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In late May, a nurse anesthetist was sentenced to nearly three years in federal prison for tampering with opioid analgesics intended for patients. For nearly eight months, while working in a hospital surgery and birthing center, he tampered with fentanyl so that he could use the drugs himself instead of giving patients their full doses.
According to the U.S. Attorney’s Office for the Northern District of Iowa, a subsequent review of procedures performed by this clinician revealed that he had “purported to use fentanyl consistently in colonoscopies and cataract surgeries as early as December 2017. A prior review of [his] obstetrics patients in 2018 revealed that one in four of [his] spinal anesthesia patients received narcotics that were insufficient to reduce labor pain, such that the women giving birth also required general anesthesia.”
How did the hospital discover the tampering, also known as clinical drug diversion? Through its own investigation, triggered by something suspicious in the nurse anesthetist’s behavior? Anomalies in medication management? Obvious tampering with medications?
Although co-workers had expressed concerns about some of his behaviors, the tipping point occurred when a hospital visitor found him semiconscious in a public restroom of the hospital.
How is it possible that this nurse’s malfeasance was only discovered by accident? If drug diversion is found by accident, or at a point when other employees or patients are noticing changes in behaviors, the damage may already be done. Patients are likely to have been harmed. Hospital safety protocols have been compromised, and regulatory compliance has been put at risk.
Hospitals and pharmacies should have programs to prevent incidents from ever reaching this point. Some do, but the quality and usefulness of their programs vary widely. Some systems rely on manual audits and reviews, which catch only a small percentage of clinicians who divert controlled substances. Other systems rely on slightly more sophisticated systems, such as automated dispensing cabinets (ADCs), RFIDs (tags to electronically identify objects) and other devices designed to improve medication management, prevent medication errors and, ultimately, detect clinical drug diversion.
However, healthcare workers who divert controlled substances are like other people with addiction disorders: They quickly find ways around most systems. The bottom line is that it is a criminal act to divert medications intended for patients. And while the health system does what it can to limit risks of harm to patients and staff — especially by complying with hundreds of federal and state policies and regulations, as well as accrediting and licensing bodies — they have yet to find truly effective ways to prevent drug diversion.
Fortunately, with the widespread adoption of compliance analytics to improve data management in electronic health records — especially to protect patient data, improve efficiencies for hospital-wide systems as they consolidate resources, prevent data breaches and more — the ability to detect and prevent drug diversion is quickly evolving.
The healthcare industry is more readily recognizing the problem of clinical drug diversion and working to identify and prevent diversion within facilities. This trend is being seen in the release of recommendations and policy guidelines by accrediting bodies, such as the Joint Commission, and professional organizations, such as the American Association of Nurse Anesthetists, the American Society of Health-System Pharmacists, and the Emergency Nurses Association.
With artificial intelligence (AI) and machine learning — leveraged by the expertise of clinical professionals, compliance experts and investigators — hospitals can readily begin to see what is happening as medications travel through their systems. This insight works to identify early warning signs of possible diversion, allowing an organization to intervene early.
AI allows hospitals to eliminate labor- and resource-intensive aspects of their current manual audits and report-based systems. It helps compile disparate data from multiple sources in particular ways that, when reviewed by a diversion or pharmacy team, create a picture that makes sense. For example, with AI, a case can be opened showing how often in a certain time period a nurse has dispensed a specific medication too many times during her shift or without orders or has held it too long before administering it to a patient.
In short, AI offers more efficient identification of diversion, improving hospital and health systems’ ability to enforce their policies consistently and increasing their ability to offer targeted education (including opportunities to seek help through appropriate channels) to all staff and to those identified as diverters. More than this, it offers a life-saving intervention for compliance offices, disrupting the ability for diverters to access controlled substances and misuse them in ways that can harm patients or themselves with life-altering ramifications.
Organizations that want to improve their drug diversion monitoring can begin by evaluating their current workflows, both to assess how effective the process is and to identify gaps in it. Most organizations today use some combination of reactive models, which rely on reports by other staff members, or report-based models, which provide a combination of reports about individual behaviors, such as accessing ADCs or dispensing a high volume of controlled substances.
A case-based program that leverages AI offers a new alternative over these legacy approaches because it focuses on aggregating an array of relevant data sources, reviewing 100% of transactions and presenting only those events that have a high likelihood of being a form of diversion. After organizations have identified specific areas for improvement within their diversion detection programs, they can then pick the best solution for their organization.
The harm caused by diverters like the anesthetist mentioned at the top of the article can be prevented. If a hospital has systems in place to detect early warning signs of diversion behavior, patient suffering and organizational risk can be greatly reduced, if not eliminated. With AI and appropriate monitoring in place, clinical drug diversion will be detected long before so much damage is done. This is a crisis in healthcare that can be remedied and will save patient and workforce member lives.