One of the biggest challenges facing clinical trials today is recruiting participants in a timely manner, 40% Cancer trials fail to meet accrual targets. Reasons for this include, but are not limited to, lack of awareness and education about options for participating in research trials, limited distance from the study site, and inability to participate due to the impact of protocol requirements on daily life due to illness.
These barriers have helped spark the decentralized clinical trials (DCT) movement catalyzed by Covid-19, which has led many trials to rapidly move to remote participation models out of necessity and brought greater visibility Tell the public about this. The primary goal of the DCT design and strategy is to increase the number of available trial participants, including participants from diverse populations. The DCT model achieves these goals by reducing or eliminating the need for patients to travel long distances to study centers where trials are conducted, and ease the burden of ongoing participation through flexible, local, and home-based data collection techniques and methods.
To help ensure representative diversity in clinical research across therapeutic areas, a bill has recently been introduced into legislation, namely Diversity and Equitable Participation in Clinical Trials (DEPICT) Act. However, achieving diversity goals in research is a multifaceted issue. Not only does it involve new recruiting strategies, it also requires real-time information and the ability to quickly reallocate resources, attract new partners, and quickly adapt to trends and risk-based strategies, making data and real-time analytics an essential element.
The introduction of this legislation comes at an opportune time as the life sciences industry sees digital efforts in clinical development gaining more traction and momentum. These types of technological advancements, such as modern clinical data platforms, risk-based quality management approaches, and advanced analytics have been “emerging” for many years and are now rapidly being adopted and implemented at scale. This critical shift presents a tremendous opportunity for life sciences organizations to increase efficiency, create better medicines, and transform the patient experience.
How Modern Data Infrastructures Influence Trial Decisions
Today, more than ever, clinical development is a data-driven business. As development pipelines move toward more targeted and precise medicines, the number and variety of data sources involved has grown exponentially, including more streams of genomic, biomarker, and physiological measurement data. With the rapid development of clinical trials, many of the existing development processes will not be larger scale to handle the diversification of data available now and will emerge rapidly in the future.
One way to handle these disparate data streams is to use data infrastructure and ecosystems designed specifically for digital experimentation. This starts with a modern clinical and operational data platform that automates data pipelines across source systems and serves as the foundation for all data.
With one source of truth available to clinical teams, operational insights such as diversity metrics and recruitment and retention goals are readily available and shared across extended clinical teams, including outsourcing partners. Researchers can spend less time trying to figure out what’s going on in a trial because the data comes from many different sources, lagging each other, and instead, they can focus on actions to achieve desired outcomes and goals.
Modern data and analytics platforms deliver value throughout the development value chain and shorten development cycles for faster development. Currently, many roles reviewing clinical data still require manual access and download of data distributed across multiple systems, including from outsourced partners.
2019 research on data strategy and transformation by Tufts Center for Drug Development and Research It shows that 75% of the more than 140 life sciences companies surveyed are still using Excel and SAS as their primary method of integrating and combining data. These manual methods result in trial delays, rework, and significant opportunity costs for all stakeholders, including trial participants.
Modern data and analytics platforms address these challenges by automating manual processes including ingesting, mapping, and normalizing data—providing clinical teams with continuous insight to ensure data integrity and monitor participant trends.
Analytics as a core decision-making capability
For operations teams, real-time analysis of key measures such as screening, registration, and protocol compliance enables teams to be nimble and support quick and data-driven decisions. For DCT designs and technologies to deliver on their promise to improve recruitment and retention challenges, including in diverse populations, they must work well within existing clinical data ecosystems.
This includes real-time interoperability with an underlying data infrastructure that combines data from all experimental systems and sources to continuously provide valuable insights. Not only does analytics play a role in identifying the right patients and enrolling them in trials, but it can also adapt to current trends such as storms, flu outbreaks and, more recently, the Covid-19 pandemic.
The Covid-19 pandemic has demonstrated the importance of real-time data and analytics for effective trial operations and collaboration among stakeholders. While some trials were suspended due to site closures in pandemic hotspots, others were able to continue by rapidly moving to more remote data capture, centralized monitoring and targeted analytics to ensure safety and compliance.
An example of using analytics to make quick decisions includes combining publicly available data sources on Covid cases by city with upcoming study dose visits by site. 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 helps keep enrolled patients in the study, avoiding costly delays, while ensuring participant safety, data quality and integrity throughout.
Data-Driven Insights and Decisions Support Trial Diversity Goals
To meet the requirements set forth by the DEPICT Act, accurate and accessible reporting backed by analytics is a strategic imperative for clinical development organizations. Putting automated analytics infrastructure at the center of decision-making is critical to the future state of trials and clinical development to gain optimal visibility and flexibility across different key reporting areas, including diversity data.
Improving recruiting is a two-pronged approach: optimizing the patient experience and making engagement easier, while relying on real-time analytics to enable clinical teams to adjust strategies, make decisions and share lessons learned to optimize their recruiting mix and retention strategies to properly represent the population The goal. Legislation like the DEPICT Act enables researchers to use their data to help treat rare and aggressive diseases in all communities, especially those that have historically been underrepresented in clinical trials, leading to better medicines for all.
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