Presentation
Artificial intelligence for Emergency Medical Service
SessionDevelopment Approaches (DH5)
DescriptionEmergency medical services (EMS) are essential for providing timely and effective care to patients in various medical emergencies. However, EMS operations often involve complex and challenging environments, which can lead to Human Factors and Ergonomics issues. These issues can include:
High workload: EMS personnel are often required to perform multiple tasks simultaneously, including driving an ambulance, assessing patients, providing medical care, and communicating with other healthcare providers. This high workload can lead to fatigue, stress, and errors in judgment.
Time pressure: EMS personnel often need to make quick decisions, often under pressure from the patient or their family. This time pressure can also lead to errors.
Environmental challenges: EMS personnel may work in a variety of challenging environments, such as noisy and crowded scenes, or extreme weather conditions. These challenges can make it difficult to communicate and coordinate care effectively.
To address these issues, cognitive performance modeling has served as a traditional approach aimed at developing models of human performance in complex and challenging environments. These models can be used to identify cognitive factors that contribute to human errors and to design interventions to reduce these errors. However, the cognitive performance modeling approach has some limitations. First, it can be difficult to develop accurate models of human performance in real-world settings. Second, even when accurate models are developed, it can be difficult to design interventions that are effective in reducing human errors.
As an alternative and complementary approach, the use of AI in EMS has the potential to offer a number of benefits. First, it improves patient care. AI can help EMS personnel to make better decisions and to provide more timely and effective care to patients. For example, AI can be used to triage patients more accurately, to identify early signs of deterioration, and to consult with remote specialists. Second, it can reduce workload. AI can automate many of the tasks that EMS personnel currently perform manually, such as charting and documentation. This can help to reduce their workload and allow them to focus on providing care to patients. Third, safety can also be Improved. AI can help to reduce the risk of accidents and injuries to EMS personnel and patients. For example, AI can be used to develop collision avoidance systems for ambulances or to develop systems that can alert EMS personnel to potential hazards at the scene of an emergency.
Existing research on AI for EMS is still in its nascent stages. However, there is a growing body of evidence that suggests that AI has the potential to address many of the Human Factors and Ergonomics issues that EMS personnel face nowadays. One of the main limitations of existing research is that it is largely focused on developing and testing AI algorithms for specific tasks, such as triage or decision support. However, there is a need for more research that considers the broader implications of using AI in EMS systems from Human Factors and Ergonomics perspective .
Therefore, the objective of this research is to provide prioritized use-cases of AI for EMS.The research will be conducted using a combination of methods, including literature review, survey and interviews. Our work provides a total of fifteen use-cases of AI for EMS missions. Here are five of the most prioritized use-cases:
Triage: AI can be used to develop triage algorithms that can help EMS personnel to quickly and accurately assess the severity of a patient's condition. This can help to ensure that patients with the most urgent needs are prioritized and receive care quickly.
Resilient decision support: AI can be used to develop resilient decision support systems that can help EMS personnel to make complex decisions easily, such as choosing the best treatment for a patient or deciding where to transport a patient. This can help to improve the quality of care that patients receive with resilience.
Telemedicine: AI can be used to develop telemedicine systems that can allow EMS personnel to consult with remote specialists. This can be especially beneficial in rural areas or in areas where there is a shortage of medical specialists.
Patient monitoring: AI can be used to develop patient monitoring systems that can track a patient's vital signs and other physiological parameters. This can help EMS personnel to identify early signs of deterioration and to intervene quickly to prevent complications.
Safety: AI can be used to develop safety systems that can help to reduce the risk of accidents and injuries to EMS personnel and patients. For example, AI can be used to develop collision avoidance systems for ambulances or to develop systems that can alert EMS personnel to potential hazards at the scene of an emergency.
Triage is the most urgent use-case because it ensures that patients with the most urgent needs are prioritized and receive care quickly. Decision support with resilience is also a high-priority use-case because it helps EMS personnel to make complex decisions more effectively. Telemedicine is also critical to EMS operations in rural areas or areas where there is a shortage of medical specialists and thus remote medical service is inevitable and beneficial. Patient monitoring helps EMS personnel to identify early signs of deterioration and to intervene quickly to prevent complications. Lastly, safety could also help to reduce the risk of accidents and injuries to EMS personnel and patients.
From an AI technology point of view, all of the use-cases that were prioritized in the results section are technologically feasible. There are a number of existing AI technologies that can be used to address these use-cases. For example, machine learning algorithms can be used to develop triage algorithms and decision support systems. Natural language processing algorithms can be used to develop telemedicine systems. Computer vision algorithms can be used to develop patient monitoring systems and safety systems.
