title: Improving quality assessment through multilevel modeling: the case of nursing home compare.
abstract: OBJECTIVE
  To demonstrate how multilevel modeling and empirical Bayes (EB) estimates can improve Medicare's Nursing Home Compare quality measures (QMs).
  
  
  METHODS
  Secondary data from July 1 to September 30, 2004. Facility-level QMs were estimated from minimum data set (MDS) assessments for approximately 31,000 Minnesota nursing home residents in 393 facilities.
  
  
  METHODS
  Prevalence and incidence rates for 12 nursing facility QMs (e.g., use of physical restraints, pressure sores, and weight loss) were estimated with EB methods and risk adjustment using a hierarchical general linear model. Three sets of rates were developed: Nursing Home Compare's current method, unadjusted EB rates, and risk-adjusted EB rates. Bayesian 90 percent credibility intervals (CIs) were constructed around EB rates, and these were used to flag facilities for potential quality of care problems.
  
  
  METHODS
  MDS assessments were performed by nursing facility staff, transmitted electronically to the Minnesota Department of Health, and provided to the investigators.
  
  
  RESULTS
  Facility rates and rankings for the 12 QMs differed substantially using the multilevel models compared with current methods. The EB estimated rates shrank considerably toward the population mean. Risk adjustment had a large impact on some QM rates and a more modest impact on others. When EB CIs were used to flag problem facilities, there was wide variation across QMs in the percentage of facilities flagged.
  
  
  CONCLUSIONS
  Multilevel modeling should be applied to Nursing Home Compare and more widely in other health care quality assessment systems.
authors.affiliation: Center for Health Policy, School of Public and Environmental Affairs, IUPUI, Indianapolis, IN 46204, USA.
authors.forename: Greg
authors.initials: G
authors.lastname: Arling
authors.forename: Teresa
authors.initials: T
authors.lastname: Lewis
authors.forename: Robert L
authors.initials: RL
authors.lastname: Kane
authors.forename: Christine
authors.initials: C
authors.lastname: Mueller
authors.forename: Shannon
authors.initials: S
authors.lastname: Flood
mesh_terms: D001499:Bayes Theorem; D019985:Benchmarking / Q000379:methods*; D014487:Centers for Medicare and Medicaid Services, U.S.; D006801:Humans; D015994:Incidence; D006278:Medicare / Q000592:standards*; D008910:Minnesota; D017146:Models, Organizational; D009735:Nursing Homes / Q000592:standards*; D015995:Prevalence; D011785:Quality Assurance, Health Care / Q000379:methods*; D019984:Quality Indicators, Health Care; D020379:Risk Adjustment; D018570:Risk Assessment / Q000379:methods*; D014481:United States