Employers have struggled with medical inflation for nearly two decades. As a result employers turned to worksite wellness programs in an attempt to manage and improve employee health in hopes of reducing health care demand. These programs are no longer “nice to have” initiatives, but are present in some form in most forward-thinking companies.
However, effective programs need to get to the heart of health management, beyond the vending machine choices, health risk assessments, and weight loss challenges. To truly affect costs, targeted efforts driven by clinical data need to accompany these traditional strategies.
Next Step in Savings: Data
Evidence-based guidance and strategies are the next step to manage rising health care costs. In some instances, employers have access to critical clinical information to drive these strategies through their medical, pharmacy and dental carriers. Biometric data is also important in understanding the conditions and risks driving plan costs.
The key clinical data points each employer should review include:
- Chronic conditions
- Utilization – Pharmacy, ER, hospital admissions
- Large claim analysis
- Risk index (RI) – How “sick” is the population
- Care gap index (CGI) – Identifies opportunities for intervention
- Relative risk score (RRS) – Expected cost burden
- Health risk assessments (HRA)
- Biometric screenings
Evaluating the data above is paramount in determining investment and intervention strategies—communication campaigns, targeted disease management and wellness initiatives, and changes to plan design. In addition, collecting relevant data at the onset of a health management program is a best practice. This provides a baseline to compare against subsequent years and allows an organization to focus specifically on the areas which will drive the most immediate impact.
Demand-management Strategy Based on Data
An effective demand-management strategy is the confluence of data analysis, program design, and the optimal balance of incentives to reward desired member behaviors. Such an approach was effectively deployed by a 2,000-life manufacturing client of ours, who had a high prevalence of non-compliant diabetics in their employee population. Utilizing clinical data, they identified exactly which services/tests were most often not received by the group’s diabetic population. From this they developed a strategy to reduce gaps in care, increase the compliancy rate of their diabetic population, and ultimately reduce cost.
One strategy included the removal of obstacles to access health care. The employer partnered with local community resources to bring particular services onsite to their facility. Local optometrists, endocrinologists, and general physicians provided services such as foot exams, A1C tests, and eye exams onsite with no expense or PTO usage by employees.
The employer also implemented the following:
- Plan design changes
- An education campaign
- A contribution/incentive strategy
Their strategy, driven by clinical data, resulted in a PEPY cost reduction for the diabetic population of over 55% versus the predictive model, after accounting for the costs associated with the incentive, plan design features, education campaign, and other items. The group predicts this strategic plan saved them over $1,000,000 in plan costs, and has provided them with a much more aggressive risk mitigation plan for the future.
Employers like this, who take the steps to design, measure, and adapt their programs based on clinical data are better allocating costs toward cost drivers, and as a result have a greater probability of achieving sustained health care trend reductions and improved employee productivity.