Comparing Commercial Systems for Characterizing Episodes of Care (PDF)

Payers are increasingly using episodes of care to measure and reward efficiency in health care. Much attention has been paid to the effects of different rules for assigning episodes to providers, but little to how individual costs are assigned to episodes. In this paper, we studied the extent to which the two most widely-used commercially available episode groupers differ in the episodes they construct. In particular, we compare how much of, and how, the two groupers allocate claims/spending to episodes of care and, for grouped claims, compare the 25 clinical episodes accounting for the greatest share of total spending. Using multi-payer data from the MarketScan Commercial Database, we applied the two most widely used commercial episode groupers: Episode Treatment Groups (ETGs) by Symmetry and Medical Episode Groups (MEGs) by Thomson-Reuters (Medstat). 

The groupers varied in their ability to allocate spending to episodes: MEG allocated 82% and ETG allocated 86% of spending to episodes. Of episodes ending in 2006, MEG classified 69% (corresponding to 53% of costs) as acute, 21% of episodes (43% of costs) as chronic, and the remainder as preventive or administrative. In contrast, ETG classified 54% of episodes (corresponding to 39% of costs) as acute, 36% of episodes (59% of costs) as chronic, and the remainder as preventive or administrative. Five percent of MEGS and 6% of ETGs accounted for half of each grouper’s allocated spending. The 25 most expensive episodes accounted for 49% and 46% of total spending on ETGs and MEGs, respectively. 

Comparative application of the two most widely used episode groupers to the same commercial claims data shows important differences between the two approaches. As the use of episode-based payment expands, payers should be aware that differences between groupers may affect the allocation of spending to episodes, as well as episode classification, thereby impacting the services and providers that are assigned to an episode.

Allison B. Rosen , Eli Liebman , Ana M. Aizcorbe , and David M. Cutler