Life Sciences Companies Are Using AI to Empower Sites to Recruit Patients Faster

When recruiting for a trial, research staff must compare patient data against the trial’s inclusion and exclusion (I/E) criteria to find potential matches. However, more than 80% of patient data stored in EMRs is unstructured, such as physician notes, imaging, lab reports, family history, etc. For example, muscle-invasive bladder cancer (MIBC) does not have a specific code and may appear in notes as ‘muscle invasive bladder cancer’, ‘MIBC’, ‘advanced bladder cancer’, ‘invasive bladder cancer’, ‘high grade bladder cancer’, ‘advanced urothelial bladder cancer’, etc. We found 40,000 patients with ‘bladder cancer’ using the ICD-10 code C67. They were able to accelerate chart reviews and enrollment using AI, with 35% faster enrollment and four times faster screening after switching from manual processes.