Clinical Data Management: The Past, Present and Future
Artificial intelligence and machine learning are poised to transform clinical data management (CDM). Experts at the PPD clinical research business of Thermo Fisher Scientific elaborate on where CDM is heading.
We rely on internet search engines to comb through and find relevant information with ease, but the same standard is yet to be applied to clinical data. However, with recent technological advancements in artificial intelligence (AI) and machine learning (ML), the possibility of searching through clinical data — in a similar way to internet search engines — presents exciting opportunities in clinical data management (CDM).
In order for CDM to evolve into a Google-like approach, modernization of data collection and consolidation is required. Progress has already started, and movement from paper-based trials to electronic data capture, digitally-enabled trials and proliferation of data sources — such as wearable devices and electronic health records — are already commonplace. But this is only the beginning.
Although the technology is ready to be utilized, there are still challenges that companies must overcome. Some of these challenges include:
- Disparate data sources: Ease of use is not yet the standard for CDM. Traditional data management typically requires searching for data across multiple unstructured data formats. Consolidating structured data in databases is crucial for achieving a more seamless experience.
- Data collection: With advances in technology, the sheer volume of data collected continues to rise exponentially. Traditional practices of collecting and analyzing data are no longer practical, and new methods — such as data visualizations and risk-based data cleaning — must be implemented.
- New technology: The technology for fully digital CDM already exists, but an understanding of AI/ML technology and how it can be implemented is required for success. CDM teams must take the lead in defining how technologies are used and what the future of data management will look like.
Without a doubt, the integration of AI and ML is set to drive the digital revolution of CDM, but there are still many hurdles that companies face for the technology to bring about significant benefits.
The PPD clinical research business of Thermo Fisher Scientific specializes in providing ongoing collaboration with clients to develop customized CDM approaches, such as advising on study design, managing internal and external interdependencies, and deploying AI/ML technologies.
To delve deeper into the ongoing challenges of clinical data management and ways to incorporate a modern approach to end-to-end data life cycle, download our CDM article authored by two PPD Functional Service Partnership (FSP) solutions experts.