A Benefits Data Solution Can Help You Track High Cost Medical and Prescription Spending. Here's How.

Benefits cost savings is weighing on the minds of many in our industry right now. The COVID-19 pandemic has affected budgets all around the world, and while employee health is still a top concern for HR and Benefits teams, the cost of employee health has never been higher. 

The average cost of health insurance for an employee and their family is estimated at over $20,000, and it seems to rise each year. Employee benefits experts are often tasked with measuring these costs, finding opportunities for savings, and ensuring programs are working. 

The first step in this process is finding out which pieces of your benefits coverage fall into the high cost category, and many benefits analysts start with medical and prescription costs. It’s not always easy for them to see these two areas of spending side by side, but a good data warehouse solution and benefits analytics tools can help. 

Some data solutions make it harder to find answers about high cost medical and prescription trends, but Artemis has explored this particular topic into one of our Standard Stories, a pre-built report that allows both an at-a-glance view and a deeper dive. Let’s take a look: 

Standard Stories cover some of the most important topics and questions for benefits advisors and self-insured employers. These dashboards give a 30,000-foot view of what’s happening with enrollment, overspending, high cost areas, utilization, and population health. 

Our first few charts on this dashboard dive into key metrics around high cost medical and prescription spending. Here are the highlights: 

• Just 1% of members (on this sample plan) are high cost claimants, or 251 people total
• 25% of total employer paid claims are for high cost claimants 
• The average cost per high cost member is around $200,000, and it’s going up compared to our previous analysis period 
• Top diagnoses for these members include neoplasms (cancers), musculoskeletal conditions, and genitourinary conditions. 
• High cost drugs for this plan include cancer treatments, anti-rheumatics, and cystic fibrosis treatments.
  

Next, our pre-built Standard Story for high cost medical and prescription gives you some information on demographics. This handy fan chart shows some background on high cost claimants. 

For our sample population, the majority of high cost claimants are subscribers, followed by spouses and dependents. 

The Standard Story even lets us look down to the member level to see our top 10 high cost claimants by member ID, condition, gender, age, and some cost information. This is all sample data, but it shows that the most expensive member on this plan is an employee aged 66, a male, and diagnosed with coagulation and hemorrhagic disorders. Other top conditions for these high cost claimants include bacterial infections, respiratory infections, and cancers. 

You’ll notice a little disclaimer at the bottom of this chart saying, “Your HCC medical + Rx threshold is set to $100,000.” This highlights one way in which Artemis Standard Stories is unique: it allows for some customization. If a benefits team has an especially tightly managed plan or younger/healthier member population, they may consider $50,000 in claims as a high cost claimant. Other plans with a number of retirees may want to raise this threshold. 

Another feature that allows customization is our filters. 

Filters let you see a smaller segment of the population in your data, and Standard Stories like this one let you select age bins, gender, home states, plan, relationship, subscriber status, and more. If we wanted to see just our female high cost medical and Rx members, we can simply click the box and apply the filter. Select multiple filters to see, say, only high cost members in California who are subscribers on an Aetna plan. 

We hope this look at Artemis Health Standard Story on high cost medical and prescription spending was helpful. A great data solution can help benefits advisors and self-insured employers understand their healthcare data, dive deeper on benefits cost containment, and gain confidence using their data to make great decisions for members.