Aims
Access to clinical trials for oncology patients is challenging due to rapidly expanding trial portfolios, complex eligibility criteria and fragmented information systems. Clinicians often lack the time to efficiently match patients to suitable trials. To address this, we developed TrialCatch.org, a clinician-facing digital platform designed to streamline trial identification and improve enrollment opportunities. The primary aim is to develop and evaluate a real-time, free-to-user clinical trial matching tool that integrates structured patient data with curated eligibility criteria to help oncologists identify suitable trials efficiently and accurately. A secondary aim is to establish a sustainable process for maintaining and updating the trial database to ensure ongoing accuracy as trial portfolios evolve.
Methods
TrialCatch is a web-based application that links deterministic, programmed logic with tightly constrained use of AI. It allows clinicians to input free-text clinical and molecular data, which are algorithmically matched against a curated database of active oncology trials. The system prioritises eligibility against key inclusion and exclusion criteria, and presents ranked options along with contact details for each trial. A multi-reader, multi-case validation study involving six oncology consultants—comparing unassisted matching, TrialCatch-alone, and AI-assisted matching against clinical vignettes—will commence in the coming weeks. Results are expected to be available for presentation at the COSA ASM in November.
Results
At the time of submission, formal evaluation data are pending, with the validation study about to begin. The platform has been successfully developed and deployed in a prototype environment. Clinician testing indicates improved efficiency in identifying relevant trials, accompanied by very encouraging early feedback.
Conclusions
TrialCatch represents a novel approach to improving oncology clinical trial accessibility. While formal evaluation is ongoing, early development and feedback suggest a strong potential to significantly enhance matching efficiency. Results from the validation study, alongside a live platform demonstration, are expected to be presented at the COSA ASM.