The Future of Clinical Data Management | A Medidata NEXT Global Series
Medidata’s annual event series—NEXT Global—brought together leaders from across the life sciences industry, including representatives from IQVIA, Sanofi, Amgen, and more, to explore the shifting landscape of clinical data management (CDM). We’ve compiled highlights of these discussions below to help define the future of clinical data management and the implications for data managers worldwide.
How has clinical data capture and management changed given the emergence of decentralized trials? And how should clinical data managers adapt to meet the data challenges of tomorrow? Read on to discover insights from some of the world’s largest data management players.
The Changing Landscape of Clinical Data Management
The life sciences industry is at a pivotal moment in data acquisition. The evolution of technologies has led to a larger number of data sources than ever before. And clinical trials are collecting a wider variety of patient data at a much higher velocity.
There are many promising advantages to these innovations—greater insights into the patient experience, reduction of patient burden, and the ability to capture clinical data from anywhere, at any time. But how do we manage the breadth and depth of this data? We must start by moving past the traditional methods of data cleaning that are unprepared for this data management evolution.
Leveraging a unified clinical data management platform centralizes disparate data in one place and provides a single source of truth for all data collected. Such a solution provides a holistic view of different data types, as opposed to viewing them in isolation—allowing decision makers to derive actionable insights and take corrective action in real-time.
“The evolving data capture landscape brings immense opportunities. It provides greater insights into patient experience and disease by capturing more data at any time through wearables, eCOA, ePRO, and eSource apps used by home nurses and sites. It also reduces the patient burden by allowing data capture from anywhere.”
– Wayne Walker, SVP, Rave Platform Technology, Medidata
The Biggest Challenges Facing Clinical Data Management Teams
CDM teams are faced with emerging challenges as a result of this new world of data management. The continued acceleration of trial decentralization has led to a larger percentage of data coming from outside EDC (electronic data capture). Sensors, wearables, and telemedicine have greatly increased the pool of available data sources that need to be collected and reviewed.
Data managers will also see more implementation of risk-based practices, as well as automation of daily tasks thanks to advancements in machine learning. The use of a “one-size-fits-all” model will no longer suffice as study designs become increasingly complex.
From a clinical trial technology perspective, there is a need to better highlight data issues for CDM teams, and to develop intelligent workflows that streamline the review process, giving data managers a better idea of what data needs to be reviewed—and what data does not.
“One of the most prominent challenges is the need to accelerate data delivery and the processes surrounding it. Things like rapid or transactional level access to data, being able to see the data as soon as it comes in, and managing a large volume of external data sources.”
– Tracy Mayer, Vice President and Global Head, Biostatistics, Clinical Data Management and Connected Devices, IQVIA
How Can Clinical Data Managers Keep Up with this Shifting Landscape?
As the clinical data management landscape changes, so will the role of data manager. There is an emerging need for clinical data management to evolve from a retrospective approach to a proactive one. This requires data managers to incorporate more critical thinking into their roles. Anticipating and adapting a data management strategy depending on the type of data being collected will lead to more efficiency.
These changes point to a larger shift in CDM: the evolution from data managers to data stewards. Data managers must have governance over all the data coming in, as well as managing and reviewing that data. By embracing new tools and analytics, data managers can take a more holistic data governance approach in a world of increasingly disparate data sources.
“Data managers of the future will need to adapt their data management strategy for the type of data being collected, the type of protocol, and the application of risk-based quality control. They will need to have critical thinking skills.”
– Patrick Nadolny, Global Head of Clinical Data Management, Sanofi
Clinical data management must continue to adapt to the high-velocity data sources of today’s clinical trials. There are new and greater challenges being brought on by the evolution of decentralized trials. However, there is also more opportunity than ever to shift the CDM paradigm from a retrospective, one-size-fits all view to an anticipatory and analytical perspective.
Learn more about the changing landscape of modern data management—as told by top industry experts.
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