A Digital Case-Finding Algorithm for Diagnosed but Untreated Hepatitis C: A Tool for Increasing Linkage to Treatment and Cure

BACKGROUND AND AIMS Although chronic HCV infection increases mortality, thousands of patients remain diagnosed-but-untreated (DBU). We aimed to (1) develop a DBU phenotyping algorithm, (2) use it to facilitate case finding and linkage to care, and (3) identify barriers to successful treatment. APPROACH AND RESULTS We developed a phenotyping algorithm using Java and SQL and applied it to ~2.5 million EPIC electronic medical records (EMRs; data entered January 2003 to December 2017). Approximately 72,000 EMRs contained an HCV International Classification of Diseases code and/or diagnostic test. The algorithm classified 10,614 cases as DBU (HCV-RNA positive and alive). Its positive and negative predictive values were 88% and 97%, respectively, as determined by manual review of 500 EMRs randomly selected from the ~72,000. Navigators reviewed the charts of 6,187 algorithm-defined DBUs and they attempted to contact potential treatment candidates by phone. By June 2020, 30% (n = 1,862) had completed an HCV-related appointment. Outcomes analysis revealed that DBU patients enrolled in our care coordination program were more likely to complete treatment (72% [n = 219] vs. 54% [n = 256]; P