However, there are also some challenges that need to be addressed before AI can be widely implemented in EMS systems. The first challenge is data privacy and security. EMS agencies need to ensure that the data that is used to train and operate AI systems is collected and used in a way that protects the privacy and security of patients. Secondly, EMS agencies need to develop ethical guidelines for the use of AI in EMS. These guidelines should address issues such as bias, transparency, and accountability. Third, the Human-AI collaboration aspect should be considered. EMS agencies need to develop strategies for ensuring that EMS personnel are able to collaborate effectively with AI systems. This includes providing training on how to use AI systems and how to interpret their outputs.
In conclusion, the use of AI in EMS has the potential to offer a number of benefits, including improved patient care, reduced workload, improved safety, and reduced costs. However, there are also some challenges that need to be addressed before AI can be widely implemented in EMS systems. These challenges include data privacy and security, ethical considerations, and human-AI collaboration. From this study, we found prioritized use-cases of AI for EMS (e.g., Triage, Decision support). These use-cases were chosen based on their potential impact on patient care, EMS personnel, and EMS systems. All of these use-cases are technologically feasible, and there are a number of existing AI technologies that can be used to address them. EMS agencies should begin to develop plans for implementing AI in their systems. This includes identifying the use-cases that are most relevant to their needs, developing strategies for addressing the challenges, and investing in the necessary training and infrastructure.
High workload: EMS personnel are often required to perform multiple tasks simultaneously, including driving an ambulance, assessing patients, providing medical care, and communicating with other healthcare providers. This high workload can lead to fatigue, stress, and errors in judgment.
Time pressure: EMS personnel often need to make quick decisions, often under pressure from the patient or their family. This time pressure can also lead to errors.
Environmental challenges: EMS personnel may work in a variety of challenging environments, such as noisy and crowded scenes, or extreme weather conditions. These challenges can make it difficult to communicate and coordinate care effectively.
To address these issues, cognitive performance modeling has served as a traditional approach aimed at developing models of human performance in complex and challenging environments. These models can be used to identify cognitive factors that contribute to human errors and to design interventions to reduce these errors. However, the cognitive performance modeling approach has some limitations. First, it can be difficult to develop accurate models of human performance in real-world settings. Second, even when accurate models are developed, it can be difficult to design interventions that are effective in reducing human errors.
As an alternative and complementary approach, the use of AI in EMS has the potential to offer a number of benefits. First, it improves patient care. AI can help EMS personnel to make better decisions and to provide more timely and effective care to patients. For example, AI can be used to triage patients more accurately, to identify early signs of deterioration, and to consult with remote specialists. Second, it can reduce workload. AI can automate many of the tasks that EMS personnel currently perform manually, such as charting and documentation. This can help to reduce their workload and allow them to focus on providing care to patients. Third, safety can also be Improved. AI can help to reduce the risk of accidents and injuries to EMS personnel and patients. For example, AI can be used to develop collision avoidance systems for ambulances or to develop systems that can alert EMS personnel to potential hazards at the scene of an emergency.
Existing research on AI for EMS is still in its nascent stages. However, there is a growing body of evidence that suggests that AI has the potential to address many of the Human Factors and Ergonomics issues that EMS personnel face nowadays. One of the main limitations of existing research is that it is largely focused on developing and testing AI algorithms for specific tasks, such as triage or decision support. However, there is a need for more research that considers the broader implications of using AI in EMS systems from Human Factors and Ergonomics perspective .
Therefore, the objective of this research is to provide prioritized use-cases of AI for EMS.The research will be conducted using a combination of methods, including literature review, survey and interviews. Our work provides a total of fifteen use-cases of AI for EMS missions. Here are five of the most prioritized use-cases:
Triage: AI can be used to develop triage algorithms that can help EMS personnel to quickly and accurately assess the severity of a patient's condition. This can help to ensure that patients with the most urgent needs are prioritized and receive care quickly.
Resilient decision support: AI can be used to develop resilient decision support systems that can help EMS personnel to make complex decisions easily, such as choosing the best treatment for a patient or deciding where to transport a patient. This can help to improve the quality of care that patients receive with resilience.
Telemedicine: AI can be used to develop telemedicine systems that can allow EMS personnel to consult with remote specialists. This can be especially beneficial in rural areas or in areas where there is a shortage of medical specialists.
Patient monitoring: AI can be used to develop patient monitoring systems that can track a patient's vital signs and other physiological parameters. This can help EMS personnel to identify early signs of deterioration and to intervene quickly to prevent complications.
Safety: AI can be used to develop safety systems that can help to reduce the risk of accidents and injuries to EMS personnel and patients. For example, AI can be used to develop collision avoidance systems for ambulances or to develop systems that can alert EMS personnel to potential hazards at the scene of an emergency.
Triage is the most urgent use-case because it ensures that patients with the most urgent needs are prioritized and receive care quickly. Decision support with resilience is also a high-priority use-case because it helps EMS personnel to make complex decisions more effectively. Telemedicine is also critical to EMS operations in rural areas or areas where there is a shortage of medical specialists and thus remote medical service is inevitable and beneficial. