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.

Figure 1

Predictive Values of Two Algorithms for Identifying Physician-Recognized Depression by Prevalence

Figure 2

Mean Primary Care Physician Quality Performance by Disease-Identification Algorithm