BACKGROUND: Performance measurement at the provider group level is increasingly advocated, but different methods for selecting patients when calculating provider group performance have received little evaluation. OBJECTIVE: We compared 2 currently used methods according to characteristics of the patients selected and impact on performance estimates. RESEARCH DESIGN, SUBJECTS, AND MEASURES: We analyzed Medicare claims data for fee-for-service beneficiaries with diabetes ever seen at an academic multispeciality physician group in 2003 to 2004. We examined sample size, sociodemographics, clinical characteristics, and receipt of recommended diabetes monitoring in 2004 for the groups of patients selected using 2 methods implemented in large-scale performance initiatives: the Plurality Provider Algorithm and the Diabetes Care Home method. We examined differences among discordantly assigned patients to determine evidence for differential selection regarding these measures. RESULTS: Fewer patients were selected under the Diabetes Care Home method (n=3558) than the Plurality Provider Algorithm (n=4859). Compared with the Plurality Provider Algorithm, the Diabetes Care Home method preferentially selected patients who were female, not entitled because of disability, older, more likely to have hypertension, and less likely to have kidney disease and peripheral vascular disease, and had lower levels of predicted utilization. Diabetes performance was higher under Diabetes Care Home method, with 67% versus 58% receiving >1 A1c tests, 70% versus 65% receiving ≥1 low-density lipoprotein (LDL) test, and 38% versus 37% receiving an eye examination. CONCLUSIONS: The method used to select patients when calculating provider group performance may affect patient case mix and estimated performance levels, and warrants careful consideration when comparing performance estimates.
BACKGROUND: Performance measurement at the provider group level is increasingly advocated, but different methods for selecting patients when calculating provider group performance have received little evaluation. OBJECTIVE: We compared 2 currently used methods according to characteristics of the patients selected and impact on performance estimates. RESEARCH DESIGN, SUBJECTS, AND MEASURES: We analyzed Medicare claims data for fee-for-service beneficiaries with diabetes ever seen at an academic multispeciality physician group in 2003 to 2004. We examined sample size, sociodemographics, clinical characteristics, and receipt of recommended diabetes monitoring in 2004 for the groups of patients selected using 2 methods implemented in large-scale performance initiatives: the Plurality Provider Algorithm and the Diabetes Care Home method. We examined differences among discordantly assigned patients to determine evidence for differential selection regarding these measures. RESULTS: Fewer patients were selected under the Diabetes Care Home method (n=3558) than the Plurality Provider Algorithm (n=4859). Compared with the Plurality Provider Algorithm, the Diabetes Care Home method preferentially selected patients who were female, not entitled because of disability, older, more likely to have hypertension, and less likely to have kidney disease and peripheral vascular disease, and had lower levels of predicted utilization. Diabetes performance was higher under Diabetes Care Home method, with 67% versus 58% receiving >1 A1c tests, 70% versus 65% receiving ≥1 low-density lipoprotein (LDL) test, and 38% versus 37% receiving an eye examination. CONCLUSIONS: The method used to select patients when calculating provider group performance may affect patient case mix and estimated performance levels, and warrants careful consideration when comparing performance estimates.
Authors: Thomas S Rector; Steven L Wickstrom; Mona Shah; N Thomas Greeenlee; Paula Rheault; Jeannette Rogowski; Vicki Freedman; John Adams; José J Escarce Journal: Health Serv Res Date: 2004-12 Impact factor: 3.402
Authors: Ashish K Jha; Jonathan B Perlin; Michael A Steinman; John W Peabody; John Z Ayanian Journal: J Gen Intern Med Date: 2005-08 Impact factor: 5.128
Authors: Robert N Foley; Anne M Murray; Shuling Li; Charles A Herzog; A Marshall McBean; Paul W Eggers; Allan J Collins Journal: J Am Soc Nephrol Date: 2004-12-08 Impact factor: 10.121
Authors: Clemens S Hong; Steven J Atlas; Yuchiao Chang; S V Subramanian; Jeffrey M Ashburner; Michael J Barry; Richard W Grant Journal: JAMA Date: 2010-09-08 Impact factor: 56.272
Authors: Gregory C Pope; John Kautter; Randall P Ellis; Arlene S Ash; John Z Ayanian; Lisa I Lezzoni; Melvin J Ingber; Jesse M Levy; John Robst Journal: Health Care Financ Rev Date: 2004
Authors: Aaron K Ho; Christie M Bartels; Carolyn T Thorpe; Nancy Pandhi; Maureen A Smith; Heather M Johnson Journal: Am J Hypertens Date: 2016-02-24 Impact factor: 2.689
Authors: Thomas R Radomski; Xinhua Zhao; Carolyn T Thorpe; Joshua M Thorpe; Chester B Good; Maria K Mor; Michael J Fine; Walid F Gellad Journal: J Gen Intern Med Date: 2016-02-22 Impact factor: 5.128
Authors: Heather M Johnson; Andrea G Olson; Jamie N LaMantia; Amy J H Kind; Nancy Pandhi; Eneida A Mendonça; Mark Craven; Maureen A Smith Journal: J Gen Intern Med Date: 2014-11-06 Impact factor: 5.128
Authors: Christie M Bartels; Heather Johnson; Katya Voelker; Carolyn Thorpe; Patrick McBride; Elizabeth A Jacobs; Nancy Pandhi; Maureen Smith Journal: Arthritis Care Res (Hoboken) Date: 2014-09 Impact factor: 4.794
Authors: Aaron K Ho; Carolyn T Thorpe; Nancy Pandhi; Mari Palta; Maureen A Smith; Heather M Johnson Journal: J Hypertens Date: 2015-11 Impact factor: 4.844
Authors: Carolyn T Thorpe; Nicole R Fowler; Katherine Harrigan; Xinhua Zhao; Yihuang Kang; Joseph T Hanlon; Walid F Gellad; Loren J Schleiden; Joshua M Thorpe Journal: J Am Geriatr Soc Date: 2016-08-22 Impact factor: 5.562