Using the Electronic Health Record to Characterize the Hepatitis C Virus Care Cascade

Shannon M Christy1,2,3,4, Richard R Reich5, Julie A Rathwell3,6

  • 125301 Department of Health Outcomes and Behavior, Division of Population Science, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

Insights

Electronic health records reliably capture hepatitis C virus (HCV) testing data. However, manual review is still needed to track HCV treatment initiation and completion for effective screening interventions.

Area of Science:

  • Hepatology
  • Public Health
  • Health Informatics

Background:

  • Chronic hepatitis C virus (HCV) infection is a significant risk factor for hepatocellular carcinoma.
  • Effective screening and treatment interventions are crucial for managing HCV infection.
  • Evaluating data sources for accuracy in capturing HCV testing and treatment is essential for public health initiatives.

Purpose of the Study:

  • To assess the concordance between electronic health record (EHR) data and manual medical record review for HCV testing and treatment data.
  • To estimate the rates of HCV treatment initiation and completion among patients with chronic HCV infection.

Main Methods:

  • A review of 177 patient medical records from an academic health care system (2015-2018).
  • Comparison of EHR data warehouse with manual medical record review using Cohen κ coefficient for concordance.
  • Descriptive statistics to determine treatment initiation and completion rates.

Main Results:

  • Generally good concordance (κ = 0.66–0.87) between EHR and manual review for HCV testing data.
  • The EHR data warehouse did not capture crucial HCV treatment variables.
  • Manual review identified 28 patients with chronic HCV; 14 initiated treatment, and 9 achieved viral cure.

Conclusions:

  • EHR data is generally reliable for HCV testing information.
  • Manual medical record review remains necessary for comprehensive data on HCV treatment initiation and completion.
  • Improvements in EHR design and natural language processing are needed to fully capture treatment data.
Abstract

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