I’ve written a number of blog posts on the challenges of clinical trial recruitment, and that’s for good reason. According to Dr. Steven Alberts, Chair of Medical Oncology with the Mayo Clinic, only 5% of cancer patients ever enroll in a trial. On top of that, only a fraction of all trials ever complete enrolling enough patients on time and, in some cases, never even get off the ground due to poor enrollment.
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
Just imagine the amount of time, effort, and cost that goes into finding eligible subjects.
There’s no question that the health sciences community has already made strides in improving the way trials are filled with patients. Digital endeavors are making it easier for sponsors, contract research organizations (CROs), and hospitals to find patients for clinical trials. And, through more complex data analyses, organizations are better able to sift through patient records to identify a good patient-trial fit. Nonetheless, much of this effort is still manual.
But, it is getting better and will continue to get better. Here’s why: organizations, like ICON, are beginning to use IBM’s “Watson” (an artificial intelligence computer) to automate the matching of clinical trials to patients. By analyzing a patient’s medical and clinical attributes, and comparing them to various study protocols, Watson can determine which trials the patient is eligible for, and even indicate the particular qualities that may have excluded the patient, giving clinicians the opportunity to help increase eligibility.
Technology solutions like this give everyone hope, from sponsors and CROs to patients. We all benefit from more effective patient recruitment tactics, and today we can thank IBM for their new Watson for Clinical Trial Matching offering.
If you’re interested in learning more about using Watson for your patient recruitment efforts, let us know.