Back in the early 1900s, Albert Einstein had a problem. Sophisticated instruments were unexpectedly showing that the measured speed of light was the same if the source or the observer were moving or stationary. In other words, if one were moving away from a bullet, it should look (to the observer) that the bullet had slowed down. Light’s refusal to conform to the prevailing common sense about how the universe should work ultimately forced Einstein in 1905 to conclude that, in order for the speed of light to be constant, time and mass had to be elastic. This ushered in a new field of relativity mathematics that is still being used to plumb the known universe’s Music of the Spheres.
While the controversies surrounding the effectiveness of “population health management” (PHM) are quite minor compared to Einstein’s Theory of Relativity, the comparison is still instructive. The similar mismatch between what is assumed, what is observed and how to mathematically describe the ultimate truth also underlies Al Lewis’ book, Why Nobody Believes the Numbers. In other words, we assume care management-based patient coaching always yields savings, increasingly sophisticated observations often fail to show it and, as a result, we need new mathematics to reconcile what we assume and what we observe.
Interestingly, author Al Lewis of the Disease Management Purchasing Consortium never doubts the speed of light or that high quality PHM ultimately can save money. While PHM vendors may interpret his long history of skepticism as some sort of shakedown, Al’s passion is clearly evident: Why Nobody Believes the Numbers is ultimately driven by a search for the truth. For that he deserves a lot of credit.
The good news is that Mr. Lewis does a masterful job of examining the prevailing assumptions underlying the PHM universe by relying on layman’s logic, simple examples, real world anecdotes and clever insights. As a result, even the mathematically challenged can come away with a better grasp of the pitfalls that surround selection bias, regression to the mean, invalid comparators and calculation of trend. As a result, the first chapter on “Actuaries Behaving Badly” is arguably “must reading” for human resources managers, sales personnel or C-suite types that are contemplating the “return on investment” from a company wellness or a disease management program.
That bad news is that Al Lewis is no Einstein. He suggests that a solution is at hand thanks to a simplistic “dummy year adjustment” methodology that is based on serial observations over a long period of time that includes all patients with the index condition. When this is combined with a series of common-sense based “plausibility” tests, Al proclaims his mix of common sense and fundamental mathematics will yield a single, yes or no, black or white, it did or did not reduce insurance-claims expense-truth.
Unfortunately that ain’t necessarily so. Even Einstein’s insights couldn’t explain all of the sublime harmonics that make up the Spheres. There’ll be a future posting with more on how Why Nobody Believes the Numbers falls short. That will address the unavoidable impreciseness that surrounds measures of central tendency, the challenges of measuring subgroups and the moving-target realities of an insurance industry that continue flummox those of us who are trying to explaining the health care universe.
In the meantime, if you’re a buyer or a vendor, I recommend Why Nobody Believes the Numbers for your bookshelf. You’ll come away with a better grasp of the good, the bad and the ugly of outcomes measurement, understand what it can and cannot tell us and appreciate the underlying and still evolving debate over the ultimate value of the PHM industry.
Jaan Sidorov, MD, is a primary care internist and former Medical Director at Geisinger Health Plan with over 20 years experience in primary care, disease management and population-based care coordination. He shares his knowledge and insights at Disease Management Care Blog, where this post first appeared.
You’re correct. It does remind me of the saying, Do figures lie or liars figure. Probably the most useful how to understand statistics book I’ve ever read.