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What if we could join forces with some of our industry’s most exceptional minds to revolutionize the way the world thinks about healthcare?

Every day, PRA aims to do just this.

PRA Insights Team
PRA Insights Team

Our team of world-class experts is helping to shape the future of healthcare, leveraging our global infrastructure with deep data and state-of-the-art technologies to design studies at the forefront of clinical research. From artificial intelligence and machine learning to the first fully virtual trial, PRA is committed to helping people proactively advance and improve their own health.

This year, two PRA teams were invited to participate in the PHUSE/FDA Data Science Innovation Challenge. PHUSE is a non-profit, volunteer-supported community dedicated to advancing clinical information. Every year, PHUSE partners with the FDA for its Innovation Challenge, a non-competitive environment that brings industry and academia together. This event focuses on finding innovative solutions for numerous global healthcare challenges. Collaboration is encouraged through data, artificial intelligence, machine learning, and social intelligence.

PRA’s teams were invited to participate in this prestigious event in two categories: Drug Safety and Surveillance and Approach for Predicting Drug Interactions.

Drug and Safety Surveillance

The Drug and Safety Surveillance category focuses on pharmacovigilance—the practice of detecting, assessing, and understanding adverse events following the use of a drug or biologic product. When practiced successfully, pharmacovigilance can improve the safety of a product and protect public health. Participants in this category answer questions such as:

  • How can we better detect safety signals?
  • How can we incorporate a bioinformatics approach to pharmacovigilance systems to enhance signal detection?
  • How can we use science and technology to move the field forward?

PRA’s Social Intelligence & Communities team, led by Michael Durwin and Deb Piaseczynski, will present their work leveraging multiple social and medical databases, AI-powered social listening, and social platform monitoring to research how patients, caregivers, and HCPs talk about a specific disease in a webinar on March 25. The data collected by this team helps us truly understand the patient voice, which in turn helps us understand how they describe their conditions, challenges, and frustrations, and the type of information they seek. This data can help us identify behavioral trends and activities.

Understanding the Patient Voice Through Social Listening

Approach for Predicting Drug Interactions

The Approach for Predicting Drug Interactions category focuses on drug-drug interactions. Participants in this category collect and analyze post-market and observational health data of prescription and over-the-counter medicines and dietary supplements. Their goal is to predict potential drug-drug interactions to reduce the volume of adverse events resulting from patients taking multiple medications.

Led by Kerry Deem and Kathleen Mandziuk, PRA’s multidisciplinary team of data scientists, machine learning/artificial intelligence experts, epidemiologists, and clinical specialists focused on drug interactions due to polypharmacy in geriatric populations, patients undergoing immunosuppressive therapy, and individuals receiving prescriptions from a high number of prescribers. Using machine learning (ML) and pattern recognition models built on publicly available data sets, as well as PRA’s Symphony Claims and EHR data, the team aims to predict and/or identify side effects or adverse events related to drug-drug interactions.

Approach for Predicting Drug Interactions

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