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      Incorporating statistical uncertainty in the use of physician cost profiles

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          Abstract

          Background

          Physician 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.

          Methods

          We 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.

          Results

          Across 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).

          Conclusions

          Health 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 references9

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          Economic profiling of physician specialists: use of outlier treatment and episode attribution rules.

          This paper examines the influence of episode attribution methodology and cost outlier methodology on the accuracy of physicians' economic profiles. Four years of claims data from a mixed model HMO were processed using the leading episode grouper software. Episode grouped results then were applied to construct input distributions for a simulation model. For each of four specialties (cardiology, family practice, general surgery, and neurology), we employed sets of 18 simulations to investigate the effects of three alternative episode attribution methodologies and six alternative cost outlier methodologies on sensitivity, specificity, and positive predictive error in classifying cost-efficient and cost-inefficient physicians. For identification of cost-efficient physicians, the most accurate profiling results were obtained when Winsorizing outliers at 2% and 98% of episode-type cost distributions, and attributing responsibility for episode costs to physicians who accounted for at least 30% of associated professional and prescribing fees. No consistent combination of outlier methodology and episode attribution rule was found to be superior for identifying cost-inefficient physicians.
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            Does affiliation of physician groups with one another produce higher quality primary care?

            Recent reports have emphasized the importance of delivery systems in improving health care quality. However, few prior studies have assessed differences in primary care quality between physician groups that differ in size and organizational configuration. We examined whether larger physician group size and affiliation with networks of multiple groups are associated with higher quality of care. We conducted a cross-sectional observational analysis of 132 physician groups (including 4,358 physicians) who delivered primary care services in Massachusetts in 2002. We compared physician groups on performance scores for 12 Health Plan Employer Data and Information Set (HEDIS) measures reflecting processes of adult primary care. Network-affiliated physician groups had higher performance scores than non-affiliated groups for 10 of the 12 HEDIS measures (p < 0.05). There was no consistent relationship between group size and performance scores. Multivariable models including group size, network affiliation, and health plan showed that network-affiliated groups had higher performance scores than non-affiliated groups on 8 of the 12 HEDIS measures (p < 0.05), and larger group size was not associated with higher performance scores. Adjusted differences in the performance scores of network-affiliated and non-affiliated groups ranged from 2% to 15%. For 4 HEDIS measures related to diabetes care, performance score differences between network-affiliated and non-affiliated groups were most apparent among the smallest groups. Physician group affiliation with networks of multiple groups was associated with higher quality, and for measures of diabetes care the quality advantage of network-affiliation was most evident among smaller physician groups.
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              Economic profiling of primary care physicians: consistency among risk-adjusted measures.

              To investigate whether different risk-adjustment methodologies and economic profiling or "practice efficiency" metrics produce differences in practice efficiency rankings for a set of primary care physicians (PCPs). Twelve months of claims records (inpatient, outpatient, professional, and pharmacy) for an independent practice association HMO. Patient risk scores obtained with six profiling risk-adjustment methodologies were used in conjunction with claims cost tabulations to measure practice efficiency of all primary care physicians who managed 25 or more members of an HMO. For each of the risk-adjustment methodologies, two measures of "efficiency" were constructed: the standardized cost difference between total observed (standardized actual) and total expected costs for patients managed by each PCP, and the ratio of the PCP's total observed to total expected costs (O/E ratio). Primary care physicians were ranked from most to least efficient according to each risk-adjusted measure, and level of agreement among measures was tested using weighted kappa. Separate rankings were constructed for pediatricians and for other primary care physicians. Moderate to high levels of agreement were observed among the six risk-adjusted measures of practice efficiency. Agreement was greater among pediatrician rankings than among adult primary care physician rankings, and, with the standardized difference measure, greater for identifying the least efficient than the most efficient physicians. The O/E ratio was shown to be a biased measure of physician practice efficiency, disproportionately targeting smaller sized panels as outliers. Although we observed moderate consistency among different risk-adjusted PCP rankings, consistency of measures does not prove that practice efficiency rankings are valid, and health plans should be careful in how they use practice efficiency information. Indicators of practice efficiency should be based on the standardized cost difference, which controls for number of patients in a panel, instead of O/E ratio, which does not.
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                Author and article information

                Journal
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central
                1472-6963
                2010
                5 March 2010
                : 10
                : 57
                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
                Article
                1472-6963-10-57
                10.1186/1472-6963-10-57
                2842268
                20205736
                5ad80ffc-6b03-402d-8f4c-0ddb9051b3dc
                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.

                History
                : 26 May 2009
                : 5 March 2010
                Categories
                Research article

                Health & Social care
                Health & Social care

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