Enhancing the effectiveness of emergency department computed tomography scans using pre-trained language models

Objective

This study aims to develop a predictive model using a medical clinical assistive large language model to determine the necessity of computed tomography (CT) scans in emergency department settings based solely on data collected at triage. The model seeks to improve patient flow and more efficiently allocate limited medical resources while reducing unnecessary radiation exposure.

Methods

The model uses data collected from emergency department triage and includes patient symptoms, chief complaints, vital signs and medical history, without the need for physiological test data.

Results

This study analyzed 165,391 emergency department records from Shuang Ho Hospital of Taipei Medical University and used a large language model to develop a model for predicting whether a patient should undergo a CT scan. While initial results indicate that detailed symptom descriptions and severity of pain assessments can enhance prediction accuracy, our approach centers on data preprocessing, the integration of unstructured data, and external features. In our final performance comparison, the model developed using a large language model exhibited the best performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.88 and an area under the precision-recall curve (AUPRC) of 0.5414. This represents a 0.6 % improvement over existing language models and a 4.8 % improvement over traditional machine learning approaches. It is notable that the model achieved a high negative predictive value of 0.9261, indicating strong reliability in identifying patients who don't require CT scans. This model will allow physicians to better understand the overall health status of patients and provide earlier diagnostic and treatment recommendations based on comprehensive model information, which will ultimately lead to better patient care.

Conclusion

This research establishes foundational work for future studies that aim at optimizing emergency diagnostic processes and enhancing patient care through improved medical predictions. However, expanding the dataset’s diversity and pursuing external validations are essential to improve the predictive accuracy and applicability of the model in a variety of emergency department settings.