Drug diversion is the illegal transfer, distribution, and use of controlled substances intended for patient care. This act is one of the most persistent threats in healthcare. Despite advances in technology, stricter regulations, and increased awareness, drug diversion is still happening within hospitals, ambulatory surgery centers, and other healthcare settings.
Key takeaways
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A 2025 case involving a Tennessee hospital has reignited discussions about drug diversion prevention after an artificial intelligence (AI) powered monitoring system reportedly failed to detect months of fentanyl theft by a nurse.
The incident highlights that while technology can strengthen surveillance efforts, it cannot replace human vigilance, a strong organizational culture, and comprehensive diversion-prevention programs. This case offers important lessons about the realities of drug diversion and the role of AI in healthcare security.
What is drug diversion in healthcare?
Drug diversion refers to “deflection of prescription drugs from medical sources into the illegal market,” according to the Centers for Medicare and Medicaid Services (CMS). Diversion can occur in different ways, such as:
- Removing medications from automated dispensing cabinets without authorization
- Falsifying medication documentation
- Tampering with medication waste procedures
- Substituting medications with other substances
Healthcare professionals who divert medications may struggle with substance use disorders, although financial gain and illegal distribution can also be motivating factors.
What are the consequences of drug diversion?
The consequences extend far beyond the individual involved. Drug diversion can lead to:
- Inadequate pain management for patients
- Exposure to contaminated medications
- Increased risk of infectious disease transmission
- Regulatory penalties
- Legal liability
- Damage to organizational reputation
- Safety risks for healthcare workers and patients
Data from the Centers for Disease Control and Prevention show that healthcare-associated drug diversion incidents have been linked to multiple outbreaks of bloodborne infections, including hepatitis C.
What happened in this case?
In 2025 in Chattanooga, Tennessee, an advanced practice nurse working at a hospital admitted to diverting fentanyl that remained after surgical procedures, according to CBS News. The diversion reportedly occurred over several months, and observations from other staff members ultimately triggered the investigation that uncovered the drug diversion activity.
The hospital reportedly had an AI-powered medication monitoring system in place, but the system failed to detect the diversion during the months it was occurring. Instead, concerns raised by staff led to the investigation. The case has sparked discussion about the capabilities and limitations of AI-based diversion monitoring tools and whether they can reliably identify every instance of drug diversion.
"This case shows that healthcare professionals are still an important safeguard against drug diversion,” said Cara Lunsford, BSN, RN, CPHON, Vice President of Healthcare Policy and Clinical Solutions. “Technology can identify patterns, but frontline staff often notice behavioral changes and workflow concerns.”
The incident is particularly significant because fentanyl is the most commonly diverted controlled substance among healthcare professionals, according to data from the Legislative Analysis and Public Policy Association. Given its potency and widespread use in surgical and hospital settings, fentanyl diversion poses serious risks to both healthcare workers and patients.
While AI and analytics tools can help identify suspicious patterns, this case demonstrates that technology alone isn't foolproof and that staff awareness, reporting, and oversight remain critical components of an effective drug diversion prevention program.
Why didn't AI detect the drug diversion?
While AI systems can reduce instances of drug diversion, these systems are only as effective as the data they receive and the patterns they’re designed to recognize.
Many diversion detection platforms analyze information from multiple sources, including:
- Automated dispensing cabinet (ADC) transactions
- Electronic health record (EHR) documentation
- Medication administration records (MARs)
- Controlled substance waste documentation
- Staff scheduling and badge access data
These systems look for unusual patterns such as excessive medication withdrawals, frequent overrides, discrepancies in wasting practices, or medication administration patterns that differ significantly from peers.
However, sophisticated diversion schemes may not immediately trigger alerts. If documentation appears consistent or if diversion occurs in small amounts over time, abnormal patterns can be difficult to distinguish from normal clinical practice.
As a result, AI should be viewed as a decision-support tool rather than a standalone solution for diversion prevention.
What does AI do well in drug diversion prevention?
