By Tori Yokoyama, FSA, MAAA
Principal, Actuarial Science and Predictive Modeling at Arbital Health
| Population health benchmarks often serve as an anchor point against how healthcare provider performance is measured. Whether they guide contract settlement calculations to help determine trend or directly set target prices, or to simply help explain the drivers of performance and identify areas of opportunity for improved performance, benchmarks bring clarity to complex data. |  |
This article will focus on the latter—how benchmarks can uncover performance insights and highlight opportunities for improvement (outside the scope of direct use in contract settlements, though many of the same principles will apply to both). In a risk-based contract, understanding appropriate benchmark data for a managed patient population ensures healthcare providers can proactively target interventions and optimize value-based care partnerships. But doing this accurately isn’t easy. Acquiring the right datasets and navigating modeling limitations can lead to wrong conclusions around performance results, and patient interventions can even be mistargeted!
It’s critical to understand the complexities of benchmarking so you can make confident, data-driven decisions. When developing benchmarks, there are three key factors that should be taken into consideration: compatibility, flexibility, and actionability.
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Target Population CompatibilityHaving metrics that reflect underlying characteristics in the managed population is the ultimate goal of benchmarking. Important considerations include:
Although it's not feasible to adjust for every factor to guarantee a completely equitable comparison, it's crucial to ensure that the most significant elements are considered to the extent possible. Often, generic benchmarks are used based on straight averages across the nation or balanced using a simple redistribution by age and gender. For value-based care contracts where providers are taking risk on especially sick and vulnerable populations, these simple benchmarks are inadequate. In this particular example, an age-gender adjusted benchmark would likely underestimate benchmarks, and to different degrees by cohort, possibly resulting in inappropriate care for some patients. |
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FlexibilityMany benchmarks are created with a focus on a specific metric, such as overall, medical, and pharmacy costs per member per month (PMPM). These benchmarks typically provide a high-level number which is often a deterministic data point. While single data points provide value, they fall short in being able to meaningfully communicate the significance of differences or look deeper into underlying causes of discrepancies. With more flexibility, benchmarks can be presented as ranges of possible results and allow for deeper statistical analysis, providing more confidence in how meaningful differences are instead of being misled by random chance or noise. This is especially true with smaller patient populations, where a single large claim can throw off comparisons. |
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ActionabilityInsights alone have no value unless they are accompanied by resulting action. Comparisons to benchmarks and the revealing of new information need to drive action to generate value. Relevant actions include, but aren’t limited to:
Actionable insights should be relevant to clinical expertise and provider interventions, which can require custom views and a deep understanding of business operations and objectives. They may also require deeper analytical dives to look at specific patient cases and determine the most appropriate course of action. |
How Arbital Helps
Arbital Health has developed our Insights Module with statistical and actuarial rigor at all phases of the process. Beginning with a foundational data ingestion and quality assessment layer, data is cleaned and reviewed across four dimensions: completeness, accuracy, reasonability, and sufficiency. This data is then enriched to compare against benchmark data that undergoes the same enrichment process, adjusting for key population characteristics. This benchmarking process helps identify important gaps and provides detailed utilization benchmarks.
Finally, this population and benchmark data are summarized into actionable insights, with additional GenAI-enabled flexibility rapidly being developed—allowing users to generate customizable insights that meet their specific business needs. This process is driven by an industry-leading, robust automated technology.
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The Arbital Health Platform is a neutral network for collaborative risk contracting, allowing all stakeholders to centralize, measure, and adjudicate value-based care contracts. We help connect healthcare organizations and establish shared data, analytics, modeling, and measurement standards across the industry. Each step is guided and reviewed by extensive actuarial expertise. To learn more about how Arbital Health can help you successfully navigate the complex world of value-based contracting, request more information via the form below or schedule a meeting with our expert team. |
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About Arbital Health
At Arbital Health, we’ll handle the complexities of your value-based care contracts, so you don’t have to. Our goal is to accelerate the shift to value-based care by building the neutral platform that allows all stakeholders to centralize, measure, and adjudicate value-based care contracts. We aspire to be the trusted umpire adjudicating every outcomes-based contract in healthcare, whether contracts are between life sciences companies and payers, payers and providers, or employers and digital health companies. We make it simple to adjudicate contracts and unlock the trillion-dollar shift to value-based care.
