Clinical data management must evolve to keep pace with the new data acquisition landscape

Over the last decade, clinical trials have evolved to include an ever-expanding array of data sources (e.g., wearable devices or sensors), increased data volume and precision, risk-based quality management techniques, decentralised clinical trials, and adaptive designs. Clinical trials have also been placing greater emphasis on the patient experience and value differentiation. These factors have led to greater complexity in clinical trial designs and consequently, an exponential increase in the volume of data captured. In fact, the amount of clinical trial data has risen seven-fold in the last 20 years¹ and a typical Phase III study now generates an average of 3.6 million data points². Phase II and III protocols currently involve 263 procedures per patients, supporting approximately 20 endpoints³.

These dramatic changes in the data acquisition landscape bring new challenges for data management and monitoring teams. Aggregating and reconciling data from multiple and new sources using systems not designed to handle them is burdensome and unscalable. These data sources also increase silos, which makes it harder for study teams to access clean, reliable data, impacting the decision-making process.

Clinical data management needs to be transformed to ensure patient centricity

With more data being collected directly from patients, including wearable devices, electronic clinical outcome assessments (eCOA), and telehealth platforms, monitoring and data management processes need to adapt to how and where the data is collected, and scale to detect and clean issues without additional resources. There is thus an urgent need to shift our thinking from reactive strategies to proactive planning and technology-based solutions to replace query and listing-based reviews of trial data.

In traditional clinical trials, clinical data managers are tasked with manually identifying trends and data anomalies via data listings, dashboards, and home-grown tools that often lack interoperability. However, modernised processes and technologies are increasingly available to support clinical data managers as they adapt to the new realm of data management in clinical trials, including benefits associated with risk-based quality management methodologies guided by RBQM/ICH E6 recommendations.

The challenge for those in data management leadership and those working directly on trials is how to best incorporate an ever-increasing list of data sources, novel data types, analytic tools, and existing personnel in a risk-based environment to execute the core function of data management, which is ensuring that clinical data is collected ‘fit for purpose’. As mentioned above, modern clinical data managers will have to be increasingly proactive when planning and deploying interoperable technology-based solutions to replace the manual query and listing-based reviews. In short, clinical data managers are becoming the nucleus for bringing together all of the disparate data to harmoniously communicate a complete patient’s journey.

Gain a full view of the patient with a unified patient-centred platform

At Medidata, we believe that studies can be more efficiently conducted with a unified view of patient and study data. Medidata’s Patient Cloud is a suite of powerful solutions that makes it simple and engaging for patients to participate in any clinical trial — allowing clinical trials to be easier, faster, and produce better results. Data from any source is aggregated and standardised in near real-time into a centralised study design that provides a reconciled view of the patient in chronological context.

Medidata Patient Cloud solutions such as eConsent, eCOA, and Sensor Cloud can ingest and analyse data while easily integrating with Rave EDC, RTSM, and the Medidata uni­fied platform, giving sponsors and CROs a broader view of the entire patient experience. This end-to-end approach offers:

• Real time data insight for immediate decision making
• Effi­cient and seamless mid-study changes
• Automated and streamlined processes
• Eliminated startup costs by unifying all on one system

The future of data management has arrived

What is clear is that the old ways will not work in the new, modernised world of clinical data management. Clinical data management organisations will need to prioritise upskilling clinical data managers and dedicate time and resources to expand their analytical mindsets, clinical skills, and risk and mitigation processes, as well as their understanding and use of real-world evidence, data trends, and new clinical endpoints based on alternative EDC designs from decentralized trials.

If clinical data management commits to this cultural shift and we augment our daily activities with a new data governance toolkit that includes specialised tools for real-time analytics, data managers will be better equipped to provide engagement from protocol design to data visualisation development, as well as with patient-centric technology solutions in any phase of a decentralised trial.

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^{1, 2 & 3 Tufts CSDD Impact Report, 2021.}

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