Developing a manual clinical trials screening process in a diverse southern gynecologic oncology practice in the Deep South.

e23209 Background: At this time, there is no standard clinical trial screening process or definition that has been adopted within Gynecologic Oncology. In our low resource, highly diverse Gynecologic Oncology patient population, we sought to create an equitable, adaptable, manual screening process that better reflected our patient population. Our objective is to describe our clinical trial screening process and our success in improving trial enrollment and a matched trial portfolio. Methods: An IRB-approved, QI project was implemented in July 2022 to evaluate trial access for Gyn Onc patients. First, screenable events were defined as: the start of one’s first scheduled visit, bi-monthly tumor boards, weekly pre-op meetings, and by keeping track of on a shared Epic list. Potential patients were those with a screenable event: new patients or diagnoses, regimen changes, progressions, and recurrences. Events were categorized into screen positive or screened no trial available. Screen positives were further categorized as screen fail, screen positive, enrollment failure, or enrolled. Data about patients was collected via weekly research team meetings. Monthly meetings occurred to review progress. This data was compared to trials available, number of patients with trail available, and those that enrolled. Reasons for enrollment fails were tracked to improve access and anticipate barriers. Results: Overall demographic data of patients was collected. The average age of participants was 60.67 years (SD = 13.45), and the average distance to a treatment center was 59.27 miles (SD = 137.43). Other demographics are presented in Table 1. For each month, the percentages of “screen no trial available (SNTA)” events and patients who screened positive and did enroll on trial were recorded. Over time, SNTA rates stayed stable, but enrollment increased. Patient preference accounted for 32.8% of enrollment fails (n = 42), pre-existing symptoms/conditions accounted for 23.4% (n = 30), and distance/location accounted for 21.1% (n = 27). During a time of high employee turnover in the summer months of 2023, there was a rise in enrollment fails due to staffing (n = 6, 4.7%). Conclusions: Through a collaborative quality initiative, we describe an effective process of clearly defining and tracking our patient population and ‘screenable events’ for which all patients are screened and offered trial participation if eligible. We show that we improved understanding of the patient population, built a clinical trial portfolio better matched to population served, exceeded national averages for enrolling patients on trials, and are moving toward improved number eligible over time. Table: see text