Journal
CIN-COMPUTERS INFORMATICS NURSING
Volume 40, Issue 7, Pages 497-505Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/CIN.0000000000000889
Keywords
Automated comorbidity lists; Data quality; Drug use disorders; EHRs; Mental health disorders; Substance use disorders
Funding
- Mount Sinai Medical Center, Mount Sinai Medical Center Nursing Alumnae Award
- NYU-HHC Clinical Translational Science Institute [NIH/NCATS 1UL1 TR001445]
- NYU Cancer Institute Translational Research Pilot Fund
- American Association of Nurse Anesthetists Foundation [2017-GS-2]
- NYU Cancer Institute
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This study analyzed the data quality of automated comorbidity lists in electronic health records and found deficiencies in documentation. The automatic comorbidity lists were found to be less accurate in identifying comorbidities compared to provider narrative notes. This may have implications for healthcare delivery and clinical research.
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 kappa coefficients and a condition logistic regression. Subjects were racially and ethnically diverse and predominantly used Medicaid insurance. Weak kappa coefficients (kappa = 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.
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