With the recent passage of the 21st Century Cures Act, the job of small group health insurance brokers has gotten markedly more complex.  Not only will they be evaluating which small group plan or plans are a fit for a client, but they will now also need to evaluate whether or not that client should shift to a health reimbursement account (HRA) and reimburse their employees for the health insurance they buy as individuals.  Given this complexity and the growing demand for better and more complete information, data driven digital solutions will be the only means for a broker to reasonably serve their small group clients. 

To get a sense of the scale of the issue, let’s take a look at the volume of offerings in two locales: New York City and Portland, Oregon. In New York City, approximately 744 small group plans are offered by eight carriers, and 300 individual plans are offered by 10 carriers.  Underlying these plans are more than 26 networks and 14 formularies. Similarly, in Portland, approximately 329 small group plans are offered by 10 carriers and 63 individual plans are offered by eight carriers.  Underlying these plans are more than 23 networks and 14 formularies.  The good news is that choice abounds and the chances of meeting the needs of the client and their employees are very good.  The challenge is how do you efficiently do so.

There are four key elements to a quality plan recommendation: plan design, premium, provider-network fit and formulary fit.  Each of these elements must be considered in the context of the others. 

Under the Affordable Care Act, there has been a homogenization of plan benefit designs across carriers, each offering comparable combinations of deductibles, maximum out of pocket (MOOP) expenditures and coinsurance. This isn’t a great place to start to determine differentiators.  However, provider-networks are. 

Finding the right provider-network fit

Provider-networks differ dramatically between, and even within, carrier plan offerings.  Today, data is available to enable dynamic small group provider-network disruption analysis.  By surveying employees for the doctors and facilities they want to use, a network “match” can be instantly, and inexpensively, developed for each network underlying each available group plan.  Simultaneously, each employee’s doctors and facilities can be compared against the network’s underlying individual plans to determine a fit in that market as well.  The results could look something like this for each market and each plan:

Finding the formulary fit

The same kind of process can be used to develop a formulary disruption analysis.  As the past summer’s EpiPen controversy illustrated, formularies differ significantly between carriers and even within carrier plan offerings.  And with roughly half of all adults in the U.S. on prescription drugs, which account for 20 percent of U.S. health care costs, drug cost and coverage are significant elements of the plan recommendation process.  By surveying employees for the prescription drugs they take, a dynamic disruption analysis can be generated, quickly and at minimal cost.  A comparison of two plans based on the drugs put forth in an employee survey could look like this:


Discussions about these types of tools typically lead to two objections.  The first is that small business owners only care about their personal doctors and drugs being covered.  While business owners certainly care about their own physicians and drugs, they also know that benefits are put in place to retain employees.  If they can offer a better benefit solution — one that’s data-driven — while meeting their business’ financial objectives, they will embrace the process. 

The second is a concern about the broker and/or the company having access to each employee’s selection of doctors and drugs.  This can be addressed by the technology that the broker and/or client is using.  Survey results and the underlying analysis can be firewalled off from the client and even from the broker.  This type of solution can even improve employee response rate. If employees feel comfortable that their oncologist or their mental health professional will not be shared with their employer, they are more likely to respond. 

Different quoting, enrollment and benefits administration platforms take varying approaches to address the privacy concern.  But the value of generating this actionable intelligence to the group, its employees and indeed the broker, should not be overlooked. 

With network and formulary analyses in hand, and in conjunction with plan benefit design and premiums, the process of a data driven plan selection can begin.  

Plan recommendation following network and formulary fit

The results of each group’s analysis will be markedly different, providing the broker with a data-driven method of differentiating carriers and plans for each group client.  The results of these analyses can be striking.  For any given group, one or two networks and/or formularies tend to rise to the top.  But a different group in the same area may well see completely different results based on the make-up of that client’s employees.  This kind of network analysis delivers actionable intelligence to the broker and client that can be used to select one or more group plans from the field of options, evaluate the benefits of shifting from the group to the individual market and help employees choose the right plan based on their individual doctors and facilities. 

While there are several reasons why a group may choose to shift from the group to the individual market, let’s look at one reason that may specifically arise from the data.  If the best network “fit” for a group plan is low, but, through the individual market, high, then employees may well benefit from a shift to the individual market where they can better find a plan in which their preferred doctors and facilities participate.   

If the employer chooses to stay in the group market, network and formulary fit provide gross filters to cull the field of plan choices.  And if the best network and/or formulary fit plans are too expensive for the group, this same data can be used to engage employees in an effort to seek buy-in on a higher employee contribution for those better fitting plans.  For example, “We can offer a plan in which most of your doctors participate, but we will need you to contribute 50 percent, or we can offer a plan with half of your doctors with a 25 percent contribution.”  Some quoting platforms provide dynamic calculations of employer/employee dollar contributions on a sliding scale, facilitating the process.  

The 21st Century Cures Act just made a broker’s already difficult job even more difficult. There are many options, and there is simply too much differentiation between the plans’ underlying networks and formularies, to simply pick the most commonly recommended plan or roll the group over to the successor plan the next year.  A data driven analysis will empower the broker with actionable information for the business and its employees, helping them make the best plan choice possible.