For me, air travel is a perfect time to catch up on some reading. I read scholarly journals or articles on benefits, get caught up on legislation, or even throw in a romance novel or two (sometimes I need a little entertainment). My literary selection this week is a book titled Why Nobody Believes the Numbers: Distinguishing Fact from Fiction in Population Health Management, written by Al Lewis. That’s it. I officially admit it — I’m a health plan nerd. The only scenery the poor romance novel saw on my trip was the inside of my travel bag.
Over the years there have been occasions when a carrier, vendor, or consultant offered me services with claims of significant savings or an array of impressive numbers. Trends, ratios, utilization percentages and claims of return on investments have all danced across presentations. But my typical response has always been to raise an eyebrow.
This book was meant for me. Its purpose is to de-myth many of the so-called claim savings scenarios presented in the marketplace and Mr. Lewis is not shy about pointing them out. He’s let the cat out of the bag, while presenting his findings in a delightfully humorous way.
A good portion of the book is devoted to basic fifth grade math — a stark reminder that no outcome can be reduced by 100% (even though some sales pitches cite astronomical savings.) A theme of importance throughout the book suggests the use of plausibility testing. The author provides examples, as well as a variety of plausibility rules that should never be ignored.
My favorite subject so far has been that of wellness. The author maintains that just because health plan costs go down after implementing a program doesn’t necessarily mean the program has been successful. Described formulas should take into account risks groups and how they move (some medium- and high-risk groups may move down, while low-risk groups move up); a certain percent of risk groups change because some members change their behaviors on their own (without the help of the wellness plan); and further, the importance of what you measure. Mr. Lewis explains that even though many wellness companies and programs tout savings, interestingly enough, results are never plausibility-tested.
A good measurement for wellness would be to travel back in history on your claims data and grab whatever metrics you can that could relate to the wellness program itself. Then, after a good time period has passed (that is, not after only three months, as one wellness vendor did), compare your past claims to your most recent data following the program’s implementation.
Some of the metrics might include: urgent care, emergency room visit, primary care physician visits, specialist visits, preventive services, weight-loss surgeries, out-patient services, or anything else you can obtain to help you measure the results of wellness utilization. Preventive care and physician office visits should naturally increase, while the other expenses should decrease. If you don’t see these types of statistics change, then you might not be able to give your wellness program a gold star.
I haven’t finished the book yet but so far it’s given me a new viewpoint and yet another reason to continue raising an eyebrow when I review results of disease management, wellness, and claim savings reports. Further, I’ll delight when I do finish and absorb all this new knowledge — that’s when I can get back to the lovey-dovey romance books.
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