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Incorporating statistical uncertainty in the use of physician cost profiles

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      Abstract

      BackgroundPhysician cost profiles (also called efficiency or economic profiles) compare the costs of care provided by a physician to his or her peers. These profiles are increasingly being used as the basis for policy applications such as tiered physician networks. Tiers (low, average, high cost) are currently defined by health plans based on percentile cut-offs which do not account for statistical uncertainty. In this paper we compare the percentile cut-off method to another method, using statistical testing, for identifying high-cost or low-cost physicians.MethodsWe created a claims dataset of 2004-2005 data from four Massachusetts health plans. We employed commercial software to create episodes of care and assigned responsibility for each episode to the physician with the highest proportion of professional costs. A physicians' cost profile was the ratio of the sum of observed costs divided by the sum of expected costs across all assigned episodes. We discuss a new method of measuring standard errors of physician cost profiles which can be used in statistical testing. We then assigned each physician to one of three cost categories (low, average, or high cost) using two methods, percentile cut-offs and a t-test (p-value ≤ 0.05), and assessed the level of disagreement between the two methods.ResultsAcross the 8689 physicians in our sample, 29.5% of physicians were assigned a different cost category when comparing the percentile cut-off method and the t-test. This level of disagreement varied across specialties (17.4% gastroenterology to 45.8% vascular surgery).ConclusionsHealth plans and other payers should incorporate statistical uncertainty when they use physician cost-profiles to categorize physicians into low or high-cost tiers.

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      Most cited references 13

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      The measurement of observer agreement for categorical data.

       G Koch,  J R Landis (1977)
      This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.
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        The measurement of observer agreement for categorical data

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          The measurement of observer agreement for categorical data

           JR Landis,  GG Koch (1977)
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            Author and article information

            Affiliations
            [1 ]RAND, Santa Monica, CA, USA
            [2 ]Muskie School of Public Service, University of Southern Maine, Portland, ME, USA
            [3 ]Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
            [4 ]RAND, 4570 Fifth Avenue, Suite 600, Pittsburgh, PA 15213-2665, USA
            Contributors
            Journal
            BMC Health Serv Res
            BMC Health Services Research
            BioMed Central
            1472-6963
            2010
            5 March 2010
            : 10
            : 57
            2842268
            1472-6963-10-57
            20205736
            10.1186/1472-6963-10-57
            Copyright ©2010 Adams et al; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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            Research article

            Health & Social care

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