Data, Marketing, Outreach, Healthcare
January 19, 2021
At Causeway Solutions, our data has shown that men who shop at Big & Tall stores are more likely to require a total knee replacement than a man who does not. This is an undeniable fact, but what does one have to do with the other?
While each of these theories cannot be completely ruled out, the answer is much less entertaining and quite logical. Rather than considering Big & Tall shopping as causation for the surgery, there is instead a very reasonable correlation. Men that shop at Big & Tall stores have a higher body mass index (BMI) than their smaller peers. An elevated BMI is a known risk factor for osteoarthritis, and arthritis is the leading reason for a total knee replacement.
Advanced data in today’s environment allows us to connect the dots from consumer behavior to a diagnosis to a procedure. For example:
As population health experts delve into the never-ending data banks of electronic health records, it is all too easy to become fixed on determining cause and effect in seemingly mundane behaviors or patterns. Advancements in artificial intelligence continue to decrease the time it takes to comb through these records, identify risk connections between medical history, and create future or potential diagnoses. These correlations continue to open numerous doors for researchers, providers and value-based care experts alike; each hoping to provide interventions that can help a patient avoid the path towards more difficult medical needs.
However, within these rapid advancements and expanding knowledge base, it can become too easy to overlook some correlations that do not have any causation. Integrating medical and consumer data sets can generate some remarkably interesting and impactful results. Consumer data incorporates numerous behavioral and socioeconomic factors that can provide an excellent representation of many of the determinants of health that are so impactful on medical status and needs. When this information is combined with medical epidemiology and outcomes, the predictive capabilities of the data set is very powerful.
In addition to benefiting providers and researchers seeking to maximize preventative care models, these integrated consumer and medical data sets can also be helpful for hospital marketers seeking to reach a more focused audience for a key service line. Recognition of consumer behaviors that are correlated with medical diagnoses and potential procedures provides incredible insights that are not otherwise available when only considering general attributes of potential patients such as age and gender.
How could marketing and patient outreach campaigns be enhanced using this information? Here are just a few interesting insights:
As more data becomes available it remains essential that multiple perspectives be considered in analyzing the entirety of the information. Rather than only seeing the medical record, recognizing the correlation between behaviors, purchases, and media preferences is key. This observation of medical needs may require integration of different sources, but the outcomes can be dramatic for providers and marketers alike.