Ryan: How have real-world data-enabled algorithms helped R&D thus far?

To address this problem, Janssen looked at routinely collected histopathology data and built an algorithm that aims to predict the presence of tumor mutations from the digitized histopathology images – and we’re now deploying these algorithms to guide patient screening efforts for our clinical trials. If you can screen patients for a trial efficiently, you’ll enroll more of the right patients in the right studies.

Ryan: How else have real-world data-enabled algorithms helped R&D thus far?

We worked with partners to develop an algorithm, which recently received FDA Breakthrough Device designation, that aims to detect subtleties in ECGs – tests frequently done within six months of the onset of symptoms, like shortness of breath – that could suggest a patient has pulmonary hypertension. Earlier detection could avoid a several-year delay in diagnosis and potentially result in earlier access to treatments.

We’re still going through the validation phases of this work. Rigor is important. But this is the kind of difference and change that we’re looking to make for science and for patients.

Ryan: How is real-world data accelerating patient recruitment?