How to Gauge the Effectiveness of Employee Wellness Programs

Tracking the data on the effectiveness of employee well-being programs can feel like you’re watching a Ping-Pong match. While more than 60% of U.S. businesses offer such programs, research on their effectiveness has been mixed. There are findings that point to positive gains in both cost savings and productivity measures, while other studies, including the recent report from the National Bureau of Economic Research, find that programs can make for good recruitment tools but won’t do much to lower costs or improve health. The result: persistent questions on whether well-being programs deliver meaningful value and, if so, which ones do.

I contend that what lies at the heart of the inconsistent results is not the programs per se but rather how we define and ultimately measure “well-being.” WebMD Health Services has developed a measurement system that addresses this deficiency.

The wellness and well-being industry has traditionally focused on assessing the impact of programs on lowering specific health risks (smoking, stress, and weight, for example) with little acknowledgment of the interplay between those risks and how, taken together, they provide a more relevant definition of well-being and a more accurate reflection of value.

Ask employees what happens when they succeed at a weight-loss effort. They may feel healthier, but they also report that they sleep better, have more energy, experience more positivity, and find that they can get off blood pressure medication. In short, weight loss may have improved sleep, minimized depression and anxiety, and conferred some clinical benefits as well. The impact went beyond the one specific metric.

But, as an industry, we neither define nor measure well-being in a comprehensive way. There is no substantive, relevant approach to assessing well-being and no models to measure the impact that an improvement in one risk can have on other health risks. For example, a change in a person’s risk for stress may also change the person’s risk for inadequate sleep; a shift in physical activity risk may shift the person’s risk for stress. In short, our definitions and measurement tools have gotten in the way of capturing the true value of well-being programs for employees and their employers, and traditional metrics, such as ROI, don’t always reflect whether the program is relevant to the employee.

Working with our team of statisticians and researchers, WebMD Health Services decided to build another model to evaluate the impact of our telephone health-coaching program, a one-on-one offering that employers often include as a part of their overall well-being program to support employees in making health behavior changes. We created a novel algorithm based on an employee’s modifiable risk factors and preventive screenings that serves as a proxy for health status and enables us to stratify health risks into categories that measure acuity, that is, the level of severity of a condition or a measure of overall health status.

With this holistic model, we stratify a range of health risks into high, moderate, and low acuity, and assess the impact of reducing the risk of an employee developing a chronic condition or worsening one they already have. Two distinct employee populations were evaluated: a group of 82,681 people who had lifestyle strategies to maintain overall health and well-being and minimize the risk of developing a preventable condition, and a group of 28,941 people who were managing at least one of five diagnosed chronic conditions — heart failure, coronary artery disease, chronic obstructive pulmonary disease, diabetes, and asthma.

For the lifestyle group, the algorithm included 12 modifiable health risks such as poor diet, smoking, and overweight/obesity, and 11 preventive screenings such as mammography and colonoscopy. Each risk was given a weight relative to all other risks based on future projected health care costs and the likelihood that it would contribute to future chronic conditions.

For employees with chronic conditions, we looked at medical and pharmacy claims. Acuity scores were calculated using a weighted algorithm consisting of four normalized indices: future predicted costs, potential hospitalizations and emergency department visits, current gaps in care, and the risk of developing related conditions.

Not surprisingly, the higher the acuity levels, the higher the direct medical costs. For employees focused on lifestyle changes, those with higher acuity levels cost employers an estimated $5,598, on average, versus $4,018 for people in the low-acuity group — a difference of $1,580 per person. In the condition management group, the mean health care cost per person was $25,046 in the high-acuity group and $6,302 in the low-acuity group.

Coaching made a difference. For healthy employees receiving coaching for changing their lifestyle behaviors, 23% moved from high acuity levels to moderate levels and 43% from moderate to low. Looking at a subset of 1,000 employees, the change in medical costs after 12 months of lifestyle coaching was estimated at $195 per participant, based on lower acuity ratings.

For people receiving condition management coaching, 39% of employees with high acuity levels moved down to moderate and 12% moved from moderate to low, for an annual cost savings of $1.1 million overall, or $1,113 per participant.

Despite the limitations of traditional measurement models and program evaluations, employers continue to invest in well-being programs, and with good reason. Making well-being a business priority can improve the lives of employees; infuse workplace culture with greater positivity, energy, and commitment; reduce health care costs; and potentially help transform health in the United States and elsewhere. A new measurement model can more accurately measure wellness programs’ impact.