Patient Identification for Clinical Trials Through AI and Real-World Data

It is well known that the information needed to identify the ideal patients for clinical trials exists within electronic health record (EHR) data, but accessing it remains challenging without cumbersome, manual chart reviews. Today, clinical research teams are overwhelmed with data but lack the necessary insights, especially during a time of intense pressure to accelerate clinical trials and reduce costs. AI tools like ChatGPT are processing vast amounts of data to save time and deliver answers, so how can AI strategies be utilized to analyze EHR data to identify ideal patients faster?

This webinar will explore ways in which advanced AI models today and in the future can be applied securely to real-world data (RWD) such as EHR data. The featured speakers will discuss key examples of how applying AI methods such as machine learning, natural language processing and large language models on EHR data, both structured fields and unstructured clinical notes, is accelerating site selection and patient identification.

This webinar is part of the RWD in Action Series and will feature a discussion that includes:

• The challenges and opportunities of using EHR data as a RWD source for clinical trial site selection and patient identification
• How AI is being applied today to leverage EHR RWD for clinical development, featuring life sciences case studies
• Future applications of AI for EHR and other RWD sources to accelerate trial design, build external control arms and more

Register for this webinar to learn practical strategies for patient identification using AI on real‑world EHR data.