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How are medical groups identified as high-performing? The effect of different approaches to classification of performance
BACKGROUND: Payers and policy makers across the international healthcare market are increasingly using publicly available summary measures to designate providers as “high-performing”, but no consistently-applied approach exists to identifying high performers. This paper uses publicly available data...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6639957/ https://www.ncbi.nlm.nih.gov/pubmed/31319830 http://dx.doi.org/10.1186/s12913-019-4293-9 |
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author | Ahluwalia, Sangeeta C. Damberg, Cheryl L. Haas, Ann Shekelle, Paul G. |
author_facet | Ahluwalia, Sangeeta C. Damberg, Cheryl L. Haas, Ann Shekelle, Paul G. |
author_sort | Ahluwalia, Sangeeta C. |
collection | PubMed |
description | BACKGROUND: Payers and policy makers across the international healthcare market are increasingly using publicly available summary measures to designate providers as “high-performing”, but no consistently-applied approach exists to identifying high performers. This paper uses publicly available data to examine how different classification approaches influence which providers are designated as “high-performers”. METHODS: We conducted a quantitative analysis of cross-sectional publicly-available performance data in the U.S. We used 2014 Minnesota Community Measurement data from 58 medical groups to classify performance across 4 domains: quality (two process measures of cancer screening and 2 composite measures of chronic disease management), total cost of care, access (a composite CAHPS measure), and patient experience (3 CAHPS measures). We classified medical groups based on performance using either relative thresholds or absolute values of performance on all included measures. RESULTS: Using relative thresholds, none of the 58 medical groups achieved performance in the top 25% or 35% in all 4 performance domains. A relative threshold of 40% was needed before one group was classified as high-performing in all 4 domains. Using absolute threshold values, two medical groups were classified as high-performing across all 4 domains. In both approaches, designating “high performance” using fewer domains led to more groups designated as high-performers, though there was little to moderate concordance across identified “high-performing” groups. CONCLUSIONS: Classification of medical groups as high performing is sensitive to the domains of performance included, the classification approach, and choice of threshold. With increasing focus on achieving high performance in healthcare delivery, the absence of a consistently-applied approach to identify high performers impedes efforts to reliably compare, select and reward high-performing providers. |
format | Online Article Text |
id | pubmed-6639957 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66399572019-07-29 How are medical groups identified as high-performing? The effect of different approaches to classification of performance Ahluwalia, Sangeeta C. Damberg, Cheryl L. Haas, Ann Shekelle, Paul G. BMC Health Serv Res Research Article BACKGROUND: Payers and policy makers across the international healthcare market are increasingly using publicly available summary measures to designate providers as “high-performing”, but no consistently-applied approach exists to identifying high performers. This paper uses publicly available data to examine how different classification approaches influence which providers are designated as “high-performers”. METHODS: We conducted a quantitative analysis of cross-sectional publicly-available performance data in the U.S. We used 2014 Minnesota Community Measurement data from 58 medical groups to classify performance across 4 domains: quality (two process measures of cancer screening and 2 composite measures of chronic disease management), total cost of care, access (a composite CAHPS measure), and patient experience (3 CAHPS measures). We classified medical groups based on performance using either relative thresholds or absolute values of performance on all included measures. RESULTS: Using relative thresholds, none of the 58 medical groups achieved performance in the top 25% or 35% in all 4 performance domains. A relative threshold of 40% was needed before one group was classified as high-performing in all 4 domains. Using absolute threshold values, two medical groups were classified as high-performing across all 4 domains. In both approaches, designating “high performance” using fewer domains led to more groups designated as high-performers, though there was little to moderate concordance across identified “high-performing” groups. CONCLUSIONS: Classification of medical groups as high performing is sensitive to the domains of performance included, the classification approach, and choice of threshold. With increasing focus on achieving high performance in healthcare delivery, the absence of a consistently-applied approach to identify high performers impedes efforts to reliably compare, select and reward high-performing providers. BioMed Central 2019-07-18 /pmc/articles/PMC6639957/ /pubmed/31319830 http://dx.doi.org/10.1186/s12913-019-4293-9 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Ahluwalia, Sangeeta C. Damberg, Cheryl L. Haas, Ann Shekelle, Paul G. How are medical groups identified as high-performing? The effect of different approaches to classification of performance |
title | How are medical groups identified as high-performing? The effect of different approaches to classification of performance |
title_full | How are medical groups identified as high-performing? The effect of different approaches to classification of performance |
title_fullStr | How are medical groups identified as high-performing? The effect of different approaches to classification of performance |
title_full_unstemmed | How are medical groups identified as high-performing? The effect of different approaches to classification of performance |
title_short | How are medical groups identified as high-performing? The effect of different approaches to classification of performance |
title_sort | how are medical groups identified as high-performing? the effect of different approaches to classification of performance |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6639957/ https://www.ncbi.nlm.nih.gov/pubmed/31319830 http://dx.doi.org/10.1186/s12913-019-4293-9 |
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