Artificial Intelligence for Pharmacovigilance
- November 8, 2018
- 0 Comments
[Editor’s note: This guest post is the ninth in a series from Larry Taber, BP3’s Digital Strategy Officer for Life Sciences and Pharma. Larry has over 32 years of deep pharmaceutical and biotech expertise ranging from discovery research to business development. He has over 17 years of leadership of international teams successfully partnering with over 100 companies. Larry is a proven business process problem-solving professional and trainer. He has completed Master Black Belt, Black Belt, and Malcolm Baldrige National Quality Award /Performance Excellence Examiner certifications. He is passionate about the incredible advances in medical science and the enablement that is being made possible for patients through digital technologies. He holds a M.S. in Medical Biochemistry from Indiana University Medical School and a B.S. in Chemistry (Magna Cum Laude) from Purdue University.]
Will your next Pharmacovigilance Assistant be Human or an AI Machine?
Pharmacovigilance professionals and regulators are overwhelmed by the amount of data that they are being asked to process to ensure patient safety.
Consider this, the FDA now receives more than 150,000 new reports every month; a 500% increase in just the past 7 years!
By contrast, the historical challenge has been too few reported incidents to be able to make reasonable inferences about whether there is a new signal of a potential issue that requires action.
Aside from the fundamental issue of the volume of adverse events reported, companies are also expected to survey scientific literature, medical records, social media, disease & patient registries, internal and external clinical trial databases, and the list is growing.
Despite the fact that companies are expected to nearly double the number of PV resources by the year 20241, throwing more people at this won’t solve the problem.
This is actually a big data problem. There is just too much coming in, from too many sources, too quickly. Trying to analyze this much complex information is simply impossible for a human.
That’s where machine learning can come into play.
The Oracle research, entitled ‘Addressing the Data Challenges of Pharmacovigilance’, reveals that 62% of drug safety experts have begun efforts to improve adverse event processing with the use of AI. It is the new force multiplier for PV teams. Take a look at what some companies are doing to use AI to supplement the work of their PV teams.
Just getting started? Go here to learn more about Artificial Intelligence.
If you are ready to take your first steps to bring AI to your PV team, contact us at BP3 Global (firstname.lastname@example.org). We are expert business process design professionals skilled in big data integrations and the application of digital technologies, such as robotic process automation and natural language processing necessary to enable your AI capabilities.