Suggestive Analytics. The power of suggestion meets the power of data. But, before we talk about that, let’s contrast it to the buzz phrase of the day — Predictive Analytics.
I’d like to have a dime for every white paper, blog, journal article and marketing brochure I’ve seen in the past six months that cheer a coming healthcare revolution as a result of “Predictive Analytics.” The only thing more prevalent is the term “Big Data.” The latter fascination and buzz is rooted in Freudian reference, I’m quite sure, so of course every male CIO in healthcare wants it. J But what of the former — what about predictive analytics? On what do we base this sudden love affair with predictive analytics? Not much, I’d say.
Wikipedia has a fairly lengthy definition of “Predictive Analytics.” As defined there, “Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events.”
Seems to me that predictive analytics in healthcare tends to come in two flavors — too easy and too hard — with a neglected middle ground. Do we really need predictive analytics and Big Data to know that a 32-year old sedentary patient who smokes and has a BMI of 30 is a high risk for multiple chronic diseases? What about a patient living alone, over 65, post-CABG — do we need Watson to tell us that such a patient is high risk for readmission? Hardly… and yet we are surrounded by such patients. The problem isn’t that we don’t know. The problem is that we don’t intervene.
At the other extreme are the patient outliers and rare cases. As much as we would hope and try, no computer algorithm in the near future is going to predict the impending stroke of a 34-year old patient who is a competitive triathlete with no family history of cardiovascular disease, yet we pursue such predictive scenarios and celebrate enormously if we come within a country mile in a randomized trial of pulling it off. We ignore the forest of high-risk patients that surrounds us, in pursuit of the perfect and isolated tree.
The predictive middle ground of data that we choose to ignore at the point of care is genetic and family history. Depending on which report you believe, we know of at least 150 genetic and family history markers that have a profound impact on the outcome of care. Think about it — we can predict and manage care with genuine data-driven decisions in these cases, but we ignore this middle ground, in part, because the data is not readily available in EMRs at the point of care (a different problem altogether worth discussing), we ignore these opportunities for the same reason we ignore the forest of “too easy” scenarios.
Predicting clinical outcomes and risk is not the problem. The problem lies in our inability and unwillingness to intervene — personally, culturally, and operationally — when we see the opportunity. What healthcare system is operationally capable or even culturally willing to assign a caregiver at home to ensure that the 65-year-old post-CABG patient will not be readmitted? How many women are willing to be tested for BRCA1 and BRCA2 mutations that raise the risk of breast cancer by 60% … and take action if they are affected? I won’t even talk about our inability to do something about the skyrocketing incidence of obesity and diabetes. It’s not that we don’t know, it’s that we are generally incapable of intervening.
Does this cynicism mean I have no interest or hope in predictive analytics for healthcare? No. We need to continue inching along technically and culturally with the concepts until, someday, the two will intersect. In the meantime, we should be realistic and look for other analytic opportunities that are within the grasp of healthcare and already surround us in other parts of our lives — which brings me back to this notion of Suggestive Analytics.
I first saw the power of suggestive analytics in a program called the Antibiotic Assistant at LDS Hospital, thanks to colleagues Dave Classen and Scott Evans. The Assistant is a complex algorithm which predicts and ranks the best course of antibiotic therapy for inpatients, given the profile of their lab, micro and pathology results, and general demographics. It’s a very impressive, and an early example of predictive analytics. However, to me, the equally impressive story is its use of suggestive analytics.
When the program was first released, only the predictive efficacy of the rank ordered antibiotic protocols were presented to physicians. Naturally, physicians always chose the highest ranked protocol, even if the predicted efficacy of the top choice only differed by a tenth of a percentage compared to the second best choice, but that second best choice might be 10 times less expensive than the top choice. The breakthrough came when Scott and David revealed the cost of those antibiotic protocols to the physicians, thus suggesting to physicians that they also consider cost alongside predicted efficacy when making their decision. The benefits to clinical outcomes stayed virtually the same, but costs dropped from an average of $123 per dose to $52.
As e-commerce consumers, we are surrounded by Suggestive Analytics. Amazon was among the first to influence our behavior by using data in this capacity. They surround our transaction — e.g., buying a book — with data-driven suggestions that affect our purchasing behavior — customer ratings, frequency of purchase by other consumers, commonly bundled and related purchases and products, availability of the product, arrival date of the shipment, new vs. used prices.
Richard Thaler and Cass Sunstein wrote a great book (Nudge: Improving Decisions About Health, Wealth, and Happiness) that gives example after fascinating example of what amounts to suggestive analytics, even though they never specifically use the term.
The difference between predictive and suggestive analytics is summarized quite easily: under predictive analytics, Amazon would fill your shopping cart for you, based upon using predictive data mining algorithms. Under suggestive analytics, you get to fill your own shopping cart. Would I prefer that Amazon predict my shopping habits for me and streamline the whole process without my intervention? Absolutely. Is it reasonably possible in the near future? Absolutely not.
Predictive analytics is certainly appealing in concept but, right now, it is little more than a marketing term, another adjective of hype. Our healthcare industry would be better served to borrow concepts from the world of e-commerce and social networking, and embrace a new concept of Suggestive Analytics at the point of care to nudge our behavior in desired directions.
Dale Sanders also serves as VP of Healthcare Quality Catalyst