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How Does Your Healthcare Plan Stack Up? The Path to Better Benchmarking

Healthcare costs are soaring. Consider that in the U.S., costs for employers are projected to increase by 7% in 2021, with claims expected to return to normal levels after a year of claims activity suppressed by the novel coronavirus (COVID-19) pandemic.[1] The crisis has further complicated planning and cost management, as its long-term effects on employee health are uncertain at best. In addition, organizations are in a constant battle to keep plan costs manageable without jeopardizing employee satisfaction, which can affect talent attraction and retention. To inform future healthcare spending, companies need comprehensive employee insights and detailed benchmarks to see how they stack up against peers.

But HR leaders are tired of hearing the answer to this problem lies “in the data” — and rightly so. They don’t have the tools they need to unpack those insights and quantify the value of their healthcare programs. And oftentimes, traditional measurement approaches don’t reveal the full picture.

Why traditional measurement approaches fall short:

Stretched Assumptions IconStretched assumptions: Many standard reports and metrics apply layers of assumptions and adjustments on top of historical data. These assumptions can introduce biases that compound over time, resulting in the measured success of a program becoming more dependent on the chosen assumptions rather than on recorded data.

Cherry-Picking IconCherry-picking: Important data points, such as large claims or terminated groups, are often excluded from measurement, resulting in overly optimistic cost results.

Bias IconBias: Vendors often report improvements to historical metrics such as reductions in inpatient admissions. However, healthcare is always evolving and unless the comparison points also evolve, employers may find it difficult to separate true overperformance from  simple trend-following.

Enter precision benchmarking. This model improves on traditional measurement methods and provides employers with unparalleled transparency, arming them with actionable insights and helping them answer the questions that matter most.

How Do Our Costs Compare with Peers with the Same Geographies and Risks?

A precision benchmarking model, such as Aon’s Cost Efficiency Measurement (CEM) analysis, uses machine learning technology to quickly and efficiently analyze information from millions of members from hundreds of employers, objectively comparing a company’s overall cost trend with the results from a matched control group (Figure 1).

Unlike traditional benchmarking models, this control group is rooted in real member data and has been rigorously matched on factors such as geography, demographics and more than 25 comorbidities across tens of millions of market members. Precision benchmarking also uses detailed claims data with related data that organizations collect on their health plans, enabling a more rigorous and accurate analysis that matches records across many dimensions.

Cost Benchmarking via Member Matching Chart

Figure 1: CEM Cost Benchmarking via Member Matching

The improved matching process also makes it easier to understand if an individual company (with its specific population’s health risks) really did have a favorable year, or if the market (correlated based on a similar risk mix) performed just as well (Figure 2).

Aon Cost Efficiency Measurement

Figure 2: Comparison of Employer and Control Group Spending

Instead of guessing what would have happened without the employer’s actions, the objectivity of custom control groups allows us to reliably estimate that the sample plan has beaten market trends since 2016.

Where Are Our Areas of Opportunity?

The CEM model can dig deeply into disparities between inpatient and outpatient care spending and drug categories (specialty, generic, brand) to understand the gaps in healthcare spending efficiency relative to the market. Companies that want to look at specific cohorts can do so by matching members to custom control groups; for example, an organization might compare the drug spending and utilization patterns for its diabetic population with a comparable population and find it is spending 7% above the norm.

Illustrative Output: Top Five Chronic Conditions Cost

Figure 3: CEM Sample Output — Top Five Chronic Conditions

Among Employer ABC’s top five most prevalent conditions, precision benchmarking shows that the diabetic costs for this population are above norms.

Analyzing this population using matched control groups makes it easier to see where an organization can make a valuable impact on healthcare costs, targeting the most expensive conditions with the largest gaps. This granularity can also help employers identify where to invest in more personalized care options (for example, a diabetic member receives a call within minutes that the member’s blood glucose levels are low, thus avoiding a trip to the emergency room), ultimately aiding both the company and its employees. Furthermore, by getting to know their populations, companies can improve their ability to estimate spending over the coming years and track the success of their programs relative to the market.

Make Precision Benchmarking Part of Your Healthcare Strategy Arsenal

Although healthcare costs continue to rise, HR managers now have a way to consistently measure and articulate the value of their programs while navigating uncertainty. Precision benchmarking will arm employers with accurate, objective data analysis and insights to help elevate their healthcare strategy.  



[1] “2021 Global Medical Trend Rates Report,” Aon