What if your company could predict which of your top value customers were at high risk of defection and proactively engage them in personalized communications to prevent them from leaving? They probably already do that. What if your business needed to know the status of expensive product shipments that were traveling the world and at risk of being delayed? Would you need to be able to automatically trigger messages to better manage the risk and unnecessary costs? You most likely are doing that too. This means that your company is using predictive analytics.
Predictive analytics have long been used in various industries, such as retail, telecommunications, transportation and even major league baseball. Unfortunately, employee wellness has been lagging in applying predictive analytics and data-driven communications for some time now. Something just doesn’t add up – especially since employee health costs are the third highest cost for companies. According to a 2016 SHRM national survey, on employers spent an average of $8,669 per employee annually on health care coverage. That’s big money. So what’s been holding up progress?
The challenge has been applying predictive models that are able to constantly learn and adapt to a dynamic world. Additionally, companies struggle with integrating real-time data that triggers communications, thus proactively influencing health behaviors. Another barrier has been delivering relevant and timely messages in creative and engaging ways. To top it all off, employers must do all of these things while complying with HIPAA standards.
Richard Kersh, CEO of Human Factor Analytics, describes predictive analytics as “the unbiased voice of data.” This technology is able to tell employers quite a bit about their employees’ health and needs – sometimes even before the individual is aware of their own risk. The benefit isn’t just limited to employees. Any data collected on spouses, children, and other family members can now be aggregated and analyzed to provide the fullest care and the biggest possible savings.
Wellness platforms aim to reduce healthcare costs by identifying and targeting general populations as well as at-risk groups to prevent certain conditions. One of the most effective tactics is to engaging at-risk employees in a variety of healthy activities and encourage regular health screenings. But how can employers know who is at risk for these conditions, especially if employees are not participating in health screenings and self-reported health risk assessments (HRAs)? That approach often misses the most significant source of costs — the high- and rising-risk employees who don’t participate.
Gallup research shows that 60% of U.S. employees who work for companies with a wellness program know that the program exists, and only 40% of those who are aware of the program say they actually participate in it — that’s just 24% of employees participating.
This is a critical drawback in the traditional strategy of predicting risk solely from those enrolled in the company wellness program, since just 5% of the insured will incur nearly 50% of total healthcare costs and it is not uncommon for less than half of the insured to be screened.
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.
Employee wellness shouldn’t be a one size fits all solution. Predictive analytics creates an improved path of self-care as unique as the health situation, motivations, and personality of the individual. 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 now have the answers.
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