avoid costly delays while ensuring participant safety and data quality and integrity from start to finish.
Data-driven insights and decision-making support trial diversity targets
To meet the requirements posed by the DEPICT Act, accurate and accessible reporting supported by analytics is a strategic imperative for clinical development organizations. Putting an automated analytics infrastructure at the center of decision-making is critical to the future state of trials and clinical development in order to gain optimal visibility and flexibility into different critical reporting areas, including diversity data.
Improving recruitment is a two-pronged approach: optimizing the patient experience and making participation easier, while in parallel, relying on real-time analytics to allow clinical teams to pivot strategies, make decisions and share lessons learned so they can optimize their mix of recruitment and retention strategies to meet the goals for appropriate representation of populations. Legislation like the DEPICT Act empowers researchers to leverage their data to help cure rare and aggressive diseases across all communities, especially those historically underrepresented in clinical trials, resulting in better medicines for all.