ArticleNPJ digital medicine2025
LLM enabled classification of patient self-reported symptoms and needs in health systems across the USA.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Authentication status and AI triage concordance among care seekers in a US health system.npj health systems · 2026Article
- Linear accelerator (linac) downtime analysis assisted with a Large Language Model (LLM).Journal of applied clinical medical physics · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
US health systems receive up to 200 M monthly website visitors. Connecting patient searches to the appropriate workflow requires accurate classification. A dataset of searches on ~15 US health system websites was annotated, characterized, and used to train and evaluate a multi-label, multi-class, deep neural network. This classifier was deployed to health systems touching patients in all 50 states and compared to an LLM. The training dataset contained 504 unique classes with performance of the model in classifying searches among those classes ranging from ~0.90 to ~0.70 across metrics depending on the number of classes included. GPT-4 performed similarly if given a master list and demonstrated value in providing added coverage to augment the supervised classifier's performance. The collected data revealed characteristics of patient searches in the largest, multi-center, national study of US health systems to date.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.