AI trial matching platforms are overcoming the obstacles associated with clinical trial recruitment, but human oversight and data standardization are crucial.
One of the biggest pain points in clinical trials is patient recruitment. Clinical trials often have strict eligibility criteria that look for suitable subjects who are healthy enough to participate. However, finding athletic patients is time consuming and costly.
Dr Daniel Vorobiof, chief medical director at patient-centric network platform Belong.Life, says that for many years clinical trials have been in a problematic situation with only a small percentage of patients being accrued into clinical trials. “Many patients have never heard about clinical trials and their own doctors have never talked to them about it,” he says.
A data analysis by Clinical Trials Arena revealed that the most common reason for trial termination is a low accrual rate. A different analysis noted that 86% of all trials do not meet enrolment timelines and almost one-third of Phase III trials fail because of slow enrolment.
However, the prevalence of trial terminations due to low accrual is decreasing, possibly due to an extended use of technology-aided solutions, including artificial intelligence (AI). Experts shared their thoughts with Clinical Trials Arena on how AI-powered trial matching can accelerate patient identification. Still, as with many technology solutions, AI is not perfect and certain limitations can slow down the process.
Benefiting all stakeholders
People who stop responding to existing therapies always look for the next best thing, and sometimes the only option left is a clinical trial, says Belong.Life’s chief technology officer Irad Deutsch. Belong.Life offers patient support across various therapy areas, but there is a high demand for clinical trials among oncology patients.
“We understood that in order to scale up, we cannot use a 100% human-based effort to provide the support and answers to all these demands,” Deutsch notes. Over the past seven years, Deutsch and his team developed an AI-powered technology that automates most of the trial matching process.