The results of a clinical trial are only as good as the data collected from it. Unclean or irrelevant data can lead to incorrect conclusions and endanger patients, not to mention the reputations of clinical research organizations (CROs) and sponsors. In many ways, data managers are the unsung heroes of clinical trials, tracking what needs to be studied and ensuring the numbers are accurate.
However, the data management profession has become increasingly complex over the past few decades, and members of that profession are creating more clearly defined roles within their organizations.
In the recent past, clinical data managers (CDMs) have taken on an endless stream of tasks and organizational responsibilities. Almost anything related to data, technology, security, or similar fields was filed under the data manager role — leaving a handful of employees completing the work of an entire department.
“Our tasks have increased...we are much busier than we were before,” says Michael Goedde, VP of global data operations at PRA Health Sciences.
Now more organizations are evaluating what a clinical data manager should be responsible for. In the process, they are stripping away these extemporaneous tasks and freeing up time so CDMs can really focus on data quality.
The role of clinical data managers in contract research organizations is also changing as companies embrace more internal technology and agile processes. Outdated systems had put limits on what these professionals could do; rapid technological development has expanded not only their responsibilities but also their abilities.
With the right processes, CROs are on the verge of creating a snowball effect in improved data management performance.
One of the many hats that clinical data managers wear is that of project manager. Data managers work alongside multiple people across the research organization to ensure the trial goes smoothly and all of the data collected is clean and valuable.
“I collaborate with many different functions; internally I have day-to-day interactions with project managers, study managers, biostatisticians, statistical programmers, Quality Assurance,” says Monica Pimazzoni, director of clinical data management at contract research organization CROS NT. “External relationships are also a key part of my role, working in a CRO I am in regular contact with sponsors, clinical CROs, third party vendors (e.g. ePRO vendors, software developers etc).”
Data managers are increasingly at the table for discussions related to technology use, trial timelines and patient experiences. Not only are they consulted before teams move forward with new trial options, but some are also actively leading the projects and following each step closely.
“You can hire talent to handle each of the various data management activities, but you still need someone to pull it all together,” says Kunal Sampat, clinical operation director at medical device company Ceribell and host of the Clinical Trial Podcast. “This is why a data manager with project management experience is priceless.”
As clinical data managers work in tandem with project managers, they are stepping into a larger role within a CRO. They are becoming leaders and letting their teams and clients know what is possible, allowing their organizations to modernize after decades of working with the same processes and software systems.
“The key role of data managers is to find new and less time consuming processes to get systems, vendors and study data ready for sharing,” the team at the Avigna Clinical Research Institute writes. “Data managers should be aware of how study data should be processed, how systems should be configured and validated to get true data that can fit the protocol endpoints.”
One of the biggest technological advancements in CROs is the use of artificial intelligence (AI) and machine learning (ML) to analyze data and track the performance of clinical trials.
“Powerful ML technologies have the potential to monitor data as it is generated—identifying issues and inconsistencies as trials are ongoing,” says Dr. Jennifer Bradford, director of data science at biometrics contract research organization Phastar. “ML technologies could be used to flag certain changes, potential issues or anomalies, directing the medical team to take any necessary action.”
As more CROs invest in machine learning tools, some professionals are looking ahead to the future. They see the current use of AI as the natural first step of combining technology with clinical trials while acknowledging that there is so much more that these tools can do.
“Machine learning can help improve the operation of our systems and the quality of the data that come from our systems,” says Dr. Wes Gilson, senior director of MR business development for Siemens Healthineers. “Beyond that, what can we do with the broader context of data being generated in the healthcare system? Where are opportunities for AI to help us really make actionable the large amount of data that we have now?”
Along with pushing for new ways to use AI and ML in data management, other industry leaders are planning out how technological adoption will change the role of data managers. A large part of the day-to-day tasks of data professionals within CROs will involve guiding these robots and managing software applications.
“There are some good examples in finance for instance where the traditional data manager or data administrator—who would create a program and look at the data [themselves]—now just manage how the algorithms are running as they manage the data,” says Francis Kendall, head of oncology programming, biometrics and oncology R&D at AstraZeneca. “Why are we not at that stage yet?”
The COVID-19 pandemic served as a catalyst for change across the clinical trial field and the role of data managers is no exception. The resurgence of remote and decentralized clinical trials means new changes (and challenges) in data management.
Fortunately, the technology to conduct remote trials was already available and in use before in-person trials were put on hold. Tanya Rodante, director of communications at diabetes management software platform Glooko, credits the proven value of wearable technology as a key to advancing remote clinical trials. Even consumer-facing technology like Fitbits and Apple Watches have proven valuable for monitoring patients and making remote clinical trials more accessible. Rodante cites a report from Stanford that 80 percent of doctors believe self-reported data from patient health apps is clinically valuable.
Even before the pandemic, there were calls for better remote trials. The Society for Clinical Data Management highlights decentralized clinical trials as a major driver affecting the role of data managers in a 2019 reflection paper. In the document, the society explains that the diminishing pool of trial participants is driving more trial developers to offer remote options where patients don’t have to travel. As a result, data managers have to reconcile multiple sources of data — from information collected by doctors to the self-recorded data from the patients themselves.
“Data processing would be focused on data consolidation from diverse technologies and sources rather than data cleaning,” the report states.
Technology leaders are already working to create improved platforms for data managers so they can better run and evaluate remote clinical trials. This can streamline their work and allow them to remain in a high-level project leadership role.
Today’s clinical data managers have an opportunity to change how they work and the technology they use. They can use the COVID-19 pandemic to lobby for better resources, investment in AI and ML tools and for a clearly defined role. The data manager position doesn’t have to be a catch-all for anything vaguely related to information. Instead, these experts can move CROs and sponsors into the future of clinical trials.
For clinical data managers, having tools that facilitate decision making is crucial.
That’s exactly what the TA Scan clinical business intelligence platform does. It helps research teams extract insights from a variety of data sources, which in turn accelerates planning and implementation.
To learn more, have a look at how TA Scan helped one pharma company save millions of dollars per year by streamlining its investigation into primary and secondary outcomes.
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