  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.
