This chapter summarizes our approach and findings from conducting quantitative analyses of episodes of care data generated from two commercially available episode grouper tools (i.e., Symmetry ETGs and Thomson MEGs). These analyses were intended to explore issues related to the construction and use of episodes of care for the purposes of performance measurement and aligning incentives to deliver high quality care. - We examined a set of episodes that were constructed using existing commercial episode grouper tools as a matter of convenience in an effort to explore a range of issues.30
The episode groupers utilize the primary diagnosis on claim line items to create and place the line items into episode. Thus, a condition consistently coded as a secondary diagnosis will not have its own episode. Only certain types of claims, as determined by procedure and revenue codes, can start an episode such as evaluation and management procedure codes, surgery procedure codes or specific inpatient facility revenue code. Ancillary claims, such as pharmacy and laboratory, and other services can be grouped into an existing episode, but do not start an episode. Each episode that does not represent a chronic condition has a "clean period" during which no claims for that condition can appear before a new episode of the same type can start. This clean period varies by specific episode. -
The goal of this project was not to critique the validity or applicability of existing grouper software tools, or to explicitly compare the tools, but rather to conduct a variety of exploratory analyses to illustrate the types of issues that would need to be considered if performance measurement or financial incentives were to be aligned around an episode of care, regardless of what tool (either de novo or existing) would be used to define an episode. Most of these issues we addressed would benefit from additional analyses to better understand the questions raised by these exploratory analyses.
In considering the findings contained in this report, readers should be aware that the results partly reflect the design features of the two commercially available grouper software tools that were used to construct episodes in this project. As such, other types of episode constructions could yield different results. Additionally, the variation in results observed across states may be an artifact of variations in coding practices in different regions and future work should attempt to understand the extent of variation in coding practices.
Our analyses focused on informing a number of overarching questions:
How much variation is there in the number of episodes, standardized payments for episodes, and the types and combinations of settings in which care was delivered?
To what extent does patient complexity, as assessed by the total number of episodes assigned to a beneficiary, influence what we observe?
What is the impact of various attribution rules, in terms of the percent of episodes that could be attributed to providers under each rule?
How much variation exists across the three states in the number and cost of episodes, the settings in which care was delivered, as well as how well various types of attribution rules worked?