Research and development organizations have long relied on single-site clinical trials to verify the safety and efficacy of new products. By designing a trial, identifying likely candidates, pinpointing locations where those patients could participate and selecting subjects, life sciences companies could perform the testing they needed to support the drug development lifecycle.
Until COVID. Once the stay-at-home orders were issued, clinical trials came to a standstill. Bringing patients to a centralized site was nearly impossible. When data could be collected, it was difficult to enter it into a traditional R&D database using standardized practices and tools.
Fortunately, the life sciences industry is made up of skilled problem-solvers. Using ad-hoc processes, data collection systems and integration tools, some R&D companies found a way to succeed by implementing multi-site, decentralized clinical trials. These companies were rewarded with quantifiable value – including greater access to larger numbers of potential participants and multiple, more flexible trial locations.
Consider These Prerequisites to Scale Decentralized Clinical Trials
Yet these experimental multisite trial processes can’t scale. To succeed with decentralized clinical trials, R&D organizations need a more industrialized, rigorous process that combines advanced technology such as AI with state-of-the-art data management capabilities.
With decentralized clinical trials, teams must collect data from different sources, including various types of electronic health records, data from providers and medical facilities, and Internet-enabled medical devices that may exist in professional settings or patients’ homes.
The data quality is likely to be inconsistent. Workers entering the data have different skillsets and levels of training. They will enter data into various systems, instead of one dedicated, well-controlled solution.
Together, these issues create a significant data management challenge. To create accessible, trustworthy, fit-for-purpose data that R&D organizations need to perform high-quality decentralized clinical trials, you need to consider the following data management capabilities:
Can your clinical trial solution connect to different systems?
How will you move data into a centralized data store and transform the data into a common format?
Are you able to ensure that the data collected is of appropriate quality?
Can you determine the data’s lineage, how it was logged and its traceability?
Will the solution be able to understand different reference and location codes?
Can it uniquely identify providers and study subjects?
Move Beyond Costly, Risky Legacy Approaches
Analysts estimate that the cost of bringing a new drug to market averages US $2.6 billion. With that level of investment, efficiency is key. Delays in time to market are simply too costly.
Despite this reality, during the pandemic some life sciences companies supported their decentralized clinical trials with hand-coded data entry and data management solutions. Yet, hand-coding is too great a risk when your system needs to be transparent, defensible and auditable.
To achieve the benefits of decentralized clinical trials, you must replace manual, inefficient and time-consuming coding processes with efficient, reliable AI-enabled data management technology that ensures your clinical trial data is complete, high quality, and accurate.
You also need to embed AI into your decentralized clinical trial processes. Powerful AI-enabled solutions can help you identify appropriate candidates, pinpoint geographic areas where candidates are located, and analyze large sets of clinical data that can lead to new scientific discoveries and treatments.
An integrated, AI-powered data management platform can help R&D organizations scale their decentralized clinical trials in a repeatable, reliable and efficient way. Using AI, teams can make their clinical trial processes completely auditable, defensible and transparent.
Unlike point solutions that lack the robustness to scale, an AI-enabled data management platform allows you to start with one trial site and grow to multiple sites – despite the exponentially larger volumes of data – without losing efficiency or transparency. By enabling a digital-first approach to R&D processes, AI can improve and transform the future of clinical trials for life sciences companies.
Compete and Win by Elevating AI-Powered Data Management
Leading life sciences companies are deploying an integrated, AI-enabled data management platform that enables accessible, trustworthy and fit-for-purpose data for their decentralized clinical trials.
Gilead Sciences, a global biopharmaceutical company dedicated to discovering and commercializing innovative medicines, wanted to help its users discover, understand and trust its development, manufacturing and organizational data.
The company deployed a data mesh framework on Amazon Web Services supported by the AI-powered Informatica Intelligent Data Management Cloud (IDMC). Now Gilead can manage, govern and provide self-service access to data efficiently and cost-effectively across business units.
IDMC also creates a robust pipeline of clean, standardized data that helps Gilead manage diverse data sources for decentralized clinical trials. By integrating this data across disparate systems, IDMC supports enrollment and trial participation across geographically dispersed locations.
It also helps researchers define, understand and manage relationships between trial participants, providers and trial locations. Because IDMC integrates seamlessly with new sources of real-world evidence data, it fuels Gilead’s analytics practice, supporting the design of better, more efficient clinical trials.
“To us, a single enterprise data platform is not just about cost efficiencies or operational efficiencies,” explains Murali Vridhachalam, head of cloud, data and analytics for Gilead. “For us, it’s a competitive differentiation in the industry.”
To learn more about how Gilead used data management solutions to support decentralized clinical trials, read the success story.
