I participated two days ago in a Deloitte webinar that addressed healthcare lifestyle analytics (see: Consumer Behavior Over Lab Results: The Power of Lifestyle-Based Analytics). The basic concept here is that healthcare organizations are able to collect various types of publicly available "social" information about individuals and, using "big data" techniques and predictive analytics, make inferences about their health status or goals. For example, a middle aged woman who takes yoga classes, is an avid golfer, and patronizes local health spas will probably have a special interest in wellness. Contrariwise, a woman of a similar age in the same town who has high TV consumption indicators, rents an apartment, and is a fast food purchaser may not be so disposed.
A key question relating to this topic is what a health system or a health insurance company engaged in this type of research plans to do with the findings. For a retail merchant selling, say, electronic gear, it would make sense to direct its advertising toward individuals predisposed to buy its products. Health insurance companies, on the other hand, make more money if their clients don't utilize healthcare services. That's the reason they pay for smoking cessation programs or offer lower rates to non-smokers. The use of lifestyle analytics by such insurance companies provides at least the basis for cherry-picking clients or choosing to not sell insurance in a state with unhealthy populations.
The webinar speakers acknowledged the possibility of cherry-picking by health systems and health insurance companies with access to lifestyle data but emphasized the potential for positive actions by health systems equipped with such information. Such positive benefits included proactive patient care coordination, personalized medicine services, measuring patients' propensity to change, developing targeted interactions with patients, and assessing a patient's preferred mode of engagement. All of this makes sense to me. In short, lifestyle analytics in healthcare can be used mainly for the benefit of patients or the opposite direction. Moreover, deleterious actions can be cloaked in what appears to be benign intentions.
For me, all of the positive aspects of lifestyle analytics are encompassed in their use for proactive care as discussed in the article quoted below (see: Lifestyle-based analytics hold promise for proactive care). Here's a key quote from it:
Lifestyle-based analytics may be an "emerging" predictive health model, but experts note that it's "simply taking data that we already have at our fingertips" and analyzing it in ways that weren't possible before. The benefit? “Moving from a reactive mode to a proactive mode” in healthcare....In the past, predictive healthcare modeling has used claims data, but the majority of the population doesn’t have good data – making predictions about life events and diseases difficult....This data also provides high correlations for lifestyle-based diseases, which account for 75 percent of the total medical dollars spent in the U.S.... [Healthcare analytics] can allow health insurers and providers to be proactive and not wait to do something until they are sick, which lowers overall healthcare spending. But the model can also indicate an individual's “willingness to change.”