title: Identifying physician-recognized depression from administrative data: consequences for quality measurement.
abstract: BACKGROUND
  Multiple factors limit identification of patients with depression from administrative data. However, administrative data drives many quality measurement systems, including the Health Plan Employer Data and Information Set (HEDIS).
  
  
  METHODS
  We investigated two algorithms for identification of physician-recognized depression. The study sample was drawn from primary care physician member panels of a large managed care organization. All members were continuously enrolled between January 1 and December 31, 1997. Algorithm 1 required at least two criteria in any combination: (1) an outpatient diagnosis of depression or (2) a pharmacy claim for an antidepressant Algorithm 2 included the same criteria as algorithm 1, but required a diagnosis of depression for all patients. With algorithm 1, we identified the medical records of a stratified, random subset of patients with and without depression (n = 465). We also identified patients of primary care physicians with a minimum of 10 depressed members by algorithm 1 (n = 32,819) and algorithm 2 (n = 6,837).
  
  
  RESULTS
  The sensitivity, specificity, and positive predictive values were: Algorithm 1: 95 percent, 65 percent, 49 percent; Algorithm 2: 52 percent, 88 percent, 60 percent. Compared to algorithm 1, profiles from algorithm 2 revealed higher rates of follow-up visits (43 percent, 55 percent) and appropriate antidepressant dosage acutely (82 percent, 90 percent) and chronically (83 percent, 91 percent) (p < 0.05 for all).
  
  
  CONCLUSIONS
  Both algorithms had high false positive rates. Denominator construction (algorithm 1 versus 2) contributed significantly to variability in measured quality. Our findings raise concern about interpreting depression quality reports based upon administrative data.
authors.affiliation: Health Informatics/USQA, Blue Bell, PA 19422, USA.
authors.forename: Claire M
authors.initials: CM
authors.lastname: Spettell
authors.forename: Terry C
authors.initials: TC
authors.lastname: Wall
authors.forename: Jeroan
authors.initials: J
authors.lastname: Allison
authors.forename: Jaimee
authors.initials: J
authors.lastname: Calhoun
authors.forename: Richard
authors.initials: R
authors.lastname: Kobylinski
authors.forename: Rachel
authors.initials: R
authors.lastname: Fargason
authors.forename: Catarina I
authors.initials: CI
authors.lastname: Kiefe
mesh_terms: D000465:Algorithms; D000928:Antidepressive Agents / Q000627:therapeutic use; D003625:Data Collection; D003866:Depressive Disorder / Q000175:diagnosis* / Q000188:drug therapy / Q000453:epidemiology; D005194:Family Practice / Q000592:standards* / Q000706:statistics & numerical data; D006264:Health Benefit Plans, Employee / Q000592:standards; D006302:Health Services Research / Q000379:methods; D006801:Humans; D038801:International Classification of Diseases; D008329:Managed Care Programs / Q000592:standards; D008873:Mid-Atlantic Region / Q000453:epidemiology; D009511:New England / Q000453:epidemiology; D010349:Patient Compliance; D015995:Prevalence; D011320:Primary Health Care / Q000592:standards; D011785:Quality Assurance, Health Care; D012680:Sensitivity and Specificity
chemical_list: D000928:Antidepressive Agents