title: The effect of provider-level ascertainment bias on profiling nursing homes.
abstract: Profiling health care providers for the purpose of public reporting and quality improvement has become commonplace. Recently, the Centers for Medicare and Medicaid Services (CMS) began publishing measures of quality for every Medicare/Medicaid-certified nursing home in the country. The facility-specific quality indicators (QIs) reported by CMS are based on quarterly measures from the minimum data set (MDS). However, some QIs from the MDS are potentially subject to ascertainment bias. Ascertainment bias would occur if there was variation in the way items that make up QIs are measured by nurses from each facility. This is potentially a problem for difficult-to-measure items such as pain and pressure ulcers. To assess the impact of ascertainment bias on profiling, we utilize data from a reliability study of nursing homes from six states. We develop methods for profiling providers in situations where the data consist of a response variable for each subject based on assessments from an internal rater, and, for a subset of subjects in each facility, a response variable based on assessments from an independent (external) rater. The internal assessments are potentially subject to provider-level ascertainment bias, whereas the independent assessments are considered the 'gold standard'. Our methods extend popular Bayesian approaches for profiling by using the paired observations from the subset of subjects with error-prone and error-free assessments to adjust for ascertainment bias. We apply the methods to MDS merged with the reliability data, and compare the bias-corrected profiles with those of standard approaches.
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester, NY, USA. jason_roy@urmc.rochester.edu
authors.forename: Jason
authors.initials: J
authors.lastname: Roy
authors.forename: Vincent
authors.initials: V
authors.lastname: Mor
mesh_terms: D000368:Aged; D015982:Bias; D001699:Biometry; D003627:Data Interpretation, Statistical; D006282:Health Personnel / Q000592:standards / Q000706:statistics & numerical data*; D006707:Homes for the Aged / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D007087:Illinois; D008390:Markov Chains; D008484:Medicaid; D006278:Medicare; D015233:Models, Statistical; D009010:Monte Carlo Method; D009735:Nursing Homes / Q000592:standards / Q000706:statistics & numerical data*; D017063:Outcome Assessment, Health Care; D011785:Quality Assurance, Health Care; D014481:United States