Erica S Shenoy, Westyn Branch-Elliman, Jenna Wiens. The Search for the Golden Egg: Applied Large Language Models to Detect Patients With Potential Exposure to Avian Influenza. Clinical Infectious Diseases, 2025;, ciaf371
Influenza A(H5N1) viruses continue to circulate in birds, dairy cows, and other animals across the United States, with 70 human cases detected durng the 2024-2025 outbreak; most identified human cases are mild, with 1 death reported. Detections in animals are orders of magnitudes greater, with close to 200 million poultry affected in 51 jurisdictions and more than 1000 dairy herds impacted in 17 states. Most human cases have been mild and have been linked to exposure to dairy herds (41/70) or poultry farms and culling operations (24/70) [1–3]. The national surveillance strategy has included targeted testing (eg, H5 testing in individuals with risk factors and compatible symptoms) and public health laboratory monitoring, which includes H5 testing as well as monitoring of clinical laboratory trends, syndromic surveillance in emergency departments (EDs), and wastewater surveillance, prior to declaration of the end of the multistate outbreak in July 2025.
For healthcare facilities, identifying patients with risk factors for H5 represents an ongoing challenge. Early identification of patients presenting with symptoms and risk factors for H5 exposure is necessary to appropriately isolate at-risk individuals to mitigate risk of onward transmission and facilitate patient evaluation, as part of the “identify-isolate-inform” model for emerging/reemerging infectious diseases, including high-consequence infectious diseases (HCIDs) [4, 5]. The Centers for Disease Control and Prevention has provided guidance on risk factors for many of these pathogens, including H5; however, eliciting this information routinely and then acting upon it in the real world has proven challenging. Given that the types of exposures for different HCIDs are pathogen-specific and clinicians are already experiencing high levels of burnout, it may be impractical to ask every patient with respiratory symptoms or conjunctivitis about specific exposures to birds, poultry, dairy cows, or related products and to document it, not to mention specific exposure-symptom combinations for other HCIDs. This is especially burdensome if the putative exposures and infections are rare.
For healthcare facilities, identifying patients with risk factors for H5 represents an ongoing challenge. Early identification of patients presenting with symptoms and risk factors for H5 exposure is necessary to appropriately isolate at-risk individuals to mitigate risk of onward transmission and facilitate patient evaluation, as part of the “identify-isolate-inform” model for emerging/reemerging infectious diseases, including high-consequence infectious diseases (HCIDs) [4, 5]. The Centers for Disease Control and Prevention has provided guidance on risk factors for many of these pathogens, including H5; however, eliciting this information routinely and then acting upon it in the real world has proven challenging. Given that the types of exposures for different HCIDs are pathogen-specific and clinicians are already experiencing high levels of burnout, it may be impractical to ask every patient with respiratory symptoms or conjunctivitis about specific exposures to birds, poultry, dairy cows, or related products and to document it, not to mention specific exposure-symptom combinations for other HCIDs. This is especially burdensome if the putative exposures and infections are rare.
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