PALO ALTO, Calif., March 26, 2019 /PRNewswire/ — Verantos, the market leader in regulatory-grade electronic health record (EHR)-based studies, today announced a collaboration with Amgen focused on the use of advanced data sources and artificial intelligence (AI) to achieve high-accuracy observational studies.
The collaboration is focused on how current real-world evidence (RWE) techniques in cardiovascular medicine may be advanced with natural language processing and machine learning to achieve more timely and clinically meaningful insights from observational studies. Initial work has demonstrated high accuracy in cohort definition of inclusion criteria, exclusion criteria, exposures, and outcomes with sensitivity increased from 50 percent to more than 90 percent for most cohorts.
“Amgen believes that utilizing innovative technologies is critical to advancing care and improving the lives of patients with cardiovascular disease, one of the world’s most significant health challenges,” says Keri Monda, Ph.D., Director, Center for Observational Research, Amgen. “Collaborating with Verantos can provide improved insights and solutions to investigate potential treatment options and unmet patient needs.”
“Early results from this collaboration have revealed that typical study approaches in cardiovascular medicine should be enhanced when clinical assertions are made,” says Dan Riskin, MD, MBA, Founder and CEO of Verantos. “We appreciate the value Amgen places on innovation in observational research methods and data ascertainment and high accuracy results, and look forward to continuing to support them in real world evidence.”
Verantos and Amgen plan to submit initial findings from their partnership for peer-reviewed publication in 2019.
Verantos is the market leader in electronic health record (EHR)-based studies for regulatory and reimbursement use. The company delivers turnkey regulatory-grade real-world evidence studies for top biopharmaceutical firms. Advanced data sources and artificial intelligence approaches enable studies that would otherwise be inaccurate or not possible using traditional data sources and approaches. Achieving credible real-world evidence studies lowers cost and risk in label expansion, post-marketing surveillance, and reimbursement.
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