Editor’s note: Integrate technology with your wellness program to both inspire participation and motivate workers. As an additional benefit, you’ll be able to collect even more data. See this related article The Right Way to Use Tech in Your Wellness Program.
Many organizations collect data about how employees use their benefit plans, but without context, data can just be an expansive jumble of numbers that serves little to no purpose.
The important factor is what happens to that data after it is collected. Does your company simply let it sit, or does it take steps to make sense of it and put it to good use?
When utilized to the fullest potential, data can become a wealth of information to help companies make informed benefit plan decisions through insights into why claims might be unusually high or why employees shrug off preventive health benefits. Though it is a big undertaking, building data analytics tools can create actionable information to improve health; increase cost efficiency and boost worker satisfaction.
Health insurance and data analytics
- Some benefit plans with richer-than-average benefits attract employed spouses to enroll in them, also known as “magnet plans.” A spousal surcharge premium offsets the increased risk an employer plan may incur by taking on spouses who do not have the same access to communication and health information.
- Per Employee Per Year (PEPY) is an average cost of how much employees and dependents cost your plan. Look at what competitors and peers spend and see if the data indicates a need to dig deeper and find the root cause of what is driving claims expenses.
- High Cost Claimants (HCC) are details of the employee population that account for most of your plan claims and cost increases. Stop-loss insurance protects plans against high cost claims. Analyze stop-loss triggers and evaluate your plan’s risk tolerance level.
- Claims expenses v. budget. How do claims match up with your plan’s budget to pay claims? Work to achieve an 80 to 90 percent loss ratio, and dig deep into histories to help reduce future claims expenses.
Key metrics & solutions
Data analytics have the power to identify hundreds of ways a company can shape up its health plan, including in prescription drug costs, which are becoming increasingly expensive.
- Metric: Percent of prescription drug spending versus the total plan spending. Consider hiring or changing your prescription benefit manager if the ratio of prescription drug spending is more than 20%.
- Metric: Percent of specialty drugs to overall drug spending. If the cost of specialty drugs increases exponentially, it is imperative to have a specialty drug management partner external of a carrier solution.
- Metric: Metabolic and pre-metabolic (two or more chronic conditions) increases. Look to improve employee lifestyle choices and offer prescription programs to pre-metabolic individuals.
Integrating findings and solutions
As a data analytics leader for Cleveland-headquartered Oswald Companies, I am there to look for consequences in behaviors and risk, communicate actionable information and provide deep knowledge for assessing an employer’s needs.
As an example, my company helped a nonprofit group as it tried to find an affordable and attractive benefits plan that would help retain the best talent. The example is discussed in “Health Plan Secret Weapon: Integrated Data Analytics.”
Preventive services offered through wellness programs can help identify conditions before they worsen, and normal doctor visits can yield needed changes that serve to reduce unnecessary care. In one case study from a company analyzed in the white paper, the addition of preventive measures resulted in a 2.5% decrease in overall ER visits and a 17.1% decrease in ER visits used to treat chronic conditions.
In another example, a company developed a three-year, integrated solution to provide minimum essential coverage, a health advocacy program, wellness activities and a communication campaign. As a result, emergency room chronic condition costs decreased by 50%.
The data is there to use
With products like Fitbit and Jawbone that can collect and submit data from the wrist, to health care software that allows doctors to receive a full picture of an employee’s medical records, the technology is here to compile a vast amount of usable health data for benefit plans. And in today’s competitive environment, quality benefit plans are crucial to attracting and retaining the best talent.
Key principles guiding the use of data analytics tools to produce actionable data:
- Define specific objectives.
- Understand challenges thoroughly.
- Identify the root causes of inefficiencies.
- Then head down the path of establishing corrective measures for driving down costs and improving health.
This article originally appeared in TLNT. To view the original article, please click here.