How Analytics Helps Make Sense of Health Plan Components (Part 1 of 4)

Before you plan for the future, you must analyze the present. Data analytics will help you along this path. It may start by identifying health plan behaviors. Perhaps you need to investigate reasons for misuse of services.

Maybe you need to identify the barriers that block optimal usage. Consider how data analytics tools analyze these major components of a health benefits plan:

Component 1: Demographics

Identifies: Your benefit recipients. The average plan once had 2.5 people per employee taking advantage of health insurance benefits. Today that average is likely lower. One reason? Perhaps people have fewer children. Another? Some plans with richer-than-average benefits have many spouses who work elsewhere enrolled in them. Why the latter development?

Solution: Rich health insurance benefits are known as magnet plans. If you have a magnet plan, non-employee spouses may choose coverage through your plan because it has better benefits. The National Business Group on Health found that 34 percent of employers will require surcharges next year for spouses who can obtain coverage through their own employer, up from 29 percent this year.2

The intention is to encourage non-employee spouses to buy coverage through their own companies. There are good reasons for this development. With the exception of babies born prematurely, non-employee spouses create the highest risk of incurring claims. One reason? Spouses don’t have the same access to plan communication— including tips for staying healthy—that plan members have. A spousal surcharge is a premium increase that offsets the increased risk an employer plan may incur for insuring a non-employee spouse.

Component 2: Per Employee Per Year (PEPY)

Identifies: An average cost of how much employees and dependents cost your company plan.

Solution: Use this high-level look at expenses to benchmark benefit costs versus what competitors and peers spend. A significant variance from the norm may indicate the need to dig deeper, isolating the root cause driving the claims expenses. Once you identify the root cause, consider a holistic evaluation of changes needed.

Component 3: High Cost Claimants (HCC)

Identifies: Details about the employee population that account for the bulk of a plan’s claims and raises its total PEPY. Every company has claims. Some you can’t control, such as a serious accident or cancer treatments. Others may include chronic illnesses, like diabetes. Left uncontrolled without proper treatment, diabetes can lead to high cost claims. Most organizations experience an 80/20 rule; 80% of claims are incurred by 20% of a plan’s members. The best practice is to identify not only HCCs, but also the probability of future high-cost claimants that may escape a company’s radar.

Solution: Identify all self-insured claims and analyze them in the context of the deductible with a cost-benefit analysis. This is crucial. If the number of plan participants in a self-insured plan who surpass a $200,000 deductible (before stop-loss insurance takes over) increases by only five employees, that’s an extra $1 million out of your company coffers.

Stop-loss insurance protects self-insured plans against high claims. In this scenario, it may pay benefits once an individual exceeds $200,000 in claims. Think the deductible is high? Dialysis and cancer treatment are two areas that can bust a plan’s deductibles.

Even plans without continuous high-cost claims should analyze their stop-loss trigger. Any assessment should include a full risk analysis that includes the probability of future high cost claimants. Your claims might include one-and-done claims such as a premature birth and an auto accident, which create higher, onetime costs. To appropriately evaluate your stop-loss deductible, consider the data analysis and your plan’s risk tolerance level.

Component 4: Claims Expenses versus Budget

Identifies: How claims match up with your plan’s budget to pay claims. This is similar to a loss ratio in fully insured plans. When a plan has a ratio of 100% or lower, claims are as expected or better. A ratio over 100% means claims expenses exceeded what was budgeted.

Solution: Work to achieve an 80% to 90% loss ratio. When this percentage exceeds 100%, dig deep into claims histories to find out why. The reasons may be systemic; maybe a population of many workers has untreated chronic diseases. Or maybe investigation will show that one expensive surprise claim—maybe a serious auto accident injury—skewed results. In any event, this piece of data analytics will give you actionable information that can drive change to reduce future claims expenses.

Data Analytics Outside a Vacuum

When data analytics identifies areas that can improve, it reports past results. When analysts dissect a report, they identify the best ways to take action. Change, however, doesn’t occur in a vacuum. Data analytics for a complex, integrated benefit like health insurance can lead to altering one aspect of a plan and unintentionally creating a significant impact on another. Some changes create the desired results. Others produce unintended consequences.

For example, Health Maintenance Organizations (HMOs) became popular as a way to manage healthcare costs through a single entry point—managed care. While they helped manage costs, they also limited care to those needing it, according to some people. Lawsuits —an unintended consequence—followed.

Next, Preferred Provider Organizations (PPOs) became more popular, giving consumers more choice than HMOs afforded. Patients were allowed to see specialists without referrals, and services within the preferred-provider network were generously covered. What happened? A significant overuse of services.

Today, High Deductible Health Plans (HDHPs) have become common, driven by consumerism and a desire to reduce employer plan costs. Will this shift also have unintended consequences by causing plan members to avoid physician care when needed due to the difficulty of paying for the high deductible?

Only time will tell, but predictive data analysis may help employers decide if HDHPs are, indeed, the best approach.

Read the full report HERE

This is part 1 of a 4 part series containing excerpts from the Oswald and Assurex Global Whitepaper data analytics whitepaper, Health Plan Secret Weapon: Integrated Data Analytics – Making Sense of Data to Make More Informed Benefit Plan Decisions.

About Denise Mirtich:

Denise Mirtich is the Data Analytics Leader and Co-Chair, Women’s Leadership Council, for Oswald Companies. She empowers clients by using Oswald’s advanced information management systems and technological innovations to enhance the client experience by providing high-value deliverables and data-driven guidance in an easy-to-digest format.