Objective: To develop and evaluate an internally validated natural language processing (NLP) model to determine guideline adherence of antibiotic decisions in the emergency department (ED) for skin and soft tissue infections (SSTIs). Methods This cross-sectional pilot study developed and applied a random forest (RF)/NLP model to classify clinical narratives of patients with skin infections as requiring either methicillin-resistant Staphylococcus aureus (MRSA) or non-MRSA antibiotic coverage. Conducted at a quaternary care children’s hospital with an annual volume of 50,000 ED visits, our study included patients aged 1 to 18 years presenting for SSTIs to the ED from July 1, 2018, through June 30, 2023. Main outcomes included the NLP model’s sensitivity, specificity, and receiver operator characteristic (ROC) curve compared to gold standard manual physician review of the medical records. Results: A total of 1588 patients were part of the training data set, with an additional 423 patients utilized for validation. The RF model achieved an area under the curve (AUC) of 0.99, 97.0% sensitivity, and 94.9% specificity. In the validation data set, the model achieved 96.6% sensitivity and 90.1% specificity. Performance remained strong despite absent/missing patient history in certain narratives. Conclusions: The NLP model demonstrated that automated analysis of clinical narratives for determining guideline adherence is feasible. Despite missing data in narratives, the model’s high performance suggests potential for broader application. Given the rise in antibiotic-resistant infections and the role of judicious antibiotic use, developing automated systems through NLP can significantly contribute to health care delivery and patient safety. The methodology of this study provides a feasible, sustainable path for similar applications in the emergency setting.
Rudloff et al. (Fri,) studied this question.