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The Untapped Data That Can Improve Lives and Lower National Healthcare Spending
By Kurt Waltenbaugh
Kurt Waltenbaugh is founder and CEO of Carrot Health of Minneapolis, MN.
Ask 10 mechanics which costs more — preventive or corrective maintenance — and each will likely give the same answer. It’s cheaper to change a car’s oil regularly than to repair a seized engine. The same principle holds true for healthcare.
In 2015, US healthcare spending reached $3.2 trillion. More than half of that went toward hospital care and physician / clinical services, which increased by 5.6 percent and 6.3 percent, respectively, according to the Centers for Medicare and Medicaid Services (CMS). The surge in payouts for these services was due to “non-price factors,” specifically an increase in “use and intensity of services.”
This makes sense given that the coverage expansion under the Affordable Care Act (ACA) gave more Americans access to healthcare than ever before. But at a time when the public and healthcare professionals have centered their focus on reducing insurance premiums and the cost of care, there is one question missing from the debate. Could the need for some of these services have been prevented?
The answer lies in a well of big data that has, until recently, been untapped by the healthcare industry.
In the health insurance market, there exists a disconnect between medical costs and an individual’s health quality. Behavioral and socioeconomic factors determine roughly 60 percent of their overall health, yet 88 percent of the country’s healthcare spending goes towards medical services, which impacts merely 10 percent of a person’s healthiness.
A study entitled “Health and social services expenditures: associations with health outcomes” compared spending by 11 nations on medical care against social care and the impacts on health outcomes. The findings showed that not only was the US the only country to spend more on healthcare than social services as a percentage of GDP, but that a higher ratio of spending on social services was also associated with better outcomes in infant mortality and life expectancy.
Access to this socioeconomic and behavioral data gives payer organizations a clearer picture of a member’s health risks. For example, detailed knowledge about where a person lives — such as neighborhood crime rate, average household income, and availability of healthy food — provides more predictive information than higher-level information on the coverage region, data that delivers far more accurate insights into quality of life. Environmental factors like “walkability” can help determine how easy it is to exercise, while air quality can indicate a person’s risk for lead exposure. For individuals living in a low-income, high-risk area, education and local job opportunities can determine their probability for upward mobility and, by extension, how likely they are to improve the socioeconomic factors impacting their health.
On the surface, proponents of data privacy might argue that these companies would push to use this information to raise premiums for those whose socioeconomic and/or behavioral patterns make them more susceptible to life-altering medical conditions. A deeper examination, however, reveals an opportunity for payers to cover more individuals with less-costly interventions without losing any competitive ground. By connecting these individuals with services that help address social and behavioral determinants of health, payer organizations help them improve their lives while also reducing the potential need for higher-cost care interventions, such as emergency room visits or hospitalization.
In fact, this approach has the potential to change the way insurance operates throughout the country. Rather than balancing enrollment with enough low-risk members into a health plan to cover the care costs for high-risk members, a strategy centered on preventive care through social and behavioral interventions means payers become more invested in their members’ total quality of life, thereby creating a healthier population.