{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:23:49Z","timestamp":1771467829620,"version":"3.50.1"},"reference-count":43,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"6","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["Hepatology"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:sec>\n            <jats:title>Background and Aims<\/jats:title>\n            <jats:p>Although chronic HCV infection increases mortality, thousands of patients remain diagnosed\u2010but\u2010untreated (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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Approach and Results<\/jats:title>\n            <jats:p>We developed a phenotyping algorithm using Java and SQL and applied it to ~2.5\u00a0million 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\u2010RNA 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\u2010defined DBUs and they attempted to contact potential treatment candidates by phone. By June 2020, 30% (n\u00a0=\u00a01,862) had completed an HCV\u2010related appointment. Outcomes analysis revealed that DBU patients enrolled in our care coordination program were more likely to complete treatment (72% [n\u00a0=\u00a0219] vs. 54% [n\u00a0=\u00a0256]; <jats:italic toggle=\"yes\">P<\/jats:italic>\u00a0&lt;\u00a00.001) and to have a verified sustained virological response (67% vs. 46%; <jats:italic toggle=\"yes\">P<\/jats:italic>\u00a0&lt;\u00a00.001) than other patients. Forty\u2010eight percent (n\u00a0=\u00a02,992) of DBU patients could not be reached by phone, which was a major barrier to engagement. Nearly half of these patients had Fibrosis\u20104 scores\u00a0\u2265\u00a02.67, indicating significant fibrosis.\u00a0Multivariable logistic regression showed that DBUs who could not be contacted were less likely to have private insurance than those who could (18% vs. 50%; <jats:italic toggle=\"yes\">P<\/jats:italic>\u00a0&lt;\u00a00.001).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The digital DBU case\u2010finding algorithm efficiently identified potential HCV treatment candidates, freeing resources for navigation and coordination. The algorithm is portable and accelerated HCV elimination when incorporated in our comprehensive program.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1002\/hep.32086","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T13:38:13Z","timestamp":1627825093000},"page":"2974-2987","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":10,"title":["A Digital Case\u2010Finding Algorithm for Diagnosed but Untreated Hepatitis C: A Tool for Increasing Linkage to Treatment and Cure"],"prefix":"10.1097","volume":"74","author":[{"given":"Brooke","family":"Wyatt","sequence":"first","affiliation":[{"name":"Division of Liver Diseases,Icahn School of Medicine Mount Sinai,New York,NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ponni V.","family":"Perumalswami","sequence":"additional","affiliation":[{"name":"Division of Liver Diseases,Icahn School of Medicine Mount Sinai,New 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