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Two Statistical Methods for Clustering Medicare Claims Data into Episodes of Care

  • Author(s): Gibbons, Robert Lyon
  • Advisor(s): Nolan, Deborah
  • et al.
Abstract

Methods for clustering health care claims into episodes of care are important tools for data analysis in evaluating health care outcomes and methods of payment. In this research, we implement two statistical methods (1) using an ensemble classifier, Random Forests, to estimate the strength of relationship in pairs of Medicare claims, and (2) using a sequence model and an Expectation Maximization (EM) algorithm.

Previous researchers into episode of care clustering have implemented other methods, some based on decision rules and others based on statistical methods. Other research in natural language processing, particularly conversation disentanglement, has developed methods that were inspirational for the methods that we implemented in this research.

We acquired two sets of Medicare claims data, one containing claims from 2006 and 2007 for 1.9 million patients (a 5 percent sample), and the other containing claims from 2007 and 2008 for 250 thousand patients. We tested our two statistical methods on claims for 50 randomly selected patients who were age 65 and older, using independent annotations by three licensed nurse practitioners. We found that the method using Random Forests outperformed the sequence model and achieved accuracy comparable to the nurse practitioner annotators. Future research into episode of care clustering might incorporate more extensive data on clinical relationships and create a more flexible representation of episode clusters, such as hierarchies and phases of care.

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