Using AI Trial-Matching Tools to Accelerate Patient Access

In a recent video interview with Applied Clinical Trials, Adrianne Rivard, senior community development manager, myTomorrows, discussed the challenges physicians face in identifying clinical trials. Highlighting insights from a recent myTomorrows report on the need to streamline clinical operations, Rivard touched on the use of artificial intelligence (AI) to streamline pre-screening, which can reduce the need for multiple databases. She also discussed how AI tools are gaining use, necessitating compliance with data privacy standards and ongoing education to build trust among physicians.

Rivard: I think we're seeing in this day and age that the use of AI can make a huge difference. For example, our platform provides physicians with a comprehensive overview of all relevant options for their patient, which is saving a lot of time for them, because what we're hearing, and we also saw this feedback in the survey, is that a lot of physicians actually have to use multiple databases to do that, and it's super time consuming. That's one thing, and the other thing is beyond the overview that our AI pre-screening also is allowing for quick and accurate pre-screening and identification of patients for these options. Especially when criteria is really complex, is what we also hear, is that it's really hard to navigate through all these systems and really understand where patients are eligible and when they're not. I think what clinops teams can do when tools like this are integrated into EHRs, for example, physicians then have access to these clinical trials already in their existing workflow, so it's saving a ton of time, which in turn, then accelerates patient access to these treatments as well.