title: Measuring low-value care in Medicare.
abstract: OBJECTIVE
  Despite the importance of identifying and reducing wasteful health care use, few direct measures of overuse have been developed. Direct measures are appealing because they identify specific services to limit and can characterize low-value care even among the most efficient providers.
  
  
  OBJECTIVE
  To develop claims-based measures of low-value services, examine service use (and associated spending) detected by these measures in Medicare, and determine whether patterns of use are related across different types of low-value services.
  
  
  METHODS
  Drawing from evidence-based lists of services that provide minimal clinical benefit, we developed 26 claims-based measures of low-value services. Using 2009 claims for 1,360,908 Medicare beneficiaries, we assessed the proportion of beneficiaries receiving these services, mean per-beneficiary service use, and the proportion of total spending devoted to these services. We compared the amount of use and spending detected by versions of these measures with different sensitivity and specificity. We also estimated correlations between use of different services within geographic areas, adjusting for beneficiaries' sociodemographic and clinical characteristics.
  
  
  METHODS
  Use and spending detected by 26 measures of low-value services in 6 categories: low-value cancer screening, low-value diagnostic and preventive testing, low-value preoperative testing, low-value imaging, low-value cardiovascular testing and procedures, and other low-value surgical procedures.
  
  
  RESULTS
  Services detected by more sensitive versions of measures affected 42% of beneficiaries and constituted 2.7% of overall annual spending. Services detected by more specific versions of measures affected 25% of beneficiaries and constituted 0.6% of overall spending. In adjusted analyses, low-value spending detected in geographic regions at the 5th percentile of the regional distribution of low-value spending ($227 per beneficiary) exceeded the difference in detected low-value spending between regions at the 5th and 95th percentiles ($189 per beneficiary). Adjusted regional use was positively correlated among 5 of 6 categories of low-value services (mean r for pairwise, between-category correlations, 0.33; range, 0.14-0.54; P ≤ .01).
  
  
  CONCLUSIONS
  Services detected by a limited number of measures of low-value care constituted modest proportions of overall spending but affected substantial proportions of beneficiaries and may be reflective of overuse more broadly. Performance of claims-based measures in supporting targeted payment or coverage policies to reduce overuse may depend heavily on how the measures are defined.
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
authors.forename: Aaron L
authors.initials: AL
authors.lastname: Schwartz
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts2Division of General Internal Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
authors.forename: Bruce E
authors.initials: BE
authors.lastname: Landon
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts3The Commonwealth Fund, New York, New York4Menzies Centre for Health Policy, The University of Sydney, Sydney, New South Wales, Australia5Lown Institute, Boston, Massachusetts.
authors.forename: Adam G
authors.initials: AG
authors.lastname: Elshaug
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Chernew
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts6Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: J Michael
authors.initials: JM
authors.lastname: McWilliams
mesh_terms: D000465:Algorithms; D006300:Health Services Misuse / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D006278:Medicare / Q000191:economics / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity; D014481:United States