
EMS explores AI and IT tools for improved patient care. The University of Pittsburgh develops a machine-learning tool to classify cardiac events using ECGs. Meanwhile, Hardin County partners with MiPi Support for IT assistance in optimizing mobile incident alert systems. Integrating these technologies can enhance EMS efficiency and response times, but ethical and legal considerations must be addressed before widespread adoption.
Emergency medical services (EMS) and first response teams play a crucial role in providing life-saving healthcare during emergencies. Many EMS organizations are looking into integrating artificial intelligence (AI) and health IT solutions into their workflows to enhance the efficiency of emergency care and improve patient outcomes.
However, the successful implementation of these technologies depends on various factors such as the type of EMS agency, available funding, workforce size and composition, resources, and data access. Organizations must carefully identify appropriate use cases based on their unique circumstances, as the 2020 National Emergency Medical Services Assessment outlines.
Two significant challenges in EMS that AI and IT tools can address are optimizing mobile incident management software uptime and stability and enhancing patient triage.
Machine Learning for Cardiac Event Identification
The EMS department at the University of Pittsburgh Medical Center (UPMC) is taking an innovative approach to streamline workflows and improve patient outcomes. They have developed a machine-learning (ML) tool that uses electrocardiograms (ECG/EKG) to classify cardiac events.
Traditionally, EMS personnel and medical staff use classification systems like the History, ECG, Age, Risk Factors, and Troponin (HEART) score to assess chest pain patients’ risk. However, accurately identifying serious cardiac events, like heart attacks, can be challenging, especially when dealing with unclear ECGs. ML enables the analysis of numerous ECG features simultaneously, providing a more comprehensive view of a patient’s heart health.
The ML tool developed by UPMC’s team can examine nearly 700 features within ECGs, helping EMS teams identify conditions like cardiac ischemia or blockages. This tool complements human interpretation of ECGs by identifying subtle but critical data features that may not be easily noticeable. It aims to direct users’ attention to abnormal aspects of the ECG, assisting in quicker and more accurate triaging.
Leveraging IT Support for Faster EMS Response
First responders often face challenges when using mobile incident alert and management software, primarily when relying on cellular networks. These networks may have failure points and choke points, affecting the effectiveness of these technologies.
The Hardin County, Iowa Emergency Management Agency collaborates with IT support company MiPi Support to address this. This partnership ensures that EMS professionals have assistance in setting up and maintaining their mobile incident alert systems. MiPi Support’s comprehensive understanding of first responder organizations and their needs helps improve dispatch communication and reduces system downtime.
The company’s software routes dispatch directly to first responders’ phones, bridging the communication gap caused by using radios and pagers. Additionally, their monitoring software detects potential issues early, allowing for timely intervention and minimal downtime.
Overall, the use cases for AI and IT tools in EMS are diverse and promising. From ML-based cardiac event identification to IT support for faster response, these technologies offer significant potential to enhance EMS efficiency and patient care. However, it’s essential to address ethical and legal considerations and follow industry best practices before widespread adoption across health systems and EMS organizations can be achieved.