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Jordan MacLachlan, PhD candidate in Computer Science at Victoria University of Wellington, is using machine learning to improve ambulance response times. The integration of machine learning into emergency services is revolutionary. By processing vast amounts of real-time data, these algorithms offer sophisticated analysis and predictions that enhance response efficiency. For instance, by assessing traffic patterns, the system can reroute ambulances to avoid delays, saving precious minutes.
The system aggregates data from various sources, including GPS signals, traffic cameras, weather reports, and historical call logs. This comprehensive data collection allows the machine learning algorithms to make informed decisions rapidly. The system evaluates factors such as the severity of the emergency, the time of day, current traffic conditions, and even weather impacts to dispatch the most suitable ambulance.
One of the key features of Jordan’s model is its predictive analytics capability. The system can predict peak times for emergencies in specific areas by analysing patterns and trends in emergency calls. This foresight enables emergency services to position ambulances strategically, reducing the time needed to reach patients.
The machine learning models are not static; they continually learn and adapt. As new data is fed into the system, it refines its predictions and recommendations. This ongoing learning process ensures that the models become increasingly accurate and effective. Jordan’s system also includes feedback loops, where the outcomes of dispatched ambulances are analysed to improve decision-making processes further.
Jordan’s journey into this research was fueled by personal experience. “I was unwell for several years and often required an ambulance,” he recalls. “Our trips to the Emergency Department were so frequent that the ambulance crew suggested my sister become a paramedic, given her ability to handle stress. Investing considerable time and effort into a widely ignored problem was an effective way I could give back, and if possible, make my sister’s job a little easier.”
Jordan’s work highlights the critical role of digital technology in enhancing emergency medical services. By harnessing the power of machine learning and data analytics, his models promise to revolutionise how ambulances are dispatched, ultimately improving patient outcomes and making the best use of resources in an era where every second counts. Jordan’s innovative approach could be the key to saving more lives.
After earning his PhD, Jordan will focus on bringing his findings to the commercial market. He aims to deliver an improved ambulance dispatch system to services like Wellington Free Ambulance.
Looking ahead, Jordan aims to integrate additional technologies, such as IoT devices and telematics, to enhance the system’s capabilities further. Connecting ambulances, hospitals, and traffic management systems can create a more cohesive and responsive emergency network. This interconnected approach would allow for real-time communication and coordination, optimising all parts of the emergency response chain. The ultimate goal is a global improvement in emergency response times, potentially saving millions of lives each year.
Jordan’s use of digital technology in emergency medical services represents a significant leap forward in healthcare innovation. His models, driven by machine learning and data analytics, offer a promising solution to improving ambulance response times. His project underscores the transformative potential of digital technology in healthcare. By refining and expanding his models, Jordan envisions a future where emergency services worldwide can respond faster and more effectively, saving countless lives. As the demand for emergency services continues to grow, innovations like Jordan’s are crucial in meeting this challenge and ensuring that help arrives when needed.
