I recently heard a segment on the radio about personalized medicine. Or “translational medicine.” Or “translational research.” Or “precision medicine.” Whatever you want to call it! Dr. Murray Feingold, a pediatrician and geneticist in the Boston area, painted a clear description of the term. He put it in words that all of us can understand.
Simply put, some people respond to certain medications and others do not. Why is that? Why do certain people respond successfully to certain cancer medication, while others don’t respond at all? Often times, it’s due to a gene mutation that the patient has, which can make the drug ineffective.
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.
In recent years, there has been an increased emphasis on personalized medicine. While it’s not uncommon for genetic testing to take place on patients prior to treatment to determine which drug would be most effective for them, the overarching objective of personalized medicine is to customize the treatment of patients based on research that has been conducted by leveraging a vast amount of genomic and patient data.
As you’d expect, personalized medicine is a hot topic among hospitals and academic medical centers, as well as drug companies. Through systems integration, warehousing, and analytics, many of these institutions are seeking ways to make sense of all their patient, genomic, and other data to improve the targeting of research results to specific patient populations.
To learn how Perficient’s life sciences and healthcare teams are helping leading institutions enable personalized medicine, please send us a note. You’re also welcome to watch a recent webinar on the topic.