Medical and Rx Claims Data: Preparing for your Employer Client Strategy Meeting Part Two

Brokers, Consultants and Employee Benefits Advisors:

?Whether you work with a data aggregator, house data internally or leverage claims data reporting from carriers and TPAs it takes bandwidth and bench strength to translate those data assets into meaningful recommendations based on where your client’s numbers suggest the need for action.

In Part One we addressed some of the most important data analytic categories to focus on, in order to identify areas of focus.

These included:

Diseases and Condition: Every client has a unique set of conditions to focus on. Each condition has a unique set of claims data points to consider.

Risk Stratification: Powerful algorithms drive population health insights to enable employers to consider future costs of their member population.

Cost Stratification: Understanding what clinical conditions drive certain medical and Rx claims bands can also be leveraged to predict future costs. These insights can inform employer strategies.

?High-Cost Claims: The single greatest driver of year over year cost changes for most employers will be the number of claimants with over $50,000 a year in spend, and the costs they incur. Many of these costs can be mitigated with smarter strategies.

Other categories routinely reported on, include:

Pharmacy: Over the past decade- pharmacy data are increasingly accounting for over 30% of overall spend for many employers. Whereas routine reporting often still looks at generic use and mail-order pharmacy- these are no longer the top drivers of costs. Specialty medicines (think the pharmaceutical commercials we see advertised on the nightly news) are often the key drivers of trend increases for an employers’ plan.

Utilization and Place of Service: Utilization data including physician office visits/1,000, hospital stay visits and outpatient hospital services often help understand where and how healthcare activity is trending.?These data can also identify practice patterns, such as: hospital services that could be delivered in a less-expensive outpatient setting. Underutilization of primary care, preventive services and behavioral health may eventually drive greater costs than overutilization of emergency departments and medical specialists.?

?Quality of Care: Measuring quality of care includes an ability to measure gaps in care for members. It is important to understand that only certain chronic conditions (such as diabetes, asthma, cardiovasacular disease and preventive services) have identifiable gaps in care. Overutilization, or misuse of key diagnostics and treatments can also assist in identifying foci of lower quality care.?

?Predictive Analytics: One of the advantages of working with a data aggregator is ready access to a host of algorithms that offer risk and cost insights. Some of these insights include:

• Risk Scores that cull through each member’s data and demographic information to provide a predictive multiplier for future costs
• HCC Predictors that identify this year’s non-high-cost claimants that are likely to become high-cost claimants (i.e., >$50,000) next year. These analytics can sometimes predict how many of this year’s high-cost claimants are expected to continue to go beyond the $50,000 threshold next year.

Data Dimensions

?Aggregate data provides a holistic picture of a membership cohort for an employer. It is crucial, however, that data analysis include deeper dives into subsets of the data. Some examples include:

• ?Relationship: Looking at employees only is the best way to measure the impact of workplace programs available only to associates. Analysis of spouse and dependent cohorts may inform plan design.
• Geography: ?An employer with large plants (and member population centers) in Louisville, KY, Huntsville, AL and Odessa, TX may find three very different patterns of health services utilization and medical/Rx spending.
• Plan Design: Whereas it is vital to “roll up” all plans to evaluate the big picture in medical spending and population health- analysis by plan will reveal important differences and behaviors that are important to thinking about the following year’s plan design.

Employer medical plan data assets are a lifeline for determining which strategies to prioritize. These same data assets, and how they are reviewed, analyzed are often the key lifeline for brokers and consultants who work as employee benefits advisors.