In pre-digital days, clinical trial data was managed in a paper-based trial master file (TMF). Housed in a physical room, the TMF sought to secure key documents — but it posed challenges for access and collaboration.
An electronic TMF (eTMF) moves this information into a digital domain. While the digital format can make data easier to analyze and manage, digital documents also pose new challenges. Data siloing, software implementation and security issues arise, according to Craig Stedman and Jack Vaughan at TechTarget.
Tomorrow’s data managers will need to understand the challenges of digital document and workflow management.
Artificial intelligence and machine learning unlock new ways to analyze data. These tools can spot patterns and provide insights far more quickly than human data analysts can.
Implementing AI and machine learning poses challenges for data management professionals. Security and privacy concerns top the list, along with the challenges of integrating AI and machine learning into existing infrastructure, writes Laurence Goasduff at Gartner.
Tomorrow’s clinical trial data managers won’t need to build AI and machine learning tools from scratch, but they will need a deep understanding of these tools in order to address security, implementation and use concerns.
Clinical trial research is deeply collaborative. Individual teams generate data and documents, but many breakthroughs are made only when information is shared between teams or across modalities.
Data managers play a key role in facilitating this essential collaboration. Tomorrow’s data managers will need to organize data within modalities to boost the efficiency and quality of clinical trials. They will also need to organize and manage the information ecosystems that make cross-modality collaboration possible.
AI and machine learning can be trained to make predictions based on existing data sets. Predictive analytics provide a number of opportunities in clinical trial work — and a number of learning challenges for data managers.
One use of predictive analytics is for clinical trial site selection. Pfizer recently put predictive analytics to work in modeling COVID-19 infection rates, says executive vice president and chief digital and technology officer Lidia Fonseca. The models helped inform Pfizer’s choice of trial sites for its COVID-19 vaccine.
As predictive analytics tools develop, data managers’ ability to integrate these tools into teams’ workflows will need to develop as well.
To bring its COVID-19 vaccines to market, Pfizer also relied on augmented-reality technology. Augmented reality allowed teams to maintain and repair lab and manufacturing equipment when 80 percent of the workforce was contributing from home, says Fonseca. Staff at manufacturing sites had to maintain strict distancing and other protocols to prevent the spread of infection — which further reduced their ability to collaborate in person.
Augmented reality offers a number of ways to connect and collaborate. The tool can also produce useful data. Data managers who understand augmented and virtual reality tools can better put that data to work.
Data managers face an exciting new world as digital tools continue to develop. By focusing on skill development, data managers can keep pace with these tools, putting them to work in effective and innovative ways.
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