Closing surveillance detection gaps for emerging digital drug discourse with ontology-enhanced large language models - Nature
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IntroductionThe age-adjusted rate of drug-involved deaths in the US was ~250% higher than two decades ago1, despite a 24% reduction in overdose-related fatalities between 2023 and 20242. Adverse outcomes from overdoses extend beyond fatalities to other forms of measurable harm, including increased strain on the medical system. In 2024 alone the Drug Abuse Warning Network (DAWN) reported more than 8.4 million nonfatal drug-related emergency department (ED) visits in the US3. Meanwhile, the National Drug Early Warning System (NDEWS) identified parallel social media conversations around emerging drugs separately linked to increased ED visits in 2025, including synthetic opioids (e.g., nitazenes), opioid-like compounds (e.g., kratom), and polydrug-use combinations (e.g., benzodiazepines, kratom, or ketamine combined with fentanyl)4. Proactively addressing drug harm requires clinicians, health professionals, and policymakers to understand contemporary and evolving drug use trends as they emerge. Yet traditional epidemiologic surveillance systems (e.g., DAWN and others) contain built-in lags ranging from months to years5.
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