Tag Archives: episodes of care

Inside Medicare’s episode payment models

The Centers for Medicare and Medicaid Services released final rules related to episode payment models on January 3, 2017, and May 19, 2017. This paper by Milliman consultants Pamela Pelizzari and Daniel Muldoon outlines the major provisions of the final rules and suggests possible implications for affected providers.

Analytic basics: Completeness and outlier episode flags

Bates-DougAnalysts working with episode of care groupers for the first time often have questions about how to use the various value-added flags assigned to episodes. Episode of care groupers link together all of the claims that pertain to the treatment of a particular condition for a particular patient, to create a powerful unit of analysis. For example, a patient with a condition such as diabetes may receive multiple types of services from multiple providers and provider types for the treatment of their diabetes. An episode of care grouper will combine all of the individual claims from the different providers so that the full cost of treatment can be assessed.

Two of the value-added flags commonly assigned to episodes include completeness flags and outlier flags. Both of these flags enable analysts to filter out, or include, types of episodes to optimize their reporting. How to apply filters using these flags depends on the analysis being performed. A brief summary of these flags, and their use, is described below.

Completeness flags
Many episodes in a data set will not be complete, meaning there are still outstanding claims related to those episodes that were not available when the data were grouped. Typically, episodes that start toward the end of your grouping period are more likely to be incomplete. For instance, if you are grouping data incurred from January 2011 through December 2013 and an episode begins on December 19, 2013, there is a lower probability that all claims for this episode will be available in the data than if the episode had started in January 2013.

Episode groupers use different logic when assessing completeness for acute and chronic conditions. For acute conditions, most groupers determine that an episode is complete if there are no incurred professional claims for that condition for a predefined number of days. Chronic conditions such as diabetes are never cured, so technically those episodes never end, but in order to support analyses, chronic conditions are often divided into annual periods and may be defined as complete when a full year of data is available for the members with those episodes.

When comparing costs to benchmarks, incomplete episodes should be excluded because incomplete episodes are excluded from cost benchmarks.

If you are comparing the average length (days) or average cost of episodes across various populations or provider groups, then you should also exclude incomplete episodes. It is impossible to accurately assess average costs per episode if every claim for every episode is not included.

If the purpose of your analysis is to evaluate the prevalence of episode conditions, then include all episodes (complete and incomplete) in your reports.

Table 1 displays the distribution of complete and incomplete diabetes episodes from a sample data set. The average cost for incomplete episodes is usually lower than the average cost for complete episodes.

Table 1

Outlier flags
Episodes that have atypically higher costs or atypically lower costs compared with other episodes within the same class are flagged as high or low outliers. There are multiple methodologies for defining outlier episodes, but commonly the flags are based on statistical variance (i.e., a number of standard deviations from the mean). In and of themselves, outlier flags are not a measure of efficiency or quality, but the magnitude of the variance in their cost indicates there is something atypical about these cases.

When comparing with benchmarks, outliers should be excluded because most benchmarks will exclude outliers for consistency.

When comparing average costs across populations or provider groups, many analysts may choose to exclude all outliers, because a few outliers for a given group may skew their results. That being said, it is also important to assess if any given population of patients has significantly more episodes flagged as high outliers compared with others. A higher percentage of high outliers might warrant the need for further investigation.

For many episode classes, all that is needed to start an episode is a professional encounter with a primary diagnosis relevant to that episode class. In some cases, very short episodes may represent visits to rule out a specific diagnosis or other situations that don’t really represent full treatment for a condition. Excluding low outliers can help remove those types of episodes from your analysis.

Table 2 displays a sample of diabetes episodes by outlier status.

Table 2

Episode completeness and outlier flags can, of course, be used together. For most comparative analyses (to benchmarks or across populations), only complete non-outlier episodes are included. Table 3 displays the distribution of diabetes episodes when both flags are used as report dimensions.

Note that an analysis based solely on complete non-outlier episodes from these sample data would reduce the number of episodes from 57,193 to 34,250, removing 40% of the episodes from the analysis. When analyzing episode classes with a limited number of episodes, applying these filters may reduce your sample size to volumes that are too small to produce statistically significant results, so it is important to assess how many episodes are in your sample before you begin.

Table 3

Episodes of care provide a useful unit of analysis for evaluating healthcare utilization and cost. The episode completeness and outlier flags allow users to include, or exclude, types of episodes to further refine their analysis.

This article first appeared at Milliman MedInsight.