Historical clinical research data is a treasure trove of information, just waiting to be discovered, analyzed, queried, and applied to future studies.
Most sponsors and CROs have a huge amount of historical data at their disposal in the form of SDTM datasets, but they haven’t been able to mine it for the valuable insights it contains. Just imagine how much information is stored in the data vaults of global pharmaceutical companies. Even if one starts from the date when digital records were first kept, the amount of information is staggering.
Historical clinical research data is a treasure trove of information, just waiting to be discovered, analyzed, queried, and applied to future studies. Even failed studies can provide valuable insights, in terms of site selection, patient characteristics, and other variables:
- Why did the studies fail?
- What can be learned about sites and patients to prevent future mistakes?
- How can previous studies guide the inclusion and exclusion criteria for future studies?
With an understanding of historical context, clinical researchers can do amazing things, like repurpose molecules for new drugs and predict data queries with assistance from next-generation AI algorithms.
The ability to load historical SDTMs, EDC data, real world evidence, multiomics data, and other valuable information into a single data repository—which can be mined with next-generation search tools using natural language processing and understanding (NLP/NLU) technologies—opens up tremendous opportunities for pharma.
Feasibility, study design, translational science, and discovery teams can all learn from the longitudinal journeys, adverse events, lab information, demographics, and other characteristics of patient cohorts that have undergone similar clinical trials in the past. This historical information is valuable reference material for creating the clinical trials of the future.
The Time to Use Historical Clinical Trial Data Is Now
Mining historical clinical research data has been prohibitive until now, and most pharma executives are unaware that the technology that makes historical clinical data analysis possible already exists.
Saama is already helping pharma companies apply historical data to studies related to Infectious disease, oncology, and other therapeutic areas. These clients are getting tremendous results, in the tangible form of fewer non-performing sites, lower Screen Failure Rates, fewer data queries, and accelerated studies overall.
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