Brokers do not have access to claims data for small employers and as a result: small businesses are making the wrong choices. This leads to employers making costly mistakes when it comes to their benefits plan. In fact, did you know that small employers typically pay up to 25% more for their benefits than large employers? On top of that, 6 million small employers pay their advisors $18 billion per year in major medical commissions for plans that may not even fully suit their needs.
Local brokers have a very difficult job of matching employers with a cost effective health plan amid rising costs and new competitors online. Read on to learn what data brokers are using today and how small group claims data is the missing piece in the planning and underwriting puzzle that will set them apart in their market.
How brokers advise small groups
Typically a broker works with a small employer to identify their needs for their upcoming policy term across all their benefits: major medical, supplemental, pre-tax accounts, and more. Unlike the case for large employers (also called groups, as this term covers associations, captives, and MEWAs), carriers do not release aggregated claims data that’s typically used to select and underwrite a policy. For example, in a large group, a broker can assess based on past medical or prescription claims, if a high deductible plan may work for the employer and their employee needs.
Especially since we’re talking about small groups, large claims or costly health conditions can have a disproportionate and substantial impact. The lack of data drives up the cost for small groups considerably. By some accounts, only 5-10% of small groups end up qualifying for stop-loss insurance that allows them to switch to an often cheaper self-funded plan.
For small groups, there is no data to really go on about the type of plan they may need. Thus, brokers and underwriters have come up with some creative–and imperfect–ways to guess what type of plan is suitable for the small group.
There are four main types of data that are used to assist in the small group benefits planning and underwriting process:
- Demographic Data
- Individual Health Questionnaires
- Behavioral Data
- Comparing Similar Groups
But, as we will outline, these methods are missing a huge piece of the puzzle: real small group healthcare claims data. Without which, underwriters and brokers are imperfectly assuming what health conditions actually exist in the group and how the plan has been utilized in the past.
We’ll outline the four main types of data used today, and offer a fifth: real small group claims data.
Demographic Data
Demographic data is the most common input to underwriting a small group. The employer provides the broker a census, or list, of their employees and general attributes including gender, age, 3 digit zip code, number of dependents, and dependent’s genders and ages. These demographic attributes are obviously extremely general and offer no indication of the health conditions of the population. You can imagine there are wide variations in health conditions based on these factors! Two individuals with a similar age have a variety of different health conditions. Beyond that, the assumptions based on this data are very broad. For example, a 30-year-old woman would be charged one of the highest premiums based on a broad assumption she may have children within the next calendar year, have a high claim, and add a dependent to the policy.
Individual Health Questionnaires
To supplement this obviously incomplete data set, the industry has also added what is called Individual Health Questionnaires also called IHQs. These lengthy questionnaires ask workers about lifestyle, stress, or physical health, and many require a biometric screening or physical exam. Approximately 30% of small employers use IHQs as part of their underwriting process. Firms and insurers may use the health information collected during screenings to target wellness offerings or other health services to workers with certain conditions and to understand employee health risks. Some small employers make this a requirement to participate in the policy.
There are many challenges of IHQs. For one, employees are implicitly incentivized to be dishonest so they can get a better insurance rate for the group. Beyond that, it’s human nature to lie about your health and project yourself as a better human than you may actually be. In a survey of 500 individuals, 45% admitted they lied to their doctor about smoking frequency and 43% lied about exercising, with 75% citing embarrassment as the main reason for lying.
Secondly, distribution and completion of these surveys widely varies. Only 32% of all employers said their IHQ was available digitally, the remainder we’re assuming are completed via paper copies. Manually tracking the completion causes additional work for employees, employers, and brokers alike. There are solutions on the market that offer a digital IHQ, however, completion issues and accuracy issues persist.
Behavioral Data
Newer to the market, behavioral data is meant to tell underwriters how risky a certain population is based on online and offline behavioral data. This data is similar to the data used to target advertisements online, with highly relevant and highly specific attributes about an individual. Using data that’s been collected on each individual, it’s said that brokers and underwriters can more accurately assess risk. The problem, however, is that the behavioral data is often about social factors rather than clinical factors or what your medical history may be. This also, is an incomplete way to assess a group, and the companies that offer behavioral data are the first to say this.
Comparing Similar Groups
A less futuristic, though commonly used method, is comparing similar groups. For example, if a manufacturing company of 30 employees with an average age of 49 with two dependents found success with a HMO plan last year, brokers sometimes use this as a yardstick that this plan structure may work for another similar company with similar demographic makeup. For obvious reasons, this method presents its own set of inherent limitations. Without knowing the medical or claims history of a group, you can’t truly assess if a plan will work for one group or another. It’s like saying if two people are both six foot two that they’ll have the same shoe size!
The missing piece: small group claims data for underwriting
For years, it’s been a common assumption that small group claims data is impossible to obtain, thus these imperfect solutions outlined above have emerged. The reality is, carriers simply do not want to be bothered with providing data on groups under 100 lives (perhaps because they’re making so much money off inaccurate plans!). This leaves brokers and employers in the dark, with employers bearing the brunt of the costs.
But we’ve cracked the code. Small groups claims data is available. Using our proprietary Claims Harvesting technology, we can gather current and historical claims for any employer group of any size, from over 250+ carriers.
Working with some of the country’s leading brokerages, we’ve built claims reports designed to help you advise your small group clients. We securely harvest data from the employee’s EOB and claims statements including:
- Date of service
- Type of claim
- In Network vs Out of Network
- Provider
- Deductible usage
- Amount billed
- Amount allowed
- Amount paid
- Patient responsibility
- Copay
- Coinsurance
- Deductible
- Rx information
Then based on the claims data, our broker clients go beyond just the data with insightful reports that help brokers confidently recommend and tailor plans to the unique needs of the employer group, and secure stop-loss insurance.
- Small Group Risk Assessment
- Underwriting report
- Line level claim detail report
- Summary claim detail report
- High-risk summary report
- Total number of enrolled
- Total number of unenrolled
- Plus, download the data directly for further analysis
Takeaways
Brokers have a very tricky job: matching employers with a cost effective health plan amid rising costs, complexity, and online competitors. Unfortunately for brokers that work mainly with small groups, they’re left in the dark without claims data that’s typically used to underwrite policies. As a result, the industry has come up with some clever ways to assess a group’s risk. However, brokers can access small group claims data with TPA Stream, giving them unmatched accuracy in plan creation and the insights they need to secure better plans for their clients.
Brokers can access the same insights they’d have for large groups on a small group scale, opening them up to confidently advise self-funded plans or justify possible rising premiums. Especially in the case of small groups, large claims or costly health conditions can have a substantial impact. Saving your clients potentially hundreds of thousands of dollars will win you more business in your marketplace.
Brokers interested in learning more about small group claims data can schedule a demo with us of our new platform.
If you’re a small employer, we can connect you with our network of brokers who have access to small group claims data. The process is simple, with a simple and safe connection to an employee’s claims portal. All data is safely stored in SOC II Type 2 database and we are HIPAA-compliant.
Read more: Healthcare Claims Data Is Becoming Expectation… And Is Possible
Get started: Schedule a demo of our claims analytics platform