One of the great challenges brokers face when recommending health plans and products offered by the health plan is determining the right choices for employers who have a high percentage of employees with chronic conditions. While the goal is obvious – keep employees as healthy as possible to maintain productivity and minimize presenteeism while minimizing per member per month (pmpm) costs – determining the best way to achieve that goal is not.

That's starting to change, however, thanks to a new generation of behavioral analytics that combine financial data on the spend with clinical data about outcomes to show which of an employer's plans are delivering the best value for each condition. These same analytics can also predict which chronic conditions are on the rise among the employee population, and within that population who is emerging to be high risk, as well as the risk of potential high-cost events down the road, such as surgeries or emergency department interventions.

Armed with this information, brokers can steer employees to health plans that will deliver the best outcomes for employees and their families at the lowest cost to all. They can also work with employers to encourage employees and/or their family members to make behavioral changes, such as using incentives for specific services by condition, that help them avoid expensive, life-and-productivity-interrupting events.

Data challenges

Of course, if it was easy everyone would already be doing it. The reason they're not is the data required for this type of in-depth analytics is fragmented across multiple data stores, including claims systems, electronic health records (EHRs), population health management (PHM), finance, and other siloed system and ZIP code data. Many of the answers are also dependent on the specifics of a particular plan, such as eligibility, co-pays, co-insurance and overall plan design.

Seeing the complete picture also requires bringing in de-identified data from health plans and other sources to create personas that represent different employee types. These personas deliver additional insights into who the employees are, using data such as their income and education levels, where they live (zip+4), family situation, ethnicity, co-morbid conditions, lifestyle choices and other factors to better understand the overall employee population, as well as the most appropriate use of healthcare services.

Developing risk scores

With this information in hand, brokers and employers can use the analytics to create risk scores for each covered member (employees and their families) that help them set priorities on plan design based on two factors: impactability and intervenability.

Impactability is the likelihood that the member will achieve a positive outcome by closing a care gap. With dollars limited, and the goal of improving outcomes while lowering or at least controlling spiraling costs, it makes little sense to pursue interventions or introduce health plans that will not have an impact on the member's health.

Intervenability is the likelihood that the member will become engaged in and follow a care plan, and eventually close the care gap. Health plans that invest in incentives or programs such as care management for members who are deemed unlikely to follow them are a waste of time, energy, and resources. It would be better to redirect those limited resources to areas where members will make the effort so positive changes can be achieved.

By creating health plan options that take into account the highest scores for impactability and intervenability (as well as the highest risk), brokers and employers can address outcomes and cost more effectively.

Extended value

The use of personas and risk scores extends beyond identifying whom to target. They also provide guidance on how to approach those members to make the changes.

For example, suppose the data indicates that a care management program should be included as part of a plan to address diabetes. Care managers at the payer can match the persona to specific, re-identified member profiles that include income, education, and other social determinants, and use that information to design a plan of care that is achievable. They can also see where obstacles exist, such as a lack of access to healthy foods in a particular neighborhood or town, so they can work with members to create alternatives that will work for their situation.

Meaningful reports

The final advantage next-generation behavioral analytics offers brokers is the ability to create comprehensive reports. These reports can be used to detail the progress the program is creating and the overall impact it is having. They can also be used to demonstrate where additional improvements can be made, or where a new issue has appeared that should be addressed, keeping employers ahead of the cost curve.

Demonstrating the value-add

Brokers that show employers how to reduce absenteeism and reduce “presenteeism” (preoccupied by non-work activities at work) elevate themselves from being just another “vendor” to a position of trusted advisor. They also move the focus of the conversation away from premium costs to a much larger picture.

Creating well-tailored, effective health plans that help improve outcomes while reducing the skyrocketing cost of managing chronic condition for employers and their employees is the key. Next-generation behavioral analytics make it happen.

David Hom is Chief Evangelist at SCIO Health Analytics®, an organization dedicated to using healthcare analytics to improve clinical outcomes, operational performance and business results. He is an internationally-recognized expert in the field of consumer engagement through programs such as Value Based Benefits and Employee Wellness.

Instant Insights /

Chronic conditions: How to help pick the right health plan for employees

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