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GMJ News > GMJ Briefs > Sentri7 AI System Fails to Prevent Months-Long Fentanyl Theft at Tennessee Hospital

Sentri7 AI System Fails to Prevent Months-Long Fentanyl Theft at Tennessee Hospital

GMJ
Last updated: 10/06/2026 15:17
By
Prof. Giorgi Pkhakadze
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1 Min Read
Hospital pharmacy with AI monitoring system interface showing drug dispensing data
AI-powered drug monitoring system Sentri7 failed to detect months of fentanyl theft at a Tennessee hospital, raising concerns about the reliability of automated surveillance technologies used at hundreds of U.S. healthcare facilities. — Photo: insung yoon / Pexels
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1 min read|146 words

An artificial intelligence drug monitoring system marketed to hundreds of U.S. hospitals has failed a critical real-world test, according to state investigation records. The Sentri7 software, specifically designed to detect controlled substance diversion patterns, did not flag systematic fentanyl theft by a healthcare worker at a Tennessee medical facility over several months in 2025.

The failure raises urgent questions about the effectiveness and validation of AI-powered surveillance systems deployed across American healthcare infrastructure. Sentri7, developed by FDA-regulated technology companies, is supposed to analyze medication dispensing patterns and identify anomalies indicative of theft or misuse. Yet the system missed clear evidence of drug diversion at an operational hospital facility.

Experts and healthcare administrators are now scrutinizing the gap between AI system marketing claims and demonstrated performance in preventing controlled substance diversion—a persistent challenge affecting patient safety and institutional integrity.

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ByProf. Giorgi Pkhakadze
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Prof. Giorgi Pkhakadze, MD, MPH, PhD, is Editor-in-Chief of the Georgian Medical Journal and Chair of the Public Health Institute of Georgia (PHIG). He is Professor and Head of the Department of Social and Behavioural Sciences at David Tvildiani Medical University, and Secretary/Treasurer of the UEMS Section of Public Health. ORCID: 0000-0001-7609-4515.

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