In the realm of emergency medical services (EMS), the ability to deliver timely and accurate interventions can mean the difference between life and death. However, current EMS infrastructure is often hampered by one-to-one video streaming limitations and a lack of advanced analytics capabilities. This leaves dispatchers and EMTs to manually sift through overwhelming and often redundant information in high-stress environments. Enter TeleEMS, a groundbreaking mobile live video analytics system designed to revolutionize pre-arrival emergency medical services.
TeleEMS is a comprehensive system that integrates audio and video data into a unified decision-making pipeline, providing critical insights before EMTs even arrive on the scene. The system comprises two main components: the TeleEMS Client and the TeleEMS Server. The TeleEMS Client is versatile, running across various devices such as phones, smart glasses, and desktops. This allows it to support bystanders, EMTs en route, and 911 dispatchers, ensuring that all parties involved have access to the necessary information.
The TeleEMS Server, deployed at the edge, serves as the backbone of the system. It integrates EMS-Stream, a communication protocol that enables smooth multi-party video streaming. On top of EMS-Stream, the server hosts three real-time analytics modules. The first is EMSLlama, a domain-specialized language model designed for robust symptom extraction and normalization from audio inputs. The second module uses state-of-the-art remote photoplethysmography (rPPG) methods for heart rate estimation from video inputs. The third module, PreNet, is a multimodal multitask model that predicts EMS protocols, medication types, quantities, and procedures by fusing text and vital analytics.
The evaluation of TeleEMS has shown promising results. EMSLlama outperforms GPT-4o in exact-match accuracy, scoring 0.89 compared to 0.57. Additionally, the fusion of text and vital analytics has been shown to improve inference robustness, enabling reliable pre-arrival intervention recommendations. This demonstrates the potential of mobile live video analytics to transform EMS operations, bridging the gap between bystanders, dispatchers, and EMTs.
The implications of TeleEMS extend beyond just improving the efficiency of emergency responses. By providing a more comprehensive and accurate picture of the situation, TeleEMS can help reduce the cognitive load on EMTs and dispatchers, allowing them to make more informed decisions in high-pressure situations. This could lead to better patient outcomes and a more effective use of resources.
Moreover, the success of TeleEMS highlights the broader potential of integrating advanced analytics and AI into emergency medical services. As technology continues to evolve, we can expect to see even more sophisticated systems that can provide real-time insights and support to those on the front lines of medical emergencies. This could pave the way for a new era of intelligent EMS infrastructure, one that is better equipped to handle the challenges of modern healthcare.
