The role of predictive analytics in wellness programs
Predictive analytics have long been used in various industries like retail, telecommunications and even major league baseball. However, employee wellness has been lagging in applying the best practices of this tool as well as data-driven communications.
The challenge has been applying predictive models that constantly learn and adapt to a dynamic world and integrating real-time data that triggers communications to proactively influence behaviors. Another barrier has been delivering these relevant and timely messages in a creative and engaging way, all while complying with HIPAA rules.
Predictive analytics can provide measurable and validated cost savings for both employers and employees. It also creates an improved self-care path as unique as the health situation, motivations, and personality of the individual to which it is tailored.
Richard Kersh, CEO of Human Factor Analytics, describes predictive analytics as "the unbiased voice of data." Indeed, this technology has the capacity to tell employers a lot about their employees' health and needs, sometimes even before the individual is aware of their own risk. The benefit is not just limited to employees, however -- any data collected on spouses, children, and other family members can also be aggregated and analyzed to provide the fullest care and the biggest possible savings.
A better view into your risk pool
Wellness platforms aim to reduce healthcare costs by identifying and targeting general populations as well as at-risk groups to prevent certain conditions by engaging employees in a variety of healthy activities and regular health screenings. But how can employers know who is at risk for these conditions? It is especially difficult when most wellness programs identify risk only for employees who choose to participate in the wellness program through use of biometric screenings and self-reported health risk assessments (HRAs). That strategy often misses the most significant source of costs -- the high-risk and rising-risk employees who are not participating. This a critical flaw in the traditional strategy of predicting risk solely from the HRAs and biometric results of those enrolled in the company wellness program, since just 5% of the insured will incur nearly 50% of total healthcare costs.
This is where predictive analytics comes into play. With access to all insured health records and medical history, pharmacy data, workers’ compensation claims and data provided by biometric screenings and HRAs, employers can now accurately locate at-risk populations and engage them with integrated targeted communications. With real-time information, data-driven programs can now identify risk pools and formulate solution strategies to help prevent disease and disorders and keep healthcare costs down.
The Employers’ Health Coalition in Fort Smith, Ark. utilizes a pharmacy cost-reduction strategy that includes therapeutic equivalent medications in combination with referenced-based pricing, which produced 18% savings in 2017 over the previous year. These savings are tracked through analytics and members are sent drug savings reports to provide evidence of benefits. The coalition is also utilizing analytics to track the economic efficiency of their proprietary member health network, as compared to outside networks.
Employee wellness need not be a one-size-fits-all solution. Employers have often been stumped by essential healthcare questions, such as "Who needs help? How do we help them? How effective was our solution?" With the clear voice of predictive analytics, employers can get answers to these important questions.