Using Big Data and technology to create intimacy and change the biggest and most obstinate healthcare cost driver: Consumer Behavior.

What do most businesses do with customers that behave poorly, waste resources, and are highly unprofitable? They dump them. Quickly. Those of us in healthcare, however, don’t have that luxury. Whether driven by mission or mandate, payers and providers must find the most effective ways to work with these financially volatile members. In my last blog, Controlling Chaos with Technology: An Overview of Transformation in Health Insurance I provided an overview of four drivers of the HC transformation: Consumerism, Care Delivery, Regulation, and Consolidation.  In this post I want to focus on consumerism, the power of member choices on healthcare costs and outcomes, and how to use big data to create the intimacy needed to influence change. Consumer behavior remains as the largest, single driver of Healthcare costs and much like seducing a porcupine changing patient behavior can be complicated and painful.

Let’s look at the impact patients have on avoidable costs and the challenge of change. What are the avoidable costs driven by member choices? Answers vary, but they’re all very big. It could be over $1 trillion. According to the CDC, 75% of our healthcare spending, around $2 trillion, is tied to chronic diseases, and they reckon that most could be prevented by improved diet, increased exercise, tobacco cessation and responsible alcohol use. Estimates so large and coarse are easy to dismiss, but conservative extrapolations of more focused analyses of preventable, annual healthcare costs in the U.S. add up in the same direction. As a tangible example of controllable but hidden costs, studies estimate that on top of the price of cigarettes, each pack adds $35 to a person’s lifetime medical bills.

At the risk of painting with broad brushstrokes again consider another view of chronic illness. A report from the 2010 World Economic Forum concluded that 8 risks and behaviors are largely responsible for the 15 conditions that comprise 80% of total chronic illness costs.

So what do we do about it?

Few people deliberately decide to be ill or unhealthy. Their choices are shaped by many influences including habitual patterns, awareness of consequences, emotions, social determinants, and environment. The strength and complexity of these drivers make people resistant to change, even for their own benefit. Though health insurers have little control over most behavioral drivers, Big Data can help isolate their effects and enable more effective ways to improve member behavior.

Here are some examples of how healthcare insurers, and increasingly, providers, leverage Big Data to improve health and cost trends.

Improve Risk Stratification Big Data is helping payers become more adept at identifying members that pose the highest risk for future claims and targeting them for enrollment in care management programs. Analyzing variances in efficacy of these programs across members can do more than identify who is at risk. Gleaning insights into who is most responsive to these programs can enable more efficient resource deployment.

Mass-customize Care Guidelines Beyond validating the aggregate effectiveness of care management programs lie insights into which approaches will be embraced by which members. For example, a member’s openness to a smoking cessation overture depends on aligning engagement approach with personal motivators. Program effectiveness will hinge on whether the individual responds better to group programs, direct counseling, incentives, punitive measures or drug therapies. Analyzing members in the context of external socio- and psycho-graphic data can help steer members into the most effective programs.

New Technologies Creating evidenced-based guidelines for care management implicitly presumes reliance on past technologies. Emerging technologies create new opportunities to improve outcomes by delivering the programs through channels that members prefer. For example, Denver Health has reported better engagement with diabetic patients using a two-way SMS tool developed by EMC. We are also seeing a range of initiatives featuring gaming, social networking, and remote monitoring. Not only do these redefine engagement models, but they generate useful member data in the process.

Though we have only scratched the surface, several key trends show how healthcare payers and providers will reduce costs and improve outcomes. Taking a finer-grained look at patient decisions and behaviors improves focus on the strongest drivers of avoidable costs. Romancing members with programs tailored to their varying yet predictable intellectual and emotional decision styles creates an ambience more conducive to change. Innovative uses of analytics and technologies can entice member engagement and consummate the difficult but positive changes that will lead to healthier, cost-effective lifestyles.

For more in-depth analysis of  avoidable medical costs reach out to me online or stop by the EMC booth #741 at HIMSS 2013 in New Orleans March 4 – 8 to meet me in person.