Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models

Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of intere...
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Autor*in:	Hwang, Jessica [verfasserIn] Adams, John L. [verfasserIn] Paddock, Susan M. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Reliability Physician profiling Health care quality Hierarchical logistic regression

Übergeordnetes Werk:	Enthalten in: Health services and outcomes research methodology - [S.l.] : Proquest, 2000, 21(2020), 1 vom: 09. Nov., Seite 111-130
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:1 ; day:09 ; month:11 ; pages:111-130

Links:	Volltext

DOI / URN:	10.1007/s10742-020-00226-4

Katalog-ID:	SPR043327605

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520			\|a Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators, particularly when the number of patients per provider is low, as expected for physician quality measures. Using claims data on Medicare fee-for-service beneficiaries treated by physicians in Florida, we illustrate the practical implications by examining two process-of-care measures collected by the Centers for Medicare and Medicaid Services in the U.S. for physician quality reporting.
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allfields	10.1007/s10742-020-00226-4 doi (DE-627)SPR043327605 (DE-599)SPRs10742-020-00226-4-e (SPR)s10742-020-00226-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Hwang, Jessica verfasserin aut Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators, particularly when the number of patients per provider is low, as expected for physician quality measures. Using claims data on Medicare fee-for-service beneficiaries treated by physicians in Florida, we illustrate the practical implications by examining two process-of-care measures collected by the Centers for Medicare and Medicaid Services in the U.S. for physician quality reporting. Reliability (dpeaa)DE-He213 Physician profiling (dpeaa)DE-He213 Health care quality (dpeaa)DE-He213 Hierarchical logistic regression (dpeaa)DE-He213 Adams, John L. verfasserin aut Paddock, Susan M. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 21(2020), 1 vom: 09. Nov., Seite 111-130 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:21 year:2020 number:1 day:09 month:11 pages:111-130 https://dx.doi.org/10.1007/s10742-020-00226-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 21 2020 1 09 11 111-130
spelling	10.1007/s10742-020-00226-4 doi (DE-627)SPR043327605 (DE-599)SPRs10742-020-00226-4-e (SPR)s10742-020-00226-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Hwang, Jessica verfasserin aut Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators, particularly when the number of patients per provider is low, as expected for physician quality measures. Using claims data on Medicare fee-for-service beneficiaries treated by physicians in Florida, we illustrate the practical implications by examining two process-of-care measures collected by the Centers for Medicare and Medicaid Services in the U.S. for physician quality reporting. Reliability (dpeaa)DE-He213 Physician profiling (dpeaa)DE-He213 Health care quality (dpeaa)DE-He213 Hierarchical logistic regression (dpeaa)DE-He213 Adams, John L. verfasserin aut Paddock, Susan M. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 21(2020), 1 vom: 09. Nov., Seite 111-130 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:21 year:2020 number:1 day:09 month:11 pages:111-130 https://dx.doi.org/10.1007/s10742-020-00226-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 21 2020 1 09 11 111-130
allfields_unstemmed	10.1007/s10742-020-00226-4 doi (DE-627)SPR043327605 (DE-599)SPRs10742-020-00226-4-e (SPR)s10742-020-00226-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Hwang, Jessica verfasserin aut Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators, particularly when the number of patients per provider is low, as expected for physician quality measures. Using claims data on Medicare fee-for-service beneficiaries treated by physicians in Florida, we illustrate the practical implications by examining two process-of-care measures collected by the Centers for Medicare and Medicaid Services in the U.S. for physician quality reporting. Reliability (dpeaa)DE-He213 Physician profiling (dpeaa)DE-He213 Health care quality (dpeaa)DE-He213 Hierarchical logistic regression (dpeaa)DE-He213 Adams, John L. verfasserin aut Paddock, Susan M. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 21(2020), 1 vom: 09. Nov., Seite 111-130 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:21 year:2020 number:1 day:09 month:11 pages:111-130 https://dx.doi.org/10.1007/s10742-020-00226-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 21 2020 1 09 11 111-130
allfieldsGer	10.1007/s10742-020-00226-4 doi (DE-627)SPR043327605 (DE-599)SPRs10742-020-00226-4-e (SPR)s10742-020-00226-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Hwang, Jessica verfasserin aut Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators, particularly when the number of patients per provider is low, as expected for physician quality measures. Using claims data on Medicare fee-for-service beneficiaries treated by physicians in Florida, we illustrate the practical implications by examining two process-of-care measures collected by the Centers for Medicare and Medicaid Services in the U.S. for physician quality reporting. Reliability (dpeaa)DE-He213 Physician profiling (dpeaa)DE-He213 Health care quality (dpeaa)DE-He213 Hierarchical logistic regression (dpeaa)DE-He213 Adams, John L. verfasserin aut Paddock, Susan M. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 21(2020), 1 vom: 09. Nov., Seite 111-130 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:21 year:2020 number:1 day:09 month:11 pages:111-130 https://dx.doi.org/10.1007/s10742-020-00226-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 21 2020 1 09 11 111-130
