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Evaluating Guideline-Adherent Antibiotic Use for Skin Infections Using Natural Language Processing: A Pilot Study.

James R Rudloff^1,2, Stephanie A Fritz¹, Albert Lai²

¹Department of Pediatrics.

Pediatric Emergency Care

|February 26, 2026

Summary

A new natural language processing (NLP) model accurately assesses antibiotic guideline adherence for skin infections in the emergency department (ED). This automated system shows high performance, even with incomplete patient data, aiding antibiotic stewardship.

Keywords:

MRSA antimicrobial stewardship emergency medicine natural language processing pediatric emergency medicine skin and soft tissue infections

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A Novel High-Throughput Ex Vivo Ovine Skin Wound Model for Testing Emerging Antibiotics

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Published on: September 16, 2022

Area of Science:

Medical Informatics
Computational Linguistics
Clinical Decision Support

Background:

Antibiotic resistance is a growing public health concern.
Judicious antibiotic use is crucial for effective treatment and preventing resistance.
Guideline adherence for antibiotic selection in emergency departments (EDs) can be challenging.

Purpose of the Study:

To develop and validate a natural language processing (NLP) model for assessing antibiotic guideline adherence in ED skin and soft tissue infection (SSTI) cases.
To evaluate the model's performance against manual physician review.

Main Methods:

A random forest (RF)/NLP model was developed to classify clinical narratives for methicillin-resistant Staphylococcus aureus (MRSA) versus non-MRSA antibiotic coverage.
The study included pediatric patients (1-18 years) presenting with SSTIs to the ED.
Model performance was assessed using sensitivity, specificity, and ROC curve analysis.

Main Results:

The RF model achieved an AUC of 0.99, with 97.0% sensitivity and 94.9% specificity on the training data.
On the validation set, the model demonstrated 96.6% sensitivity and 90.1% specificity.
High performance was maintained despite missing patient history in some clinical narratives.

Conclusions:

Automated analysis of clinical narratives using NLP is a feasible method for determining antibiotic guideline adherence.
The NLP model's high performance, even with incomplete data, suggests potential for widespread application in healthcare settings.
This methodology offers a sustainable approach for improving antibiotic stewardship and patient safety in emergency care.