Drug Claims Data: The Duct Tape of Group Benefits

For example, if an employer has 1,500 lives covered under its plan, the set of transactional claims data for each year can include in the range of 12,000 to 15,000 (or more) rows of paid claims data. This differs significantly from the aggregate-level data that plan sponsors have access to throughout the year—that is, at renewal time—that documents the top 50 or 100 drugs by amount paid and/or number of claims paid.

Aggregate-level data is summary-level data often rolled up at the DIN (specific drug product) level. Transactional-level data captures all of the unique details of each claim made to the plan. It is this data set that is exceedingly rich and full of unique and useful information to help manage multiple areas of group benefits experience, hence its versatility and value.

The following areas represent possible uses of transactional-level drug claims data that offer detailed insight for plan sponsors that are looking to manage their benefits costs. It is by no means an exhaustive list, but it offers practical and useful applications of this rich set of plan information for plan sponsors and their advisors.

Drug Plan Performance Reviews

If a plan’s year-over-year (YOY) cost inflation within the drug plan is 12% in the current environment, does that mean the plan needs to be scrapped altogether? Conversely, if the YOY increase in early 2009 turns out to be in the area of 4% (adjusted for changes in plan population), does that mean that everything is great and we can move on to the next item on the to-do list? The answer in both cases is simple: it depends.

What is good for one 800 or 8,000 life group can be very different for the 800 or 8,000 life group down the street or within the same industry. The key is looking at the plan-specific experience to address the following questions.

• How are existing plan management tools working to assist in responsible cost containment?

• What areas of the current plan experience can be optimized without the need for structural changes to the plan design, and what is the financial implication of each area? This is a key consideration for larger plans or those with existing contracts where making changes to the structure of the plan design is not feasible.

• How long will the current plan design meet the needs of the plan sponsor from the perspective of cost containment?

It is very common to see in medium- and large-size plans immediate opportunities for optimization without the need for plan design change equal to roughly 4% to 8% of total plan spending. Some readers will not believe that figure because they will assume if that’s the case, they would already know about these opportunities. However, until a plan looks at its robust transactional-level data, it is not possible to assess these findings and calculate the returns. Looking at tens of thousands of claims in detail is a far cry from reviewing a top 50 drugs list.

Population Health Analysis

A major issue many plans have in supporting the need for plan design change and/or investment in health-and-wellness initiatives is a lack of financial data to support the need and facilitate the measurement of detailed return-on-investment calculations following implementation.

A population health analysis is an evaluation of the disease state and demographic profile of the group than can address the following questions.

• What factors are driving plan costs, and to what degree? How has that changed over the past two to three years?

• What will the future cost trends be within the plan based on the existing demographic and disease state profile? What is the current saturation rate of age-related chronic conditions?

• Which disease states are most prevalent in the workplace as opposed to among the spousal/dependent population? This will impact choice of workplace-based wellness program options.

• Are there significant gender differences in our disease state profile within the employee population? If so, what are they, and how do they impact our wellness strategy?

• What are the key disease states within the employee population that have the highest spending on co-morbid diseases, and how will that impact our wellness strategy in order to target the areas where we can have the biggest impact?

Integrating Drug and Disability Claims Data

The most intriguing aspect of drug claims data is that it can be used to reverse-engineer the disease state profile of the group. This opens up a world of opportunity in improving disability management. Absence, short-term disability (STD) and long-term disability (LTD) data sets can all be integrated with drug claims data to determine the total burden of illness of key disease states to the employer, and can assist in identifying areas where absence and disability experiences can be better managed.

The integration of drug claims and disability data allows for the following.

• The ability to measure adherence with drug therapies to determine if a lack of adherence to medication regimens can be implicated as a leading indicator in determining incidence and length of STD and/or LTD claims.

For example, the integration of these data sets can assist a plan sponsor facing a significant increase in disability claims related to mental illness to determine whether or not these claimants are actually taking therapies to treat the appropriate condition, whether the dosing is therapeutic, and whether or not the claimant is actually compliant with their drug therapy. (It should be noted that in both these cases, disability and drug claims data are blinded to protect the privacy of the plan member, but a common encryption key allows for data integration to answer these types of questions.)

• Understanding whether barriers to accessing certain drug therapies are contributing to a greater number of disability claims in certain areas.

• Determining which disease states are commanding the most resources (both on the drug and disability side) and which ones can be impacted by optimizing existing disability management programs.

• Assessing the drug utilization differences between employees at work and those on disability, and whether or not drug utilization patterns allow for predictive modelling of future disability claims.

Assessing the Financial Impact of Proposed Plan Design Changes

If a plan sponsor is faced with the need to redesign the plan to facilitate both near-term and long-term cost containment, it is very useful to use two to three years of existing drug claims data to determine what the financial impact of each proposed plan design option will be. It might surprise a majority of plan sponsors to see what kind of financial impact relatively simple plan design changes can have on the plan experience that are easy to communicate to plan members, compared to complicated changes that lead to confusion, frustration and dissatisfaction among plan members—along with limited cost containment for the plan sponsor.

Completing detailed financial models using existing claims data also allows for the HR team to build a business case for necessary change that supports the selection of a particular option. The existing claims data can also be used to assist the plan sponsor in determining how many unique claimants would be impacted by each proposed change to the existing plan design. Every household has a roll of duct tape on hand to deal with issues that arise. Plan sponsors should treat transactional-level drug claims data the same way and use it to deal with existing and emerging issues within their own experience before the untreated leaks lead to exceedingly costly repairs.