Industry
Highlights
- A major shift in the clinical research landscape is evident given the evolving data complexity and multi-source patient data from imaging, labs, wearables, sensors, devices, eCOA, ePRO, DCTs, and genomics.
- Traditional and human-intensive CDM processes are insufficient to handle such high-velocity, multi-dimensional data, and meet the accelerated study closure and interim analysis expectations.
- The harmonization from multiple heterogeneous sources, and contextual interpretation of such diverse datasets presents substantial hurdles in maintaining consistent data quality and reliability.
- These challenges require accurate methods and innovative approaches to ensure data quality and data reliability in clinical trials.
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Gaps in current CDM
Legacy clinical data management (CDM) processes, though structured, are laborious, rigid, and self-limiting to drive efficiencies.
Traditional CDM has long centered around predefined clinical trial data collection, manual validation checks, and database lock and statistical analysis. Legacy CDM systems relied on processes with high manual dependency, increasing trial turnaround time and cost. They also attracted a lot of queries or required extensive discrepancy management. Though the structured approach ensured regulatory compliance and data integrity, it was largely reactive, focusing on cleaning data after it was collected rather than enabling risk-based approaches and real-time insights.
With the rise of diverse data sources such as wearables, electronic patient-reported outcome (ePRO), and real-world evidence, the limitations of traditional CDM have become increasingly clear. It lacks the agility, scalability, and analytical depth needed to process high-volume, high-velocity data or assure its veracity. This gap has created an urgent need for transformation toward a smarter, proactive and insight-driven approach.
Modernizing the ways of working through automation, integration, and analytics is crucial for a clinical data science approach.
Solving the CDS shift
Clinical data science (CDS) has emerged as a smart framework to enable the integration of complex data streams and multi-omics, driven by scientific reviews and technology (see Figure 1).
Figure 1: Drivers of clinical data science
Figure 1: Drivers of clinical data science
As patients and regulators increasingly veer towards technology enablement, there is a need for scientific and operational data review using smarter, tech-powered, proactive data-driven risk-oriented approach from study design to closeout. AI-driven models enabling early risk detection, automated data reviews, and smarter trial designs have been redefining the speed and precision of clinical development.
Data from studies now drives intelligent analytics—from descriptive to prescriptive—enabling deeper insights.
During a study setup, the integration of multi-vendor data using advanced tools and standardiszed metadata enables seamless interoperability and consistency within the ALCOA-CCEA data integrity framework. These practices serve as foundations for fueling the use of artificial intelligence.
Likewise, during study conduct and close-out, the availability of data for real-time analysis and interpretation helps in informed decision-making.
Traceability and transparency of data from the point of inception to consumption are crucial and audit trail reviews play a pivotal role here. Regular data reviews, coupled with risk-based approaches, help identify discrepancies, ensuring adherence to protocols, reinforcing data security, and, most importantly, maintaining data integrity.
Strategic drivers of CDS
The shift from CDM to CDS involves upskilling teams, embracing new approaches, ways of working, and new technologies for scientific and comprehensive data analysis and interpretation.
Successful technology adoption hinges on workforce capabilities and aligning them with streamlined processes required to support clinical data science.
Bridging the skill gap requires parallel investments in upskilling talent to fully harness the power of AI frameworks and data analytics.
The future clinical data science organisation will feature a cross-functional team (see Figure 2).
Figure 2: Future CDS organization
Figure 2: Future CDS organization
Cross-functional CDS teams are expected to master clinical protocols and use data science tools such as machine learning, statistical modelling, data visualization, and programming in R and Python for ensuring data quality (see Figure 3).
Figure 3: Skillset mapping for CDS roles
Figure 3: Skillset mapping for CDS roles
The evolution of clinical data science (CDS) emphasiszes instream, real-time data review with a focus on critical data rather than exhaustive 100% review, aligning with the ICH E6 recommendation for risk-based approaches.
During a study setup, identifying critical processes and data (CP and D) to classify datapoints as critical or supportive enables downstream risk-based approaches focused on essential data. Adhering to data standards ensures fit-for-purpose governance and consistency.
Few examples where technology can be leveraged in automating study build include study builder agents, predicting end-to-end (E2E) standards adherence, and enhancing insight generation through CDS analytics tools. Intelligent automation of data transfers strengthens efficiency and compliance.
During the study conduct, instream data review guided by risk-based approaches, data analytics—data pattern recognition, and Agile SCRUM for milestone reviews—enables early risk detection and faster decisions.
Technology adoption is central to this transformation. Advanced technologies like next-gen AI can automate routine tasks, empowering clinical data scientists to focus on science-based, risk-based data reviews, and obtain high-value insights through advanced analytics and visualizations.
Together, the synergy of evolved talent, optimiszed processes, and advanced technologies fuels the transition from CDM to CDS, unlocking scientific value and delivering high-quality clinical outcomes.
Future vision
The vision for a CDS organisation focusses on the seamless integration of data, people, process, and technology.
Transforming CDM into CDS is not just a technological evolution, but a paradigm shift in how data is valued, accessed, and acted upon. The role of data professionals in clinical trials is transitioning from managing quality to driving insights.
Once the data foundations are naturaliszed, advanced technologies like AI, machine learning (ML), and generative AI (GenAI) will automate routine tasks, enabling data-driven insights and predictive analytics (see Figure 4).
Figure 4: How CDS transforms clinical trial
Figure 4: How CDS transforms clinical trial
Skilled professionals will leverage these tools to focus on high-value scientific tasks and informed decision-making. Robust processes will ensure compliance, data integrity, and proactive risk management. Seamless data integration from diverse sources will enhance interoperability and consistency. Automated audit trails and real-time analytics will maintain transparency and adherence to protocols.
This holistic approach will drive innovation, efficiency, and high-quality outcomes in clinical trials.