Protecting Employees From Harmful Vendored Wellness Programs: A Wake-Up Call

Al Lewis wears multiple hats, both professionally and also to cover his bald spot. As founder of Quizzify, he has married his extensive background in trivia with his 30 years experience in healthcare to create an engaging, educational, fully guaranteed and validated, question-and-answer game to teach employees how to spend their money and your money wisely. As an author, his critically acclaimed category-bestselling Why Nobody Believes the Numbers, exposing the innumeracy of the wellness field, was named healthcare book of the year in Forbes. As a consultant, he is widely acclaimed for his expertise in population health outcomes, and is credited by search engines with inventing disease management. As a validator of outcomes, he consults to the Validation Institute, part of an Intel-GE joint venture.
While previous studies have revealed concerning issues regarding workplace wellness programs, the industry has now crossed a line. The program chosen as the industry's best actually harmed employees.
Previous columns on wellness have highlighted rampant innumeracy, clinical implausibility, complete ineffectiveness, and mathematical impossibility in wellness industry outcomes claims. The headline of a recent lead investigative story in Slate called workplace wellness a “sham” that producing no savings. (The influential Incidental Economist had already reached a similar conclusion, expressed “many times and in many ways.”)
 
While these articles have revealed concerning issues, one could argue that workplace wellness is a business-to-business venture, with contracts entered voluntarily. Therefore, it is the responsibility of individual corporations and their consultants to identify and prevent questionable business practices and vendor claims, as long as employees themselves aren’t being harmed.
 
However, it appears that the industry has now crossed a bright line, as the program chosen as the industry’s best—winning a C. Everett Koop Award from industry peers—did indeed harm employees, as is shown below and as was reported, and unrebutted, in STAT. This result, in any program—but especially the alleged best—should raise red flags for customers and regulators.
 
By way of background, the Koop Award is supposed to recognize the year’s most exemplary wellness program. However, these allegedly exemplary award-winners have consistently been criticized for ineffectiveness and or grossly inflated savings. Consider the 2015 winner, McKesson, whose controversial outcomes were newsworthy enough to reach Employee Benefit News. The company claimed millions in savings, bur participants got fatter, and their cholesterol and glucose increased. Comparing the improvements and deteriorations in biometric risk factors showed no net change: both the “increased” and “decreased” columns in McKesson’s award application reproduced below sum to 58%, meaning there was no change in objective participant health status. (Non-participants and dropouts are not included in the chart below.)
 

 
Not achieving favorable outcomes is a business-to-business issue. McKesson elected to contract for the program, taking the risk that it might not save money or improve employee health. However, harming employees is something else altogether, and in 2016, that's what Wellsteps did to the employees of the Boise School District. (It may have been just a correlation. Causality is difficult to prove, but Wellsteps’ report claims causality throughout.)
 
First, we will show how Boise's employees became unhealthier both objectively, according to the trend of the biometrics, and subjectively, according to self-rated health status. Then, we will explore the specific aspects of the Wellsteps intervention that could have caused this outcome.
 
Objective Biometric Mean Scores Deteriorated
Wellsteps presented a summary of the changes of biometric means. It is almost always the case that, when adjusted for expectable aging-related changes, the absolute number of low-risk people in a population whose risk increases roughly equals the number of high-risk people whose risk declines. This is regression to the mean, known in the field as the “natural flow of risk.”
 


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