Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities

Inquiry. 2020 Jan-Dec;57:46958020919275. doi: 10.1177/0046958020919275.


Quantitative metrics are used to develop profiles of health care institutions, including hospitals, nursing homes, and dialysis clinics. These profiles serve as measures of quality of care, which are used to compare institutions and determine reimbursement, as a part of a national effort led by the Center for Medicare and Medicaid Services in the United States. However, there is some concern about how misclassification in case-mix factors, which are typically accounted for in profiling, impacts results. We evaluated the potential effect of misclassification on profiling results, using 20 744 patients from 2740 dialysis facilities in the US Renal Data System. In this case study, we compared 30-day readmission as the profiling outcome measure, using comorbidity data from either the Center for Medicare and Medicaid Services Medical Evidence Report (error-prone) or Medicare claims (more accurate). Although the regression coefficient of the error-prone covariate demonstrated notable bias in simulation, the outcome measure-standardized readmission ratio-and profiling results were quite robust; for example, correlation coefficient of 0.99 in standardized readmission ratio estimates. Thus, we conclude that misclassification on case-mix did not meaningfully impact overall profiling results. We also identified both extreme degree of case-mix factor misclassification and magnitude of between-provider variability as 2 factors that can potentially exert enough influence on profile status to move a clinic from one performance category to another (eg, normal to worse performer).

Keywords: CMS-2728; USRDS; measurement error; medical evidence form; medicare claims; misclassification; profiling.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Comorbidity
  • Diagnosis-Related Groups / statistics & numerical data*
  • Female
  • Health Personnel*
  • Hospitals
  • Humans
  • Insurance Claim Review / statistics & numerical data*
  • Male
  • Medicare
  • Nursing Homes
  • Organizational Case Studies*
  • Patient Readmission / statistics & numerical data*
  • Quality of Health Care / statistics & numerical data
  • Renal Dialysis*
  • United States