Overcoming clinical trial complexities with efficient data management

Clinical trials are becoming more complex, potentially leading to lower performance, higher failure rates, longer cycle times, and poorer data quality. While it is promising to see innovations being implemented to digitalize trials, we are, in turn, also dealing with more data, data sources, and complexity. As this volume of data collected continues to grow, new challenges are emerging, such as a 27% increase in endpoints¹ and a 10% increase in trial complexity². Yet despite growing risks, only 57% of sponsors or contract research organisations (CROs) have adopted Risk-Based Quality Management (RBQM)³.

If unresolved, these challenges can impact time-to-market and delay the timely delivery of therapies to patients, impacting their lives. At Medidata, we’re committed to powering smarter treatments and healthier people through digital solutions, and to help address these challenges, we launched Medidata Clinical Data Studio, a transformative AI-powered data management and quality experience to unlock the true power of clinical research data.

Leveraging AI to modernise the data experience in clinical trials with Clinical Data Studio

Clinical Data Studio is a groundbreaking, unified experience that unlocks the true power of clinical research data by giving study teams greater control over the quality of data and the ability to deliver safer trials to patients faster. The technology democratises access to data and offers a unified and seamless experience that allows multi-source data to be integrated, transformed, and analyzed across different stakeholders to reveal insights that matter most.

Built with a user-friendly, no or low code environment, the experience leverages AI to streamline data aggregation, standardization, and management workflows so that multiple users can act on real-time data in ways that reduce burden and risk and shorten review timelines. For example, the experience requires minimal configuration and less programming time, with teams needing as little as three days to get it up and running, and with the time needed to generate complex listings reduced by 90% and KRI configuration reduced by 85%. Time for data reviews is also condensed, with patient profile reviews reduced by 50%. By speeding up various processes such as these, teams can better work as one with enhanced efficiency.

Data quality and patient safety can also be improved as the technology can help research teams identify potential data issues and safety signals to gain a more accurate understanding of patients. This reduces the challenges that may arise from siloed systems and enables data review and reconciliation to be performed up to 80% faster.

One of the first customers to harness the AI-driven Medidata Clinical Data Studio is Eisai Inc., the U.S. pharmaceutical subsidiary of Tokyo-based Eisai Co. Ltd. Eisai opted to include Clinical Data Studio in its clinical trial management platform due to its ability to seamlessly integrate into existing software and break down data silos, while maintaining quality and integrity across data sources. This not only unlocks the ability of Eisai to execute scalable and complex clinical trials, but it also empowers the company to enhance patient experiences. With innovative experiences like this, industry players like Eisai are empowering healthcare stakeholders to overcome the complexities of modern clinical trials, while fostering collaboration to reveal more meaningful and actionable data.

Breaking down silos to accelerate time-to-market

By 2034, it is projected that the global clinical trial market size will reach USD184.61 billion, up from USD120.97 billion in 2024⁴. As competition increases, data quality, trial efficiency, and patient safety can be increasingly at risk if study teams continue to work in silos. We need to break these down and Clinical Data Studio can be part of the solution, paving the way for all users to benefit from all data, maintaining data integrity and value, regardless of the source. Using an AI-powered unified experience as a single source of truth also enables data quality management, which allows issues to be fixed more immediately with automated data and risk surveillance, more harmonized cross-functional working teams, and complete transparency for decisions to be made faster for better patient outcomes.

With healthcare demands at an uptick globally, clinical trials must be accelerated to help ease the burden. Stakeholders in the clinical trials space must act now to ensure a well-rounded and seamless ecosystem with high-quality data, so that patients can get better treatments faster.

Register for our upcoming Clinical Data Studio Webinar to learn more:

https://event.on24.com/wcc/r/4643251/032124EB1DD06A1EDE02CAD0F0AB9D2E?partnerref=Hospital+Health

^{1 Getz K, Smith Z, Kravet M. Protocol Design and Performance Benchmarks by Phase and by Oncology and Rare Disease Subgroups. Ther Innov Regul Sci. 2023 Jan;57(1):49-56. doi: 10.1007/s43441-022-00438-5Epub 2022 Aug 12. PMID: 35960455; PMCID: PMC9373886.
2 Markey, N., Howitt, B., El-Mansouri, I. et al. Clinical trials are becoming more complex: a machine learning analysis of data from over 16,000 trials. Sci Rep 14, 3514 (2024). https://doi.org/10.1038/s41598-024-53211-z
3 CluePoints (2024). Scope of Industry Risk-Based Quality Management (RBQM) Adoption Revealed
4 Future Market Insights (2024). Clinical Trial Market Size is Expected to Grow at a CAGR of 4.3% and Overpass a Value of USD 184.61 Billion by 2034.}

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