Ž . Journal of Health Economics 19 2000 291–309 www.elsevier.nlrlocatereconbase Medical profiling: improving standards and risk adjustments using hierarchical models James F. Burgess Jr. a, ) , Cindy L. Christiansen b , Sarah E. Michalak c , Carl N. Morris c a ( ) Management Science Group, Department of Veterans Affairs 518 r MSG , 200 Springs Road, Bedford, MA 01730, USA b HarÕard Medical School and HarÕard Pilgrim Health Care, USA c Statistics Department at HarÕard UniÕersity, Cambridge, MA, USA Abstract The conclusions from a profile analysis to identify performance extremes can be affected substantially by the standards and statistical methods used and by the adequacy of risk adjustment. Medically meaningful standards are proposed to replace common statistical standards. Hierarchical regression methods can handle several levels of random variation, make risk adjustments for the providers’ case-mix differences, and address the proposed standards. These methods determine probabilities needed to make meaningful profiles of medical units based on standards set by all appropriate parties. Published by Elsevier Science B.V. JEL classification: I18; C11; L15 Keywords: Profiling standards; Hierarchical models; Regression-to-the-mean; Risk adjustment 1. Introduction Measuring and understanding differences in health care provider performance are drawing increasing attention from government agencies providing care or ) Corresponding author. Tel.: q1-781-687-2488; fax: q1-781-687-2376; e-mail: burgess@world.std.com 0167-6296r00r$ - see front matter Published by Elsevier Science B.V. Ž . PII: S0167-6296 99 00034-X