Emergency Medical Dispatch Centres (EMDC) in France are facing a growing number of calls, each requiring a rapid and efficient response, leaving no time for emergency medical dispatchers to code case files. However, this coding is essential for improving patient care through epidemiological analysis and, ultimately, for developing a decision support system. This study explores the use and deployment of natural language processing (NLP) models to automatically code records in French, based on the notes taken by call takers during emergency calls.

Our work investigates fine-tuned biomedical pre-trained encoder models models for classifying highly noisy medical records, demonstrating that these models can successfully accomplish this task with a high F1 score (94\%) without requiring more computationally expensive language models. To ensure the long-term reliability of the models, validation methods specifically addressing the challenges posed by EMDC data were developed. NLP can automate the coding of EMDC call records with a high degree of accuracy. However, the models currently rely solely on the written notes in the files. Future work incorporating call transcripts could further enhance their accuracy.