Evaluation of a clinical decision support alert to identify hepatic dysfunction and need for medication therapy adjustment in hospitalized patients.

Abstract

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Purpose: To optimize the hepatic dysfunction alert tool at our institution to identify appropriate patients and minimize irrelevant alerts.

Methods: This single-center, retrospective review included adults hospitalized over a 1-month period for whom a hepatic dysfunction alert fired for a medication order placed in the electronic health record. The existing alert determines hepatic dysfunction based on laboratory tests. The primary objective was to determine the proportion of patients with an alert that was deemed to be clinically relevant. Alerts were considered relevant if the patient had a Child-Pugh score in class B or C and were ordered a medication with a hepatic warning from FDA or LiverTox. The performance of 14 alternative models was evaluated.

Results: A total of 1,541 alerts fired for 309 patients. Of these patients, 155 were randomly selected for the analysis, and the alert was deemed relevant in 86 patients (55%). Patients with relevant alerts were more likely to have documented liver disease and worsening measures on liver function tests. Of the alternative models evaluated, a model that excluded INR and albumin resulted in a 27% decrease in the number of alerts fired, of which 73% were relevant; however, it failed to identify 30% of patients with relevant hepatic dysfunction. None of the other models performed better.

Conclusion: The existing hepatic dysfunction clinical decision support tool correctly identifies patients with relevant hepatic dysfunction only 55% of the time. Alternative models were able to improve the rate of relevant results, but not without missing patients with relevant hepatic dysfunction.