An Emergency Medical Services Clinical Audit System driven by Named Entity Recognition from Deep Learning

TL;DR

This paper introduces an automatic EMS clinical audit system utilizing deep learning-based named entity recognition to efficiently analyze unstructured ambulance reports, significantly reducing manual effort and improving accuracy.

Contribution

The study develops a high-accuracy, lightweight NER model for EMS reports using weakly-supervised training, enhancing clinical audit efficiency and research capabilities.

Findings

Achieved F1 scores of around 0.981 for entity matching.

BiLSTM-CRF model is faster and lighter than BERT-based models.

System reliably identifies clinical entities from free-text reports.

Abstract

Clinical performance audits are routinely performed in Emergency Medical Services (EMS) to ensure adherence to treatment protocols, to identify individual areas of weakness for remediation, and to discover systemic deficiencies to guide the development of the training syllabus. At present, these audits are performed by manual chart review which is time-consuming and laborious. In this paper, we present an automatic audit system based on both the structured and unstructured ambulance case records and clinical notes with a deep neural network-based named entities recognition model. The dataset used in this study contained 58,898 unlabelled ambulance incidents encountered by the Singapore Civil Defence Force from 1st April 2019 to 30th June 2019. A weakly-supervised training approach was adopted to label the sentences. Later on, we trained three different models to perform the NER task. All three models achieve F1 scores of around 0.981 under entity type matching evaluation and around 0.976 under strict evaluation, while the BiLSTM-CRF model is 1~2 orders of magnitude lighter and faster than our BERT-based models. Overall, our approach yielded a named entity recognition model that could reliably identify clinical entities from unstructured paramedic free-text reports. Our proposed system may improve the efficiency of clinical performance audits and can also help with EMS database research.