Clinical trial screening in gynecologic oncology: Defining the need and identifying best practices.

Abstract

Background: Evidence is limited in gynecologic cancers on best practices for clinical trial screening, but the risk of ineffective screening processes and subsequent under-enrollment introduces significant cost to patient, healthcare systems, and scientific advancement. Absence of a defined screening process makes determination of who and when to screen potential patients inconsistent allowing inefficiency and potential introduction of biases. This is especially germane as generative artificial intelligence (AI), and electronic health record (EHR) integration is applied to trial screening. Though often a requirement of cooperative groups such as the Cancer therapy Evaluation Program (CTEP), and/or the Commission on Cancer (CoC), there are no standard practice guidelines on best practices regarding screening and how best to track screening data.

Development of manuscript: The authors provided a review of current clinical trial screening practices and the effect on enrollment and trial activation across a variety of disease and practice sites. Established clinical trial screening practices and evidence supporting emerging strategies were reviewed and reported. Due to lack of published literature in gynecologic oncology, authors sought to survey the members of current rostered GOG sites to provide perspectives on clinical trial screening practices. Survey results showed a variety of screening practices. Most respondents participate in some type of manual screening process, where approximately 13 % also report incorporating AI or EHR integration. Over half (60 %) of sites track screening data to use for feasibility when opening new trials. The rapid increase in generative AI, EHR integration, and site agnostic screening initiatives could provide a significant opportunity to improve screening efficiency, translating to improved enrollment, but limitations and barriers remain.