allfieldsSound	10.1007/s10742-020-00226-4 doi (DE-627)SPR043327605 (DE-599)SPRs10742-020-00226-4-e (SPR)s10742-020-00226-4-e DE-627 ger DE-627 rakwb eng 610 ASE 44.10 bkl Hwang, Jessica verfasserin aut Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators, particularly when the number of patients per provider is low, as expected for physician quality measures. Using claims data on Medicare fee-for-service beneficiaries treated by physicians in Florida, we illustrate the practical implications by examining two process-of-care measures collected by the Centers for Medicare and Medicaid Services in the U.S. for physician quality reporting. Reliability (dpeaa)DE-He213 Physician profiling (dpeaa)DE-He213 Health care quality (dpeaa)DE-He213 Hierarchical logistic regression (dpeaa)DE-He213 Adams, John L. verfasserin aut Paddock, Susan M. verfasserin aut Enthalten in Health services and outcomes research methodology [S.l.] : Proquest, 2000 21(2020), 1 vom: 09. Nov., Seite 111-130 (DE-627)320443477 (DE-600)2005197-9 1572-9400 nnns volume:21 year:2020 number:1 day:09 month:11 pages:111-130 https://dx.doi.org/10.1007/s10742-020-00226-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.10 ASE AR 21 2020 1 09 11 111-130
language	English
source	Enthalten in Health services and outcomes research methodology 21(2020), 1 vom: 09. Nov., Seite 111-130 volume:21 year:2020 number:1 day:09 month:11 pages:111-130
sourceStr	Enthalten in Health services and outcomes research methodology 21(2020), 1 vom: 09. Nov., Seite 111-130 volume:21 year:2020 number:1 day:09 month:11 pages:111-130
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Reliability Physician profiling Health care quality Hierarchical logistic regression
dewey-raw	610
isfreeaccess_bool	false
container_title	Health services and outcomes research methodology
authorswithroles_txt_mv	Hwang, Jessica @@aut@@ Adams, John L. @@aut@@ Paddock, Susan M. @@aut@@
publishDateDaySort_date	2020-11-09T00:00:00Z
hierarchy_top_id	320443477
dewey-sort	3610
id	SPR043327605
language_de	englisch
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author	Hwang, Jessica
spellingShingle	Hwang, Jessica ddc 610 bkl 44.10 misc Reliability misc Physician profiling misc Health care quality misc Hierarchical logistic regression Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models
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topic_title	610 ASE 44.10 bkl Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models Reliability (dpeaa)DE-He213 Physician profiling (dpeaa)DE-He213 Health care quality (dpeaa)DE-He213 Hierarchical logistic regression (dpeaa)DE-He213
topic	ddc 610 bkl 44.10 misc Reliability misc Physician profiling misc Health care quality misc Hierarchical logistic regression
topic_unstemmed	ddc 610 bkl 44.10 misc Reliability misc Physician profiling misc Health care quality misc Hierarchical logistic regression
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title	Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models
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title_full	Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models
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author_browse	Hwang, Jessica Adams, John L. Paddock, Susan M.
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title_sort	defining and estimating the reliability of physician quality measures in hierarchical logistic regression models
title_auth	Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models
abstract	Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators, particularly when the number of patients per provider is low, as expected for physician quality measures. Using claims data on Medicare fee-for-service beneficiaries treated by physicians in Florida, we illustrate the practical implications by examining two process-of-care measures collected by the Centers for Medicare and Medicaid Services in the U.S. for physician quality reporting.
abstractGer	Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators, particularly when the number of patients per provider is low, as expected for physician quality measures. Using claims data on Medicare fee-for-service beneficiaries treated by physicians in Florida, we illustrate the practical implications by examining two process-of-care measures collected by the Centers for Medicare and Medicaid Services in the U.S. for physician quality reporting.
abstract_unstemmed	Abstract In healthcare provider profiling, the reliability of a performance measure indicates whether observed differences in patient outcomes can be attributed to genuine differences in quality across providers. While reliability is easy to define, estimate, and interpret when the outcome of interest is continuous and a hierarchical linear model can be assumed, several different definitions and estimators of reliability are in use for performance measures based on binary outcomes. We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators, particularly when the number of patients per provider is low, as expected for physician quality measures. Using claims data on Medicare fee-for-service beneficiaries treated by physicians in Florida, we illustrate the practical implications by examining two process-of-care measures collected by the Centers for Medicare and Medicaid Services in the U.S. for physician quality reporting.
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title_short	Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models
url	https://dx.doi.org/10.1007/s10742-020-00226-4
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author2	Adams, John L. Paddock, Susan M.
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doi_str	10.1007/s10742-020-00226-4
up_date	2024-07-03T17:57:39.798Z
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Defining and estimating the reliability of physician quality measures in hierarchical logistic regression models

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