Patient AI-drive trial accelerator launched

A patient-centric data analytics company Phesi has enhanced its AI-driven Trial Accelerator platform with the addition of the Patient Burden Score enabling sponsors to optimise protocol and study design by predicting how many times a trial participant may need to visit an investigator site, what procedures will be conducted, and what data needs to be collected and recorded during each visit.

The Patient Burden Score can be applied to reduce patient burden and simplify trial design to improve investigator site performance, shorten enrollment cycle times and significantly reduce costs.

This new metric is calculated from more than 485,000 clinical studies and 108 million contextualised patient records contained in Phesi’s database.

This granular data includes the average outcome measures recorded for each clinical trial. The median number of outcome measures recorded for a trial participant is 5, but ranges from 1 to 302. The higher the number of outcome measures collected, the greater the burden on a patient during a site visit, leaving them subject to more procedures. 

“Reducing patient burden and simplifying trial design are two sides of the same coin. When coupled with a digital patient profile, Phesi’s Trial Accelerator delivers a unique approach to clinical trial design and execution,” said Dr Gen Li, President, Phesi. “We are continually enhancing Phesi’s Trial Accelerator to support the industry in overcoming common challenges in clinical development. Features such as the Patient Burden Score, and the Patient Access Score that we implemented last year, bring new ways to reduce costs, cycle times and – most importantly – patient burden. Data underpins all we do at Phesi, and data-led approaches such as these inform predictive AI and scenario modelling for more successful trial outcomes.”

Patient Burden Score is an extension of Phesi’s existing scoring and performance toolkit to enable precision in patient-centric trial design. These highly accurate tools are built on almost 20 years of experience developing algorithms and predictive models for clinical development. The Patient Burden Score is applied to precisely understand how to prioritize protocol design options, such as inclusion/exclusion criteria, procedures and outcome measures. The metric ensures study design is optimised to meet commercial objectives and ensure development programs are patient-centric.


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