The Fire and Disaster Management Agency will introduce an artificial intelligence-powered system to automatically transcribe emergency calls with the aim of streamlining record-keeping and easing the workload on staff amid rising ambulance dispatches. The Fire and Disaster Management Agency plans to launch a model project in fiscal 2027 to utilize artificial intelligence in the handling of 119 emergency calls for firefighting, rescue and ambulance services. The agency will introduce an AI-powered speech recognition system that automatically transcribes emergency calls with the aim of streamlining the process of recording call details and easing the burden on staff amid a growing number of ambulance dispatches. Related expenses will be included in the agency's fiscal 2027 budget request. The project will utilize so-called "vertical AI," which specializes in specific fields. The AI system will also be equipped with a function that advises staff on necessary actions according to the content of each call.

Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics.