title: Medical profiling: improving standards and risk adjustments using hierarchical models.
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.
authors.affiliation: Management Science Group, Department of Veterans Affairs, Bedford, MA 01730, USA. burgess@world.std.com
authors.forename: J F
authors.initials: JF
authors.lastname: Burgess
authors.forename: C L
authors.initials: CL
authors.lastname: Christiansen
authors.forename: S E
authors.initials: SE
authors.lastname: Michalak
authors.forename: C N
authors.initials: CN
authors.lastname: Morris
mesh_terms: D006787:Hospitals, Veterans / Q000706:statistics & numerical data; D007362:Intensive Care Units / Q000706:statistics & numerical data; D015233:Models, Statistical; D010359:Patient Readmission / Q000706:statistics & numerical data; D016012:Poisson Distribution; D011785:Quality Assurance, Health Care / Q000458:organization & administration* / Q000706:statistics & numerical data; D020379:Risk Adjustment / Q000379:methods / Q000706:statistics & numerical data*