How well do automated performance measures assess guideline implementation for new-onset depression in the Veterans Health Administration?

• HIT Description: Electronic medical record database. More info...
  
• Purpose of Study: Identify with algorithms valid cases of new-onset depression, and to evaluate agreement between performance measures using guidelines for depression treatment.
  
• Years of study: 1999-2000
  
• Study Design: Case series
  
• Outcomes: Impact on health care effectivness/quality
  

• Settings: The study was conducted at three Department of Veterans Affairs (VA) medical centers. Subjects were patients receiving outpatient care, who were identified with newly diagnosed depressive disorder.
• Intervention: Automated data abstraction compared to manual review of electronic medical records.
• Evaluation Method: Measures were number of cases of new-onset depression, and agreement of performance indicators.
• Description: The VA electronic medical record contains progress notes, pharmacy refills, results of laboratory tests, diagnosis, outpatient service sector codes, and Current Procedural Terminology codes.
• Quality of Care and Patient Safety Outcome: The manual record review indicated a high number of false positives among the 109 individuals with newly diagnosed depressive disorder who were identified via automated abstraction. Thirty-nine (35.8%) actually had documentation of depression diagnosis and antidepressant prescription or other treatment within the previous six months. Good to excellent ( kappa values: 0.60-0.82; raw percentile agreement: 72-100%) agreement was found between indicators of guideline-concordant care using automated and manual chart review methods.