Data Quality of Automated Comorbidity Lists in Patients With Mental Health and Substance Use Disorders

Joanna Woersching, Janet H. Van Cleave, Brian Egleston, Chenjuan Ma, Judith Haber, Deborah Chyun

Research output: Contribution to journalArticlepeer-review


EHRs provide an opportunity to conduct research on underrepresented oncology populations with mental health and substance use disorders. However, a lack of data quality may introduce unintended bias into EHR data. The objective of this article is describe our analysis of data quality within automated comorbidity lists commonly found in EHRs. Investigators conducted a retrospective chart review of 395 oncology patients from a safety-net integrated healthcare system. Statistical analysis included κ coefficients and a condition logistic regression. Subjects were racially and ethnically diverse and predominantly used Medicaid insurance. Weak κ coefficients (κ = 0.2-0.39, P <.01) were noted for drug and alcohol use disorders indicating deficiencies in comorbidity documentation within the automated comorbidity list. Further, conditional logistic regression analyses revealed deficiencies in comorbidity documentation in patients with drug use disorders (odds ratio, 11.03; 95% confidence interval, 2.71-44.9; P =.01) and psychoses (odds ratio, 0.04; confidence interval, 0.02-0.10; P <.01). Findings suggest deficiencies in automatic comorbidity lists as compared with a review of provider narrative notes when identifying comorbidities. As healthcare systems increasingly use EHR data in clinical studies and decision making, the quality of healthcare delivery and clinical research may be affected by discrepancies in the documentation of comorbidities.

Original languageEnglish (US)
Pages (from-to)497-505
Number of pages9
JournalCIN - Computers Informatics Nursing
Issue number7
StatePublished - Jul 2 2022


  • Automated comorbidity lists
  • Data quality
  • Drug use disorders
  • EHRs
  • Mental health disorders
  • Substance use disorders

ASJC Scopus subject areas

  • Health Informatics
  • Nursing (miscellaneous)


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