How AI Helps Diversify Pool of Candidates for Clinical Trials

Clinical trials in the U.S. often struggle to get enough participants. About 80% of trials either stop early because they cannot find enough volunteers or take much longer than planned. These obstacles make it expensive to bring new treatments to market, which could make them cost more for health care providers and patients, Denise Juliano, the group vice president of PINC AI Applied Sciences, the data, research, and analytics division of Premier, said in an email interview.

Clinical trials in the United States show an underrepresentation of African-Americans at 5%, compared to 15% for white Americans, according to Premier. Similarly, Latinos are also slightly underrepresented, with 7% participation compared to 8% for white Americans.

The speed of Natural Language Processing (NLP) is helping boost diversity in clinical trials, experts say. The NLP helps find ways to study patients in their communities, figuring out what stops them from joining trials, and coming up with plans to reach those who are not represented, Juliano said.

In the past, clinical trial sites were chosen mainly based on the sponsor's connections with Principal Investigators (PIs) at those sites. Now, with the help of AI, trial sites can be picked out more precisely and efficiently by estimating how many eligible patients are in those areas, Juliano said.

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