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, clinical data managers are the unsung heroes of most trials, tracking what needs to be studied and ensuring the numbers are accurate.
The Data Manager Role is Getting a Much-Needed Overhaul
What does a clinical data manager do? 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.
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 CROs 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 Outdated systems had put limits on what these professionals could do; rapid technological development has expanded their responsibilities and abilities.
With the right processes, CROs are on the verge of creating a snowball effect in improved data management performance.
Data Managers Are Also Project Managers
One of the many hats that your average clinical data manager wears is that of a project manager. Data managers work alongside multiple people across the research organization to ensure the trial goes smoothly and that 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, formerly 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.
“Clinical data managers work closely with other groups within a clinical trial to ensure that data is collected, managed and reported in a timely manner, accurately, and securely,” says Narayan Lebaka, senior director of clinical data management at Inspirna. “The clinical data manager must understand good clinical practices (GCP), protocol, protocol deviations, metadata, basic SDTM mapping, programming and Excel.”
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,” according to the team at the Avigna Clinical Research Institute. “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.”
Data Managers Are Pioneering the Use of Machine Learning
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,” writes Jennifer Bradford, Ph.D., 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 research scientist Wes Gilson, senior director of MR business development at 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?”
Remote and Decentralized Clinical Trials
In the years following the lockdowns and restrictions of the COVID-19 pandemic, CROs have continued with remote and decentralized clinical trials. These researchers have been able to move away from implementing emergency protocols to embracing new opportunities in data management.
For example, more clinical trials are embracing patient-focused remote protocols instead of site-focused options, explains Ed Miseta, chief editor at Clinical Leader. “That move will require building a new set of tools to enable the transition,” he writes. Miseta provides the example of using novel digital tools to find and screen patients, and then again implementing technological systems to sign consent forms. As more companies employ decentralized trials, these technological investments will become standard. Clinical data managers will be expected to know how to implement the tools in trials.
Additionally, clinical data managers are also changing how they manage remote teams. Juggling several researchers who are working across the country is no easy feat. Not only does remote management require increased communication and planning, but it also requires leaders to take steps to prevent overwork or burnout.
“Micromanagement does not work in remote work,” says Patrick Nadolny, global head of clinical data management at Sanofi. “Managers have no choice but to empower staff to work more independently and autonomously.” Nadolny shared these thoughts at the Society for Clinical Data Management leadership forum on the changing landscape of research and development.
Burnout is dangerous to any company, but it can be deadly in the world of clinical trials. Team leaders can’t afford to overwork employees to the point where the data is incorrect and unusable.
Today’s clinical data managers are changing how they work and how trials are developed. 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 trial operations through better people management and technological investment.
The Strategic Data Manager’s Toolkit
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.