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Phesi Launches Predictive Patient Burden Score To Improve Trial Efficiency, Patient Experience and Investigator Site Performance

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Phesi, a patient-centric data analytics company, has today announced it has enhanced its AI-driven Trial Accelerator™ platform with the addition of the Patient Burden Score. The Patient Burden Score enables sponsors to optimize 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 contextualized 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 subjected to more procedures. By using data from ongoing and historical clinical trial protocols and amendments, and patient data to construct a Digital Patient Profile, patient burden can be predicted and optimized during planning. This reduces burden on patients and on the investigator site staff responsible for collecting data.

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“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 optimized to meet commercial objectives and ensure development programs are patient-centric.

“Patient and investigative site participation burden assessment represent a new and important pathway to optimizing protocol design,” explained Dr Kenneth Getz, Executive Director and Professor, Tufts Center for the Study of Drug Development. “In the past, assessments have attempted to simplify protocol designs by focusing on reducing and eliminating procedures based on their cost and the endpoints they support as defined by the research sponsor. Participation burden assessments amplify and leverage patient- and site-centric approaches to inform protocol design decisions.”

Phesi has already demonstrated how data can be applied to reduce patient burden in trials during a recent partnership with a leading biopharmaceutical company researching osteoarthritis of the knee. Phesi used data from 692 clinical trials in osteoarthritis of the knee, involving 151,222 patients to calculate average outcome measures. These findings were then applied to inform trial design and reduce the planned number of outcome measures for their trial from 20 to 11. This approach has proven effective in reducing patient burden and clinical trial design while also meeting FDA regulatory approval requirements.