AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal

Emergency care-sensitive conditions (ECSCs) require rapid identification and treatment and are responsible for over half of all deaths worldwide. Prehospital emergency care (PEC) can provide rapid treatment and access to definitive care for many ECSCs and can reduce mortality in several different settings. The objective of this study is to propose a method for using artificial intelligence (AI) and machine learning (ML) to transcribe audio, extract, and classify unstructured emergency call data in the Serviço de Atendimento Móvel de Urgência (SAMU) system in southern Brazil. The study used all “1-9-2” calls received in 2019 by the SAMU Novo Norte Emergency Regulation Center (ERC) call center in Maringá, in the Brazilian state of Paraná. The calls were processed through a pipeline using machine learning algorithms, including Automatic Speech Recognition (ASR) models for transcription of audio calls in Portuguese, and a Natural Language Understanding (NLU) classification model. The pipeline was trained and validated using a dataset of labeled calls, which were manually classified by medical students using LabelStudio. The results showed that the AI model was able to accurately transcribe the audio with a Word Error Rate of 42.12% using Wav2Vec 2.0 for ASR transcription of audio calls in Portuguese. Additionally, the NLU classification model had an accuracy of 73.9% in classifying the calls into different categories in a validation subset. The study found that using AI to categorize emergency calls in low- and middle-income countries is largely unexplored, and the applicability of conventional open-source ML models trained on English language datasets is unclear for non-English speaking countries. The study concludes that AI can be used to transcribe audio and extract and classify unstructured emergency call data in an emergency system in southern Brazil as an initial step towards developing a decision-making support tool.Author summary: In our study, we utilized artificial intelligence (AI) and machine learning (ML) to help process emergency call data in the Serviço de Atendimento Móvel de Urgência (SAMU) system in southern Brazil. These calls, often concerning emergency care-sensitive conditions (ECSCs), require quick identification and treatment. Our aim was to transcribe, extract, and categorize unstructured call data to improve response times and, ultimately, patient outcomes. Leveraging AI, we transcribed calls using Automatic Speech Recognition and then categorized them using a Natural Language Understanding model. The AI was successful in accurately transcribing the audio and categorizing the calls with high accuracy. However, we also discovered that using AI in this manner in non-English speaking countries is largely uncharted territory. Our findings suggest that AI could be a powerful tool in improving emergency care responses, especially in low- and middle-income countries, and this is an exciting step towards creating decision-support tools for these critical situations.