BACKGROUND: Current methods of risk adjustment rely on diagnoses recorded in clinical and administrative records. Differences among providers in diagnostic practices could lead to bias. METHODS: We used Medicare claims data from 1999 through 2006 to measure trends in diagnostic practices for Medicare beneficiaries. Regions were grouped into five quintiles according to the intensity of hospital and physician services that beneficiaries in the region received. We compared trends with respect to diagnoses, laboratory testing, imaging, and the assignment of Hierarchical Condition Categories (HCCs) among beneficiaries who moved to regions with a higher or lower intensity of practice. RESULTS: Beneficiaries within each quintile who moved during the study period to regions with a higher or lower intensity of practice had similar numbers of diagnoses and similar HCC risk scores (as derived from HCC coding algorithms) before their move. The number of diagnoses and the HCC measures increased as the cohort aged, but they increased to a greater extent among beneficiaries who moved to regions with a higher intensity of practice than among those who moved to regions with the same or lower intensity of practice. For example, among beneficiaries who lived initially in regions in the lowest quintile, there was a greater increase in the average number of diagnoses among those who moved to regions in a higher quintile than among those who moved to regions within the lowest quintile (increase of 100.8%; 95% confidence interval [CI], 89.6 to 112.1; vs. increase of 61.7%; 95% CI, 55.8 to 67.4). Moving to each higher quintile of intensity was associated with an additional 5.9% increase (95% CI, 5.2 to 6.7) in HCC scores, and results were similar with respect to laboratory testing and imaging. CONCLUSIONS: Substantial differences in diagnostic practices that are unlikely to be related to patient characteristics are observed across U.S. regions. The use of clinical or claims-based diagnoses in risk adjustment may introduce important biases in comparative-effectiveness studies, public reporting, and payment reforms. 2010 Massachusetts Medical Society
BACKGROUND: Current methods of risk adjustment rely on diagnoses recorded in clinical and administrative records. Differences among providers in diagnostic practices could lead to bias. METHODS: We used Medicare claims data from 1999 through 2006 to measure trends in diagnostic practices for Medicare beneficiaries. Regions were grouped into five quintiles according to the intensity of hospital and physician services that beneficiaries in the region received. We compared trends with respect to diagnoses, laboratory testing, imaging, and the assignment of Hierarchical Condition Categories (HCCs) among beneficiaries who moved to regions with a higher or lower intensity of practice. RESULTS: Beneficiaries within each quintile who moved during the study period to regions with a higher or lower intensity of practice had similar numbers of diagnoses and similar HCC risk scores (as derived from HCC coding algorithms) before their move. The number of diagnoses and the HCC measures increased as the cohort aged, but they increased to a greater extent among beneficiaries who moved to regions with a higher intensity of practice than among those who moved to regions with the same or lower intensity of practice. For example, among beneficiaries who lived initially in regions in the lowest quintile, there was a greater increase in the average number of diagnoses among those who moved to regions in a higher quintile than among those who moved to regions within the lowest quintile (increase of 100.8%; 95% confidence interval [CI], 89.6 to 112.1; vs. increase of 61.7%; 95% CI, 55.8 to 67.4). Moving to each higher quintile of intensity was associated with an additional 5.9% increase (95% CI, 5.2 to 6.7) in HCC scores, and results were similar with respect to laboratory testing and imaging. CONCLUSIONS: Substantial differences in diagnostic practices that are unlikely to be related to patient characteristics are observed across U.S. regions. The use of clinical or claims-based diagnoses in risk adjustment may introduce important biases in comparative-effectiveness studies, public reporting, and payment reforms. 2010 Massachusetts Medical Society
Authors: Grace Lu-Yao; Peter C Albertsen; Janet L Stanford; Therese A Stukel; Elizabeth S Walker-Corkery; Michael J Barry Journal: BMJ Date: 2002-10-05
Authors: Elliott S Fisher; David E Wennberg; Thérèse A Stukel; Daniel J Gottlieb; F L Lucas; Etoile L Pinder Journal: Ann Intern Med Date: 2003-02-18 Impact factor: 25.391
Authors: Elliott S Fisher; David E Wennberg; Thérèse A Stukel; Daniel J Gottlieb; F L Lucas; Etoile L Pinder Journal: Ann Intern Med Date: 2003-02-18 Impact factor: 25.391
Authors: Amber E Barnato; Chung-Chou H Chang; Max H Farrell; Judith R Lave; Mark S Roberts; Derek C Angus Journal: Med Care Date: 2010-02 Impact factor: 2.983
Authors: John D Birkmeyer; Therese A Stukel; Andrea E Siewers; Philip P Goodney; David E Wennberg; F Lee Lucas Journal: N Engl J Med Date: 2003-11-27 Impact factor: 91.245
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: Sanjoy Dey; Yajuan Wang; Roy J Byrd; Kenney Ng; Steven R Steinhubl; Christopher deFilippi; Walter F Stewart Journal: AMIA Annu Symp Proc Date: 2017-02-10
Authors: Andrew J Einstein; Shepard D Weiner; Adam Bernheim; Michal Kulon; Sabahat Bokhari; Lynne L Johnson; Jeffrey W Moses; Stephen Balter Journal: JAMA Date: 2010-11-15 Impact factor: 56.272
Authors: Patrick E Beeler; E John Orav; Diane L Seger; Patricia C Dykes; David W Bates Journal: J Am Med Inform Assoc Date: 2015-10-24 Impact factor: 4.497
Authors: Chad Tang; Xiudong Lei; Grace L Smith; Hubert Y Pan; Kenneth Hess; Aileen Chen; Karen E Hoffman; Brian F Chapin; Deborah A Kuban; Mitchell Anscher; Ya-Chen Tina Shih; Steven J Frank; Benjamin D Smith Journal: Pract Radiat Oncol Date: 2020-04-13
Authors: David J Nyweide; Denise L Anthony; Julie P W Bynum; Robert L Strawderman; William B Weeks; Lawrence P Casalino; Elliott S Fisher Journal: JAMA Intern Med Date: 2013-11-11 Impact factor: 21.873