Risk adjustment is a powerful tool to help compare the performance of one provider to another. We can use risk adjustment to take out the complexity factors of a patient panel, so we can make a fair comparison of health care costs attributed to one provider vs. another.
This works by establishing risk adjustment factors for various health conditions, averaging them across a population, and calculating the expected difference in healthcare costs in comparison to the general population.
In this video, I walk through an example, based on real benchmarking data, for a diabetic population. In this circumstance the risk adjustment factor is 1.5, meaning this diabetic population is expected to cost about 1.5x the general population.
We can then risk adjust the actual costs attributed to a provider, and compare to the general population to see if they are under or over performing in comparison to the network average (or another provider).
The ability to compare costs starts to breakdown when you want to look at more granular components of cost such as inpatient, outpatient facility, outpatient professional, pharmacy, and ancillary.
The percent change in these costs varies from one category to another. So, if we adjust the spend in each category by the average 1.5x for diabetics, we may be making a significant miscalculation. For example, pharmacy costs are expected to be about 140% higher than the general population, but outpatient professional is only 7% higher.
The moral to the story is that risk adjustment is an incredibly powerful tool, but you should be aware of its limitations — learn more about Arcadia’s healthcare analytics platform.
If you enjoyed this video, you may also enjoy other episodes of Tiny Talk videos on YouTube.