Measurement Matters: How the Healthcare Industry Can More Effectively Monetize the Value in Value-Based Care
This four-part series will thoughtfully and thoroughly analyze the current state of outcomes measurement within value-based care. Along the way, we’ll bring in various perspectives from stakeholders across the industry. In this post, we hear directly from Aaron Wells, PhD and Vice President for Outcomes and Reporting at PopHealthCare. You can read his white paper on measurement methodology published in Medical Economics.
The energy around value-based care has increased rapidly in recent years, but many questions remain when attempting to effectively execute a value-based care initiative. Among the most critical unknowns concerns the means by which to measure the value created by a population health solution.
The specific challenge we face is attempting to measure the value of health care utilization that didn’t happen because we were able to prevent it.
To do this, we must measure what actually happened and compare to the expected state. But how do we arrive at these expectations? This challenge puts pressure on every stakeholder group, including finance, product, clinical leaders within health plans, provider groups in risk-bearing agreements, and solutions providers such as PopHealthCare, held accountable for creating financial and clinical value through targeted interventions.
The industry has yet to make a clear determination on which methodology offers the most accurate and actionable results for measuring value. As an added layer of complexity, a lack of standards and best practices across value-based contracts creates even more ambiguity. This has left us with a fractured and inconsistent approach to measurement that must be addressed if we expect to move forward with success in value-based care delivery.
Two Common Choices and No Clear Path
Historically, our measurement choices have come down to two categories: quasi-experimental and actuarial methods. Quasi-experimental techniques rely on the comparison of two matched groups, those treated and those untreated. Each group is tracked over a minimum of two years. Actuarial methods typically rely on longitudinal data and a combination of factors such as income, inflation, technology, and morbidity to predict the expected trend for the population if no intervention is delivered.
Unfortunately, each category falls short when applied to value-based care.
The Emerging Gold Standard for Measuring Value
At PopHealthCare, we’ve spent the last several years collaborating with leading payer and provider organizations across the country to test the various techniques available for measuring value. Our efforts have landed us squarely on the Coarsened Exact Matching (CEM) Methodology, a quasi-experimental approach first introduced in the political science discipline and later translated for healthcare purposes. CEM addresses many of the deficiencies present in long-standing methods within its category. In our experience, the CEM methodology results in increased:
- Flexibility – The methodology intrinsically adjusts to external changes in the marketplace that affect outcomes, namely, the COVID-19 pandemic.
- Accuracy – CEM addresses specific challenges, such as selection bias, that can create inaccuracies in reporting. CEM is the most accurate method for population health program evaluation, as proven by peer-reviewed research.
- Transparency – CEM enables specific program attribution, providing direct insight into provider practices and care programs actually creating value, while reducing double counting of value across groups. The transparent estimation framework of CEM also enables providers to more quickly address issues within health intervention programs.
- Simplicity – Use of CEM requires only two key decisions by the parties involved in a value-based contract, and can be simply explained at all levels within an organization.
Interested in learning more about the merits of the CEM methodology and why it should be considered the gold standard for measuring value? Read our latest whitepaper, recently published in the Journal of Medical Economics. The whitepaper explains leading attributes of CEM and why it outperforms alternative actuarial and quasi-experimental methods.
Then, follow this series as we introduce additional stakeholder voices committed to answering the hard questions of how to define the value in value-based care.