Despite its limitations, AI offers several advantages over traditional manual auditing methods.
Faster detection of risk patterns
Manual diversion investigations often rely on periodic audits that may occur weeks or months after suspicious activity begins. AI can continuously analyze large volumes of data and identify potential concerns in near real time.
Reduction of human error
Healthcare organizations manage thousands of controlled substance transactions each day. AI can review significantly more data than human reviewers alone and can identify subtle trends that might otherwise go unnoticed.
Identification of emerging trends
AI systems can also help organizations identify broader trends in medication handling, waste practices, and controlled substance management that may indicate process vulnerabilities.
Why human observation remains critical
One of the most important lessons from the Tennessee case is that coworkers ultimately noticed concerning behaviors and reported them. This reinforces a reality that healthcare leaders have long understood. Employees are often the first line of defense against diversion.
Lunsford noted that it’s important to watch for patterns such as documentation discrepancies, frequent controlled substance access, or sudden changes in behavior and performance.
When staff members understand diversion risks and feel comfortable reporting concerns, organizations are more likely to identify problems before patient harm occurs.
"Leaders need to create a culture where reporting concerns is viewed as a patient safety responsibility, not an accusation," said Lunsford. "Clear reporting processes, confidentiality, and consistent follow-up can help staff feel more comfortable speaking up."
How do you build a comprehensive diversion prevention program?
Regulatory agencies such as the Drug Enforcement Administration (DEA), CMS, and The Joint Commission emphasize that diversion prevention requires multiple layers of oversight.
Effective programs typically include:
- Advanced monitoring technology and analytics
- Routine controlled substance audits
- Clear reporting procedures
- Staff education and competency training
- Anonymous reporting mechanisms
- Strong leadership oversight
- Support programs for healthcare workers with substance use disorders
Technology can enhance each of these efforts, but none can fully replace them.
What does the future of AI systems in medication monitoring look like?
As AI technology evolves, healthcare organizations will likely see more sophisticated tools capable of identifying complex diversion patterns, predicting risk factors, and integrating data from multiple systems simultaneously.
According to Lunsford, nurses should understand both the capabilities and limitations of AI. She emphasized that training should focus on accurate documentation, critical thinking, and the continued importance of human judgment in patient safety efforts.
While AI offers significant advantages, its true strength is in supporting and strengthening human expertise, not replacing it. Successful diversion prevention programs combine technology, regulatory compliance, employee education, and a culture of accountability.
Conclusion
The Tennessee case highlights that AI isn’t a fail-safe solution for drug diversion prevention. While AI can significantly improve surveillance capabilities and accelerate investigations, it cannot replace attentive staff, engaged leadership, and a comprehensive diversion prevention strategy.
The most effective approach combines advanced technology with human oversight to protect patients, healthcare workers, and organizations from the serious consequences of drug diversion.
Frequently asked questions on drug diversion in healthcare
Can AI detect drug diversion in healthcare?
AI can help detect drug diversion by analyzing medication transactions, controlled substance records, and documentation patterns. However, AI cannot identify every diversion event and should be used alongside human oversight, audits, and staff reporting.
Why can AI miss drug diversion incidents?
AI may miss diversion when documentation appears normal, diversion occurs in small amounts over time, or behaviors do not create obvious data anomalies. Sophisticated diversion methods can sometimes evade automated detection systems.
What is drug diversion in healthcare?
Drug diversion is the theft, misuse, or unauthorized transfer of controlled substances intended for patient care. It can involve medication theft, falsified documentation, improper wasting procedures, or tampering with medications.
What are the warning signs of drug diversion by healthcare workers?
Common warning signs include frequent medication discrepancies, excessive controlled substance wasting, unusual medication access patterns, behavioral changes, declining job performance, and documentation inconsistencies.
What is the most effective way to prevent drug diversion?
The most effective approach combines AI-powered monitoring, routine audits, staff education, clear reporting procedures, leadership oversight, and a culture that encourages employees to report concerns without fear of retaliation.
