BACKGROUND
Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM.


OBJECTIVE
Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field.


METHODS
Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data.


RESULTS
A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills.


CONCLUSIONS
LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

From Triage to Treatment: Scoping the Role of Large Language Models in Transforming Emergency Medicine (Preprint)

<sec> <title>BACKGROUND</title> The digitization of healthcare, facilitated by the adoption of electronic health record (EHR) systems, has revolutionized data-driven medical research and patient care. While this digital transformation offers substantial benefits in healthcare efficiency and accessibility, it concurrently raises significant concerns over privacy and data security. Initially, the journey towards protecting patient data de-identification saw the transition from rule-based systems to more mixed approaches including machine learning for de-identifying patient data. Subsequently, the emergence of Large Language Models (LLMs) has represented a further opportunity in this domain, offering unparalleled potential for enhancing the accuracy of context-sensitive de-identification. However, despite LLMs offering significant potential, the deployment of the most advanced models in hospital environments is frequently hindered by data security issues and the extensive hardware resources required. </sec> <sec> <title>OBJECTIVE</title> The objective of our study is to design, implement, and evaluate de-identification algorithms by employing fine-tuning of moderate-sized open-source language models, ensuring their suitability for production inference tasks on personal computers. </sec> <sec> <title>METHODS</title> We aimed at replacing personal identifying information (PII) with generic placeholders or labeling non-PII texts as 'ANONYMOUS', ensuring privacy while preserving textual integrity. Our dataset, derived from over 425,000 clinical notes from the adult emergency department of the Bordeaux University Hospital in France, underwent independent double annotation by two experts to create a reference for model validation with 3,000 clinical notes randomly selected. Three open-source language models of manageable size were selected for their feasibility in hospital settings: Llama 2 7B, Mistral 7B, and Mixtral 8x7B. Fine-tuning utilized the quantized Low-Rank Adaptation (qLoRA) technique. Evaluation focused on PII-level (Recall, Precision and F1-Score) and clinical note-level metrics (Recall and BLEU metric), assessing de-identification effectiveness and content preservation. </sec> <sec> <title>RESULTS</title> The generative model Mistral 7B demonstrated the highest performance with an overall F1-score of 0.9673 (vs. 0.8750 for Llama 2 and 0.8686 for Mistral 8x7B). At the clinical notes level, the same model achieved an overall recall of 0.9326 (vs. 0.6888 for Llama 2 and 0.6417 for Mistral 8x7B).This rate increased to 0.9915 for the anonymization of names with Mistral 7B. Four notes out of the total 3000 failed to be fully anonymized for names: in one case, the non-anonymized name belonged to a patient, while in the other cases, it belonged to medical staff. Beyond the fifth epoch, the BLEU score consistently exceeded 0.9864, indicating no significant text alteration due to the process. </sec> <sec> <title>CONCLUSIONS</title> Our research underscores the significant capabilities of generative NLP models, with Mistral 7B standing out for its superior ability to de-identify clinical texts efficiently. Achieving notable performance metrics, Mistral 7B operates effectively without requiring high-end computational resources. These methods pave the way for a broader availability of anonymized clinical texts, enabling their use for research purposes and the optimization of the healthcare system. </sec> 

pdf/harnessing-moderate-sized-language-models-for-reliable-3ov0ui6ugv.pdf

Harnessing Moderate-Sized Language Models for Reliable Patient Data De-identification in Emergency Department Records: An Evaluation of Strategies and Performance (Preprint)

Emergency Rooms (ERs) are at the center of various optimization research due to the growing number of visits in recent decades. The accurate logging of patient movement and time spent in ERs is essential for studying patient pathways and improving operations. Despite the innovative digital tracking system employed at the Bordeaux University Hospital, over 90% of these logs suffer from missing or incoherent values, primarily due to manual entry errors and software changes. This study explores the application of Transformer neural networks for cleaning ER logs at the Bordeaux University Hospital, addressing the challenge of inaccuracies in patient movement records accumulated over ten years. By leveraging a T5 Transformer model, we aim to demonstrate the model’s ability to correct these errors by transforming unreliable datasets into valuable resources for optimizing ER operations. Our findings reveal that the Transformer model can achieve an overall accuracy of 95.79% in identifying and correcting faulty entries, showcasing the potential of advanced machine learning techniques in enhancing data integrity for healthcare applications. This research highlights the effectiveness of Transformer networks in data cleaning tasks and opens up opportunities for their application in various fields that are burdened with corrupt sequential data.

Application of Transformer Neural Networks for Data Cleaning in Emergency Room Logs: A Case Study from the Bordeaux University Hospital.

Abstract According to the Food and Drug Administration’s definition, natural history studies (NHS) are observational studies that collect data on the course of a disease from onset to resolution or death, in the absence of an intervention. NHS plays a critical role in rare disease research to support clinical development objectives, including estimating disease prevalence, identifying diagnostic biomarkers. In this study, we present a machine learning based approach for systematically identifying NHS from PubMed as a foundational step toward large-scale NHS analysis for drug development in rare diseases. We explored a manually curated NHS corpus to develop machine learning and deep learning predictive models for NHS classification. We evaluated both binary and multiclass classifications and found that models trained specifically for binary classification (distinguishing NHS-relevant from NHS-irrelevant studies) achieved substantially higher performance than those trained to distinguish among four NHS-related categories, namely, Unrelated , Irrelevant , Secondary , and Primary . Among all models tested, the pretrained PubMedBERT-base-uncased-abstract model achieved the best performance (precision = 0.8171, recall = 0.8079, F1 score = 0.8125, AUCPR = 0.8768). These results highlight the feasibility of automated NHS identification using deep learning and underscore the effectiveness of binary classification as an initial approach. This work demonstrates potential to accelerate NHS data collection and lays the foundation for NHS analysis to enhance our understanding of disease progression in rare diseases, and may extend to common diseases as well. 

pdf/machine-learning-based-identification-of-natural-history-321chba2a3zr.pdf

Machine learning-based identification of natural history studies in rare diseases: a step toward understanding disease development and outcome


 
 Public health surveillance relies on the collection of data, often in near-real time. Recent advances in natural language processing make it possible to envisage an automated system for extracting information from electronic health records.
 
 
 
 To study the feasibility of setting up a national trauma observatory in France, we compared the performance of several automatic language processing methods in a multiclass classification task of unstructured clinical notes.
 
 
 
 A total of 69,110 free-text clinical notes related to visits to the emergency departments of the University Hospital of Bordeaux, France, between 2012 and 2019 were manually annotated. Among these clinical notes, 32.5% (22,481/69,110) were traumas. We trained 4 transformer models (deep learning models that encompass attention mechanism) and compared them with the term frequency–inverse document frequency associated with the support vector machine method.
 
 
 
 The transformer models consistently performed better than the term frequency–inverse document frequency and a support vector machine. Among the transformers, the GPTanam model pretrained with a French corpus with an additional autosupervised learning step on 306,368 unlabeled clinical notes showed the best performance with a micro F1-score of 0.969.
 
 
 
 The transformers proved efficient at the multiclass classification of narrative and medical data. Further steps for improvement should focus on the expansion of abbreviations and multioutput multiclass classification.


pdf/deep-learning-transformer-models-for-building-a-6p3ttrfs.pdf

Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study

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Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study