Concepedia

Abstract

6550 Background: Only 3-5% of cancer patients participate in clinical trials even though up to 20% are eligible. Cognitive computing has promising potential to assist trial enrollment efficiency and accuracy by performing background analytics. The Watson for Clinical Trial Matching (CTM) cognitive system utilizes natural language processing to derive patient and tumor attributes from unstructured text in the electronic health record that can be matched to complex eligibility criteria in trial protocols. Screening patients for trials was performed on an ad hoc basis with traditional methods prior to implementation of the CTM system in the Mayo Clinic breast oncology practice. Methods: The Watson CTM system was trained by Mayo subject matter experts and implemented in July 2016. Systemic therapy trials enrolling breast cancer patients were included in the system. Clinical research coordinators validated Watson-derived clinical trial matches on the day prior to patient clinic visits. They gave the oncology providers a list of matched trials for each patient to facilitate treatment decision making at point of care. Enrollment and timing metrics were tracked and compared with manual screening methods. Results: Watson CTM facilitated screening of all breast cancer patients for systemic therapy trials with matches validated by coordinators in 40% of patients. Over the 18 month (mo) period following implementation, 6.3 patients/mo were enrolled to breast cancer systemic therapy trials compared with 3.5 patients/mo in the period prior. The average monthly enrollment increased by 80%. This was further increased to 8.1 patients/mo when including accruals to breast cancer cohorts of phase I trials within the experimental therapeutics program. Time to match patients to trials with the CTM system was faster than manual methods but variable depending on the role of the screener and the depth of the matching. Conclusions: Implementation of the Watson for CTM system with a screening coordinator team was associated with an increase in breast cancer clinical trial enrollment. The system enabled high volume screening in an efficient manner and promoted awareness of clinical trial opportunities within the breast oncology practice.