Development and Implementation of an AI System for Generating Clinical Urine Drug Test Sign-Outs

Can an artificial intelligence (AI)–based system be used to enable more-rapid interpretation and clinical sign-out of urine toxicology tests?In this prognostic study involving 83 553 urine drug tests at a single medical center, AI prediction of substance use patterns was highly accurate across 26 substances (mean area under the receiver operating characteristic curve, >0.99). Following integration into the clinical workflow, AI-based preliminary test interpretations reduced clinical sign-out times by 28.5 seconds on average (23% efficiency gain), while retaining high accuracy.These findings suggest that AI-based preliminary interpretation of urine drug testing results is fast and accurate and may provide substantial efficiency gains to the clinical service.

Corresponding Author: Brody H. Foy, DPhil, Department of Laboratory Medicine brodyfoy@uw.edu).

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