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Related Experiment Videos

Identifying physician-recognized depression from administrative data: consequences for quality measurement.

Claire M Spettell1, Terry C Wall, Jeroan Allison

  • 1Health Informatics/USQA, Blue Bell, PA 19422, USA.

Health Services Research
|September 13, 2003
PubMed
Summary

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Identifying depression in administrative data is challenging. Two algorithms showed varying accuracy, with one having a high false positive rate, raising concerns for quality reporting.

Area of Science:

  • Health Services Research
  • Medical Informatics
  • Psychiatry

Background:

  • Administrative data is crucial for healthcare quality measurement, including systems like the Health Plan Employer Data and Information Set (HEDIS).
  • Accurate identification of patients with depression from administrative data is limited by several factors.

Purpose of the Study:

  • To investigate and compare two algorithms for identifying physician-recognized depression using administrative data.
  • To assess the performance of these algorithms in terms of sensitivity, specificity, and positive predictive value.

Main Methods:

  • Two algorithms were developed to identify depression: Algorithm 1 required two criteria (depression diagnosis or antidepressant claim), while Algorithm 2 added a mandatory depression diagnosis.
  • The study utilized data from a large managed care organization, analyzing member panels enrolled throughout 1997.

Related Experiment Videos

  • Medical records were reviewed for a subset of patients, and large patient cohorts were identified based on each algorithm.

    • Main Results:

    • Algorithm 1 demonstrated higher sensitivity (95%) and positive predictive value (49%), while Algorithm 2 showed higher specificity (88%) and a better positive predictive value (60%).
    • Algorithm 2 identified patients with higher rates of follow-up visits and appropriate antidepressant dosing compared to Algorithm 1.
    • Both algorithms exhibited significant false positive rates, impacting the reliability of depression identification.

    Conclusions:

    • The construction of the denominator significantly influences quality metrics derived from administrative data.
    • High false positive rates in both algorithms raise concerns regarding the interpretation of depression quality reports based on administrative data.
    • Further research is needed to refine methods for accurate depression identification in administrative datasets.