I’m not sure about you, but I remember sleeping through Statistics class.
Now, ironically Regression to the Mean plays a key role at Andus in helping our clients contain their healthcare costs.
A basic understanding of this concept and how it applies to your group health insurance plan can have a huge impact on drastically reducing or even eliminating volatility (premium increases).
I will spare you the painful memory of Stats class. Simply put, Regression to the Mean is all about how data evens out.
It basically states that if a variable is extreme the first time you measure it, it will be closer to the average the next time you measure it.
Let’s look at a hypothetical situation and how the concept plays out in a benefit plan. An employee on your healthcare plan named Bob unfortunately has a heart attack resulting in $400,000 in medical and pharmacy claims in the year the heart attack occurred. Because of Bob’s condition, one of the following scenarios is likely:
Sadly, Bob dies prematurely
Bob makes major lifestyle changes and is on the fast track to good health
Bob remains sick or develops other chronic conditions and remains an ongoing high claimant
For purposes of this article, we will focus on scenario’s #1 (dies prematurely) and #2 (gets healthy).
It now comes time for your renewal and your current carrier delivers a whopping 40% increase. When you demand an explanation as to why the increase is so large, you are told that you had a high claimant. Assuming you are fully insured, the carrier shares very little if any pertinent claims data with you.
Not to digress away from the scope of the article but what typically occurs from that point forward is a series of negative reactions to reduce your increase.
Reactions like switching carriers and raising co-pays and deductibles, installing high deductible health plans, etc to name few.
Frankly, these moves don’t work and penalize most of your members in the plan who use very little or moderate levels of healthcare. You end up with disgruntled employees who are paying more to get less.
Meanwhile, your current carrier is giving you a 40% increase because they are trying to get their money back. They assessed your risk as “X” with a profit margin included and had to pay out significantly more due to Bob being a high claimant.
Our question is: Should that be your problem?
The answer of course is NO, you paid a premium to transfer risk. However, it almost always becomes your problem.
Now let’s look at how we at Andus view Regression to the Mean.
Looking again at scenario #1 (Bob dies), obviously a sad situation but it also guarantees that he will not be a high claimant on your plan next year. Under scenario #2 (Bob gets healthy), he is very unlikely to again repeat as a high claimant the following year.
This is classic Regression to the Mean and if you have an advisor that knows how to apply the concept and navigate the insurance marketplace, it can result in a drastically improved outcome for your benefit plan.
Under the scenario I outlined, you should not be taking an increase for claims that are not going to repeat.
Again, you paid a premium to transfer risk and if the carrier must pay out three dollars on one dollar of risk that they assessed, that’s their problem, not yours.
They win, lose, or break even across their book of insureds but in the end, they typically always make money. Carriers live in the world of Regression to the Mean so finding an “A” rated carrier that will assess your risk properly is critical to eliminating volatility.
Lastly and perhaps most important, if you are not receiving detailed claims data on your plan, the whole concept of Regression to the Mean is a moot point.
You need data and information to apply the concept properly and make educated decisions on behalf of your employees.
At Andus, our data driven approach gives you the clarity and control to build a better benefit plan for less.
Please call or email me below for a more detailed discussion on how this concept may benefit you.