If there’s a time for pharmaceutical and medical device companies to seek more real world data as evidence to support their medical products, that time is now.
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
Real world data, which is the data generated in settings such as routine doctor visits and hospital stays, is often found in electronic health records (EHR) and can help produce better product profiles. Why? Because the audience is typically much larger and more diverse than in phase I-III trials.
Recently, the global CRO PAREXEL announced a new partnership with Optum, a provider of real world data, which will enable the two companies to offer hybrid studies to sponsor organizations. The studies will encompass Optum’s aggregated EHR and medical claims data, as well as PAREXEL’s clinical research data.
According to Joshua Schultz, corporate vice president and worldwide head of PAREXEL Access, “Working with Optum Life Sciences, PAREXEL will be able to further advance the clinical research process, helping clients more efficiently develop and evaluate drugs, reduce the cost of drug development and improve patient care.”
To learn more about the business of buying and selling health data, you can read my recent post here. If you’re interested in using BI and analytics tools to mine real world data, be sure to register for our upcoming webinar.