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Decision support functionality has been gaining popularity in the Benefits Administration space for quite some time. Decision support tools can be an added functionality of your existing Ben Admin system or it could be a stand-alone tool which can “bolt on” to your existing Ben Admin system. The level of decision support varies drastically from education only to comparison tools to more sophisticated recommendation engines.

There are a number of stand-alone decision support tools that exist out there- ranging from extremely complex to “third grade” simple. The avatar model has gained popularity with the onset of the Defined Contribution approach to benefits. If the avatar solution is not offered via the Ben Admin system, there are multiple “bolt-on” options that exist; Jellyvision, Navera, and WebMD are just a couple examples of more serious competitors. These types of solutions can range across the board depending on what the employer is trying to accomplish. For example, some will only offer guidance on Medical plans and others will help evaluate voluntary benefit offerings.

The tools fall into two categories: “help me choose” or “help me use.”  Help me choose what option is best utilizing decision support tool predictive modeling capabilities or help me use the Benefits Administration system to select my benefits.

The graphic below highlights some of the competitors of this decision support approach, and how they are seen in the market.

decision support

(We’d like to give our good friend, Rhonda Marcucci, a shout out for providing the foundation to put this graphic together.)

Recent studies indicate that less than 5% of employees actually use the decision support tools offered, unless there is some form of incentive to go through the system. If no incentives are offered, there is the potential for an additional cost without much return.

Does your organization utilize a decision support tool? Have you used one when enrolling in your benefits? Let us know your thoughts on decision support by commenting below.