How AI is Changing Trial Feasibility, Diversity, and Recruitment

Of all the barriers to success in clinical trials, recruitment is arguably the biggest. Clinical trial recruitment is expensive, making up to one-third of the total cost of trial costs. Clinical recruitment is also slow and inefficient; and when you consider the importance of ensuring a diverse patient base for clinical research, that hurdle gets even higher. 

Recent studies show that while African Americans comprise 20% of multiple myeloma patients in the US, they make up merely 4.5% of clinical trials. This example indicates real challenges in engagement of diverse populations, comprehensive research design, and trial outcome efficacy. Applying AI to medical records can pinpoint diverse patients for clinical trials in minutes not months, making it a game-changer for sites that deploy this technology. 

Pinpointing Patient Data Points 

AI extracts symptoms, diagnoses, treatments, genomics, lifestyle, demographics, and thousands of other data points from medical files and sorts the fragments into patient profiles, all while protecting private identifying information. As sponsors increasingly demand diversity metrics as part of the RFP process, recruiting a wide range of patients also directly affects sites’ win-rate and trial feasibility. AI effectively identifies the right mix of patients eligible for a specific study and allows sites to show sponsors that their databases align with study needs. The result is a larger and more tailored pool of potential volunteers delivered exponentially faster.

Optimizing Recruitment Efforts with the Help of AI

AI tools that can help optimize your recruitment efforts are part of the best clinical trial management and eClinical solutions available. Learn more by viewing our webinar “How Artificial Intelligence is Changing Trial Feasibility and Recruitment” and downloading our newest guide ”Increasing Clinical Trial Patient Diversity.” 

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CCTrialSuite has the only Clinical Trial Management System (CTMS) that cuts out the complexity in clinical trial software with tools designed to work for clinical trials.

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