Marketers often try to find patterns in their data in order to better advertise, and this got me thinking about connections to how health care professionals prescribe treatments.
We’re all familiar with companies sending email to market their products. What many don’t realize is that these companies leverage their data to try to discover patterns in what ads might be beneficial to you. This process is severely limited by the type, quantity, and quality of data available about a particular customer. For instance, I’ve never purchased any products through Groupon, so the only data they have for me is my Zip code. So although I thought it was silly that they recently sent me, a single male, an offer for a women’s fitness center membership, they merely lacked the data to do any better. Amazon has been better for me, but not by much. I only purchase products there a couple times every year, so each individual purchase carries more weight than it should. Just because I once purchased a television there does not mean that I am interested in seeing ads for TVs every month. Although it’s possible their algorithms could benefit from some newer concepts from pattern based analytics, their real issues are a lack of data about me. If I were to patronize them more frequently, they could offer ads more representative of my interests.
So how does this relate to healthcare? Well just as I don’t patronize Amazon more than a few times a year, most people don’t visit their physician more than a handful of times a year either. The physician might have additional data in the form of family history, but that is always changing and tends to be incomplete. The intricacies of why “personalized medicine” has yet to prosper are beyond the scope of this conversation, but I suspect that data quality and quantity has been a hindrance. Better, more complete data about a patient’s health history could lead to a better understanding and therefore better treatment options and preventative measures.