The clinical research industry strives to make our clinical trials less burdensome and more accessible to diverse patients. What role does/can clinical data management play in making studies more patient-centric?

We have the data acquisition technologies to enable patients to participate in a study in the most convenient way for the patient (at home, on the move, at a site, or a mix of all of those).

But we can be quick to complain when decisions about study design and data acquisition place additional burden and oversight on clinical data management, and we consistently say that integrating data from multiple sources and realizing data integrity and value across various data types is our number one challenge. We’ve seen many studies using more than five different sources – and some more than ten! Clinical data managers are now the stewards of all patient data, not just EDC.

How can we deliver the flexibility for patients to participate in studies in the way that best suits their individual needs? How do we turn this into an opportunity for clinical data management to build studies that acquire data from multiple sources, know the context of that data, and leverage that knowledge to move away from data point cleaning to holistic quality oversight of all the data? And do this while maximizing the effectiveness of our limited resources and reducing overall study timelines?

In this session, we’ll discuss the role of clinical data management in enabling greater patient centricity in our trials, from how we design and build our data acquisition ecosystem, provide the best experiences for patient data capture, how we solve the data integration challenges, and the latest processes and technologies for delivering comprehensive data quality oversight.

Modern clinical trials are facing an era of transformation, primarily fueled by the integration of AI and Machine Learning (ML) in clinical data management. This panel discussion will explore the high-level tools available for enhancing clinical operations and data science tasks in clinical trials. We will focus on the operational aspects such as data integration, visualization, cleaning, clinical reporting, and safety report generation and delve into the state-of-the-art AI tools and techniques that are reshaping clinical trials. The session will focus on four key areas: The AI Landscape in Clinical Trials, Operational Excellence with AI, Data Science Meets Clinical Data, and Case Studies on the implementation of AI tools highlighting challenges and successes. The panelists will explore diverse AI tools, both proven and prototypes, revolutionizing clinical operations, such as data extraction, virtual trials and predictive analytics. The team will provide analyses of AI’s role in streamlining data integration, cleaning and visualization. Panelists will include a review of proven AI applications in generating clinical and safety reports as well as virtual assistants in patient engagement. The panel will also look at how AI is transforming the roles of data managers into data scientists, including the need for Human-in-the-Loop (HITL) training and the evolution of data science automation in clinical trials.

In tandem with AI, the transformative potential of computational modeling and simulation is set to revolutionize clinical trials, introducing a new era of efficient medical device development. This session explores the concept of virtualizing patient cohorts and simulating treatment outcomes, providing a compelling vision of how these advanced digital technologies can minimize risks, cut costs, and expedite the product approval process.

The session will explore the concept of ‘In Silico’ clinical trials (ISCT) and demonstrate the unique integration of AI methodologies with computational modeling. Medical device development has already employed modeling and simulation for product development and early-stage product testing. The latest advances now allow for continued operations in virtual environments throughout the clinical trial stage. This includes generating virtual patient cohorts for treatment arms and synthetic patients for control arms, offering an innovative approach to trial design, and streamlining the assessment of novel treatments.

The convergence of these technologies holds the potential to reshape clinical research dramatically. Speakers will present compelling evidence of progress, including recent collaborative efforts with the FDA in developing a groundbreaking blueprint for utilizing these advanced approaches. This blueprint aims to accelerate development and regulatory phases, representing a significant step towards a more efficient and patient-centric future in device development.

The session promises to be rich with insights, case studies, and tangible examples, offering attendees a glimpse into the future of medical device development and regulatory processes.

Imagine if you could…

• Easily integrate and standardize data from multiple sources; build a complex listing in minutes with no programming;
• Build a complex listing in minutes with no programming
• Generate and post multiple queries in a single click
• Reconcile complex datasets with an AI virtual assistant
• Know the up-to-date status of your data cleaning at a glance
• and more…

No need to imagine. This is the new reality with Medidata Clinical Data Studio.

Clinical Data Studio is a transformative AI-powered data management and quality experience. Created for study teams to work as one. Multi-source data is integrated, transformed, and analyzed to shorten timelines, reduce risks, and ensure patient safety.

Come and see its data surveillance capabilities in action.

Imagine if you could…

• Accelerate operations and improve site relationships with risk-based metrics and data quality analytics
• Move from signal to action faster with dynamic KRI and QTL dashboards integrated with site issue management; uncover hard-to-find data quality issues with AI-powered analytics
• All within the same experience, working with the same up-to-date data as your patient-level data review

No need to imagine. This is the new reality with Medidata Clinical Data Studio.

Clinical Data Studio is a transformative AI-powered data management and quality experience. Created for study teams to work as one. Multi-source data is integrated, transformed, and analyzed to shorten timelines, reduce risks, and ensure patient safety.

Come and see its RBQM capabilities in action.

Patients who participate in clinical trials are rarely connected to their real-world data (RWD). Trial patients’ RWD can supplement active data collection in trials and provide additional insight into the benefits, risks, and costs of treatments to help patients, physicians, and decision-makers.

The process for trial patients’ data linkage to RWD can be implemented in the trial protocol or as an observational linkage sub-study. The steps needed for trial linkage, such as patients’ informed consent and subsequent IRB approval, collecting and storing personally identifiable information (PII) and tokenization can be integrated into the trial standard electronic data capture (EDC) system to minimize the burden on study sites investigators/staff.

In this session, we discuss the effort needed to implement the linkage of clinical trials to RWD and the barriers and opportunities related to that.