A Text Searching Tool to Identify Patients with Idiosyncratic Drug-Induced Liver Injury.

Abstract

Idiosyncratic drug-induced liver injury (DILI) is an uncommon but important cause of liver disease that is challenging to diagnose and identify in the electronic medical record (EMR). To develop an accurate, reliable, and efficient method of identifying patients with bonafide DILI in an EMR system. In total, 527,000 outpatient and ER encounters in an EPIC-based EMR were searched for potential DILI cases attributed to eight drugs. A searching algorithm that extracted 200 characters of text around 14 liver injury terms in the EMR were extracted and collated. Physician investigators reviewed the data outputs and used standardized causality assessment methods to adjudicate the potential DILI cases. A total of 101 DILI cases were identified from the 2564 potential DILI cases that included 62 probable DILI cases, 25 possible DILI cases, nine historical DILI cases, and five allergy-only cases. Elimination of the term "liver disease" from the search strategy improved the search recall from 4 to 19%, while inclusion of the four highest yield liver injury terms further improved the positive predictive value to 64% but reduced the overall case detection rate by 47%. RUCAM scores of the 57 probable DILI cases were generally high and concordant with expert opinion causality assessment scores. A novel text searching tool was developed that identified a large number of DILI cases from a widely used EMR system. A computerized extraction of dictated text followed by the manual review of text snippets can rapidly identify bona fide cases of idiosyncratic DILI.