A few weeks ago I did an interview with Munzoor Shaikh, a senior manager in the consulting firm West Monroe Partner’s healthcare practice, about big data analytics and hospital readmissions. My goal for the discussion was to delve into some new ways healthcare organizations are using the wealth of data they have at the point of care. Going into the discussion with Shaikh, I had the hypothesis that, because of the recent news that hospitals are being heavily penalized for avoidable readmissions, it probably wasn’t likely that many patient care organizations were using big data to solve more advanced healthcare problems than that.
Shaikh essentially confirmed my premise, calling readmissions reduction “low-hanging fruit” in the spectrum of healthcare and “well overdue.” An example of why he might be right is seen in a recent New York Times article that told the story of a rheumatologist at Stanford’s Packard Children’s Hospital who used data on previous patient populations to make a critical decision on a patient right in the moment. The statistics she did based on the data she had ended up leading to the correct decision for the patient—something that was not available to her in scientific literature.
What does this prove? For one, that the power of big data is incredible. The author of the Times piece suggests that big data could actually replace clinical trials. But I think the ceiling is even higher than that. To this end, Shaikh brought up something very interesting to me when I asked him about what's holding healthcare organizations back from leveraging big data and analytics at the point of care—the concept of positive deviance.
Positive deviance is a perspective that is based on the observation that in every community there are certain individuals or groups whose uncommon behaviors and strategies enable them to find better solutions to problems than their peers, while having access to the same resources and facing similar or worse challenges.
How does this apply to healthcare? Well, currently in the U.S. healthcare system, we care about the sick more than the healthy, and we try to learn from the sick rather than learn from the healthy. After all, 5 percent of the U.S. population spends approximately half of the nation’s healthcare dollars. So it makes sense on the surface—why waste our time and resources on the healthy when we need to cure the sick? That has always been my thought process, at least—typically, when you solve a problem, you figure out what causes the problem and aim to eliminate that cause.
Then I did some research on positive deviance, and came across a blog post last year from the Burlington, Vt.-based Champlain College that gave an example of how the perspective uses a different approach to solve a problem than the one we’re all used to. From the post:
In the early 1990s, childhood malnutrition was epidemic in Vietnam. A full 65 percent of children were afflicted with malnutrition. Researchers could have taken the usual path: Go into communities where malnutrition was highest, and start to correct. Instead, they went into communities which SHOULD have had high malnutrition but didn’t. They observed and analyzed the behaviors these communities exhibited which were different than in other places, developed hypotheses about what they were doing that was RIGHT, and tested this through implementation elsewhere. Some examples of what was being done right in these low malnutrition communities: use of foods typically thought to be inappropriate for children, but which happened to be rich in protein, iron and calcium; washing children’s hands prior to eating; feeding children three to four times/day versus the usual two.
The result? After testing and then implementing these practices more broadly, they showed an 85 percent drop in malnutrition. Can learning what to do right from those who are doing it right be more beneficial than studying only the problem (the high healthcare users)? Enter the power of big data, which if you leverage it to study the healthy, you can potentially take those behaviors and expand it system-wide across populations all over the world.
So can big data and positive deviance be tied together? Certainly, there are challenges, starting with the need for a cultural shift. As Shaikh told me, currently we are in the mindset of “nothing is wrong with me until it hurts.” As a result, we naturally respond to the problem—a reactive rather than a proactive approach. But there is opportunity to use predictive analytics to look at how “positive deviants” in healthcare—those who are not like to go to the hospital or have a chronic disease—sustain their health throughout a lifetime. As an April blog post from the Huffington Post said, “The synergy between predictive analytics and positive deviance creates a new approach to healthcare delivery that is currently untapped.”
There is actually a “Positive Deviance Initiative,” which works on projects that apply this theory into practice across various sectors, including healthcare. Look, this is not something that is going to happen overnight—nothing that requires a shift in behavior can be done quickly. But the premise is a real one, and because the industry has just begun to collect data, now is the time to start to make sense of it. Could studying those “positive deviants” who are coasting through life without major health issues be part of the answer to healthcare’s problems? I think we ought to find out.
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