Financial metrics are important for determining the best investments to make when deciding on health and wellness programs for employees. When companies purchase wellness or disease management programs, they are making an investment of time and resources for the purpose of achieving an outcome. Currently, there is no standardized or consensual way to measure and report calculations of cost savings, return on investment, cost-effectiveness, etc. in the corporate health and wellness industry. Making these calculations is complex and fraught with problems. Ideally, studies would be conducted independently of the intervention provider (i.e., free from bias), data would be objective instead of self-reported by employees (free from "demand" characteristics or other report biases), experimental designs would be used that included random assignment and/or matched control groups (equivalency of groups except for the intervention), factors extraneous to the intervention of question would be controlled (for internal validity) during the entire time of the intervention and follow up measurement periods (control groups), and attrition of employees during the test period would be adequately controlled. Unfortunately, reality is far from this ideal but we strive to come as close as we can. The best that can be done is to make all best efforts to collect unbiased and valid data and to be transparent and accurate in reporting the manner in which financial metrics have been calculated.
The results reported here came from a study conducted independently by Wellness, Inc., for a corporate client of Hummingbird Coaching Services™. The method was to ask employees who opted into the coaching program to take a Health Risk Appraisal (HRA) questionnaire at the beginning of their program and again after approximately 6 months of participation in the program. At each measurement point, the number of people with various risk factors (as defined by the HRA) were identified. For example, there were X people with depression, Y people with unhealthy weight, etc. Industry data were used to estimate the added costs incurred when various risk factors are present. For example, the additional dollars spent on health care by people who are overweight to the level found in this study, is considered to be about $6,000 more than normal weight employees. The total "additional costs" associated with the risk factors at the beginning were compared with the total "additional costs" associated with the risk factors identified at the 6 month measurement time. The reduction in "estimated additional costs" due to reduction in measured risks was 23 percent in this particular study.
A number of caveats should be pointed out with regard to this study. First, there was no control group used, as true control groups are difficult to implement in real life employment settings. Thus, it cannot be concluded definitively that reductions in risk were due to the coaching intervention. Second, the HRA results are self-reports as opposed to objective data, such as actual health care expenditures. Similarly, as opposed to measuring actual expenditures by the specific employees in this study, actuarial data were used to estimate what level of expenditures would be likely. Third, every program has a limited rate of participation in program offerings, such as program attrition rate and participation rate in measurements. In this study, 26 percent of eligible employees participated in the program and both measurements. Based on this rate of participation and the estimated effects of the intervention in this study, it could be estimated that this company could experience an overall reduction of health care costs of about 6 percent (26 percent of employees participating and 23 percent reduction in costs from participants). Finally, return on investment should include all costs to implement a program, and not just the costs associated with the retained participants. In this particular study, the ROI was estimated at $2.75 cost savings to $1 of program expenditure.
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