Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center.

Purpose: Less than 5% of patients with cancer enroll in clinical trials, and 1 in 5 trials are stopped for poor accrual. We evaluated an automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials.

Patients And Methods: Medical records from 997 patients with breast cancer were assessed for trial eligibility at Highlands Oncology Group between May and August 2016.

Antiemetic guideline consistency and incidence of chemotherapy-induced nausea and vomiting in US community oncology practice: INSPIRE Study.

Purpose: Consensus guidelines for preventing chemotherapy-induced nausea and vomiting (CINV) are variably implemented in practice. The purpose of this study was to evaluate the impact of guideline-consistent/guideline-inconsistent CINV prophylaxis (GCCP/GICP) on the incidence of no CINV after cycle 1 of highly or moderately emetogenic chemotherapy (HEC or MEC).

Patients And Methods: This prospective observational study enrolled chemotherapy-naive adult outpatients who received single-day HEC or MEC at four oncology practice networks, all using electronic health record (EHR) systems, in Georgia, Tennessee, and Florida.