There is a very significant opportunity to leverage sociodemographic and socioeconomic data in new ways in order to more effectively care for patients in the context of accountable care organization (ACO) and population health work; and much of that opportunity has yet to be fully plumbed. That was the core message of the presentation “Effectively Using Sociodemographic Data in Healthcare Analytics,” presented by Justin Pestrue, administrative director of quality analytics at Michigan Medicine (the new name for the University of Michigan Health System), Ann Arbor, Mich., on Friday, March 24, during day two of the Health IT Summit in Cleveland, sponsored by Healthcare Informatics, and held at the Hilton Cleveland Downtown, in Cleveland, Ohio.
Pestrue began by noting something that numerous industry leaders have pointed out and are aware of: only a small proportion of patients’ health status can directly be impacted by care delivery in patient care organizations (some say that proportion accounts for perhaps 20 percent of the overall impacts on the health of individuals, with personal lifestyle and behavioral choices, personal environmental influences, and other influences being far stronger overall).
“One of the biggest opportunities we have in healthcare analytics,” Pestrue told his audience, “is how we use sociodemographic and socioeconomic data to deliver great care for patients. This is important data to consider.” One obstacle? The fact that it remains challenging to gather, analyze, and use sociodemographic and socioeconomic data. Pestrue showed his audience a slide of a photo showing a man bending down searching for something in the grass under a streetlamp. The parable he told was this one: “So there was this man who was walking down the street one night, and he saw a man searching for something in the grass under a street lamp. The first man said, “Are you looking for something?” The second man said, “Yes, my car keys.” The first man said, “Do you think they’re very nearby here?” The second man said, “No, not at all, I lost them far from here.” “Well, why are you looking for them here?” “Well, because this is where the light is.” The point of the parable, Pestrue told his audience, is that so often, leaders of patient care organizations work with data that is relatively easy to access and work with, rather than data that might be very important but is harder to access and work with.
“So we have a real tendency to look at the data that’s easier to get—and that’s often the elements that are in our EHRs [electronic health records] and in our billing systems. But sociodemographic and socioeconomic data can do a really good job of helping us to better understand our patients,” he noted, with sociodemographic data include gender, race, ethnicity, age, and place of residence, and socioeconomic data including education level, income, etc., and both types of data being corralled together under the rubric “social determinants of health.”
“Among the conversational themes around these elements,” Pestrue told his audience, “are equity and justice; issues around policies and payments; and the elements around quality of care and population health management.” Speaking of the federal Centers for Medicare and Medicaid Services, he noted that “CMS is trying to identify how we appropriately risk-stratify populations. There’s a lot of work going into how demographics play into that,” he noted, adding that, “When I worked for a faith-based healthcare organization, they made it a part of their mission to ask, are we serving our population as equitably as possible, and fulfilling our mission?”
Meanwhile, at Michigan Medicine, Pestrue told his audience, “We’re looking much more closely at our quality outcomes and population health initiatives.” And, in that context, he noted, “The National Quality Forum has an ongoing Disparities Project. And one of their great recommendations is that while you want to account for risk, there are some challenges in adjusting for demographics. In addition, a lot of APMs [alternative payment models] have specific written requirements about using socioeconomic data as well.” Importantly, he said, “We were all introduced to these ideas when a lot of the public health data came out suggesting that very little of the longevity of people is determined by their engagement in the healthcare system. Healthcare plays about a 10-to-20-percent influence—the remaining factors are behaviors, social impacts, and genetics. So, if the healthcare system is only accounting for a small proportion, how do we account for all the other elements? That is one of the biggest opportunities we have—not only impacting our population health and public health challenges, but how are these factors being thought of in an ED visit or when a patient is on a chronic disease registry?”
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