From 3M Health Information Systems
A perspective on measuring value
Can health plan value be distilled down to a single score? Would it reveal anything meaningful and if so, how could that information be used? While working for Texas Medicaid, these were questions that a colleague and I pondered. It was shortly after the 2011 movie “Moneyball” was released (You remember, those good old days when a new movie was released in a theater?).
In the movie, the main character was the general manager of the Oakland Athletics major league baseball team. The team was coming off a season in which it had just lost key players to other teams, largely because it had the third smallest player budget in professional baseball. The general manager, with the help of a Harvard economics graduate, decided to pursue players using an unconventional approach. Leveraging analytics and using composite statistics on “hidden” player value, they were able to put together a team that achieved 103 wins and 58 losses. The team won the division title and had its highest win to loss ratio in a decade, in part by adopting an unconventional approach to identifying and measuring value.
Could a variation of this approach–namely creating a composite measure that is representative of various dimensions of health plan performance (e.g., quality, enrollee experience, managing costs, etc.)–be useful? There are already certain quality measures that are composites, like health plan report cards for enrollees. While composite measures have critics, I would posit that the concept could be leveraged and expanded by a Medicaid agency as a valuable tool for health plan oversight, incentive-based programs and re-procurement, following some basic guidelines.
- Limited measure set: Cost, quality (emphasis should be on outcomes) and enrollee experience are key measurements that agencies already collect and can be repurposed for this effort. This is not to suggest that one should disregard poor performance on measures outside the limited measure set. A floor could be set for these measures.
- Risk adjustment: Risk adjustment of measures enables fair comparisons of health plans based on the mix of enrollees.
- Transparency: Visibility into component measurements and calculations of composite score is critical. Visibility enables the health plan to understand which component measures need improvement and enables easy insight into how it contributes to overall performance. That understanding leads to better translation of criteria in value-based payment models with providers.
- Simplicity: An algorithm that is easy to understand and incorporates the elements above can enable the agency and health plans to clearly understand relative performance and how to improve that performance. Simplicity is not a bad thing if it doesn’t sacrifice validity.
Is utilizing a transparently calculated composite value score that much different than using a variety of standalone quality and cost measures? Depending on what the goal is, it can bring together measurements that are often siloed and used for discrete purposes and integrate them to form a broader view of value.
This type of approach could compel a Medicaid agency to make choices and prioritize the measures to include in the scoring algorithm. It could also compel Medicaid agencies to be transparent about how the building block measures contribute to the overall composite value score. Both seem to be good things and contribute to simplicity without losing validity. In the incredibly complex world of Medicaid and performance measurement, this seems like a good thing. Such an approach cannot provide insight into all facets of health plan performance, but depending on the measurements that get incorporated, it could reveal a lot. In a value-based care environment, there are many ways this approach could be used to drive optimal health plan performance.
Matt Ferrara is a program manager within 3M regulatory and government affairs.