Smart Health Software to Support Rescue Personnel in Emergency Situations

TL;DR

This paper presents a user-centered AI-driven rescue health management software that rapidly assesses patient health to support emergency responders, improving response efficiency and accuracy.

Contribution

It introduces a novel rescue health software utilizing pre-trained machine learning models tailored for emergency scenarios, emphasizing user-centered design principles.

Findings

Improved assessment speed for emergency health conditions

Enhanced decision support for rescue personnel

Validated through three practical use cases

Abstract

Rescue stations around the world receive millions of emergency rescue calls each year, most of which are due to health complications. Due to the high frequency and necessity of rescue services, there is always an increasing demand for quick, accurate, and coordinated responses from rescue personnel to save lives and mitigate damage. This paper introduces a rescue health management software solution designed to improve the efficiency and effectiveness of rescue situational awareness by rapidly assessing the health status of emergency patients using AI-driven decision support systems. The novelty in this software approach is it's user-centered design principles to ensure that its solutions are specifically tailored to meet the unique requirements of emergency responders. It used pre-trained machine learning models with rescue data and accepted new patient's input data to provide a probability of the major health complications so that rescue personnel can expedite treatment plan following the outcome. The paper focuses primarily on the software development and implementation steps with three use cases, while also providing a short overview of the previous machine learning-based development phases.