title: Identifying possible indicators of systematic overuse of health care procedures with claims data.
abstract: BACKGROUND
  Health care quality is frequently described with measures representing the overall performance of a health care system. Despite the growing attention to overuse of health care resources, there is little experience with aggregate measures of overuse.
  
  
  OBJECTIVE
  To identify a set of possible indicators of overuse that can be operationalized with claims data and to describe variation in these indicators across the hospital referral regions (HRRs).
  
  
  METHODS
  Using an environmental scan, we identified published descriptions of overused procedures. We assessed each procedure's feasibility for measurement with claims and developed algorithms for occurrences of procedures in patients unlikely to benefit. Using a 5% sample of Medicare claims from 2008, we calculated summary statistics to illustrate variance in the use across HRRs.
  
  
  RESULTS
  A total of 613 procedures were identified as overused; 20 had abundant frequency and variance to be possible measures of systematic overuse. These included 13 diagnostic tests, 2 tests for screening, 1 for monitoring, and 4 therapeutic procedures. The usage varied markedly across HRRs. For illustration, 1 HRR used computed tomography for rhinosinusitis diagnosis in 80 of 1000 beneficiaries (mean usage across HRRs was 14/1000). Among 1,451,142 beneficiaries, 14% had at least one overuse event (range, 8.4%-27%).
  
  
  CONCLUSIONS
  We identified a set of overused procedures that may be used as measures of overuse and that demonstrate significant variance in their usage. The implication is that an index of overuse might be built from these indicators that would reveal systematic patterns of overuse within regions. Alternatively, these indicators may be valuable in the quality improvement efforts.
authors.affiliation: *Department of Medicine, Johns Hopkins University School of Medicine †Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD.
authors.forename: Jodi B
authors.initials: JB
authors.lastname: Segal
authors.forename: John F P
authors.initials: JF
authors.lastname: Bridges
authors.forename: Hsien-Yen
authors.initials: HY
authors.lastname: Chang
authors.forename: Eva
authors.initials: E
authors.lastname: Chang
authors.forename: Najlla
authors.initials: N
authors.lastname: Nassery
authors.forename: Jonathan
authors.initials: J
authors.lastname: Weiner
authors.forename: Kitty S
authors.initials: KS
authors.lastname: Chan
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006300:Health Services Misuse / Q000706:statistics & numerical data*; D006801:Humans; D007345:Insurance Claim Review / Q000458:organization & administration / Q000706:statistics & numerical data*; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data; D014481:United States