In February 2024, I published a review in Applied Clinical Trials Online to outline the different approaches to patient-clinical trial matching. This is a complex and much discussed area which can be confusing.
The article explores the complexities of using technology to match patients with clinical trials based on their medical profiles and the trials’ eligibility criteria. It distinguishes between patient-centric and trial-centric matching, where the former helps patients find trials, and the latter helps providers identify suitable patients. The article emphasizes the importance of understanding the intended customer for matching platforms—patients, healthcare institutions, or trial sponsors—and the types of data used in matching algorithms.
Additionally, it highlights the challenges related to data interoperability, site and operational status, and the last-mile execution in connecting eligible patients to trials. The article also discusses the potential role of AI, NLP, and LLMs in improving the accuracy of matches. Key considerations for choosing a trial matching solution include understanding the platform’s target users, data sources, scope of trial coverage, communication processes, and the workflow for confirming matches. Ultimately, the article aims to guide stakeholders in selecting and implementing effective clinical trial matching solutions.
The original article is here:
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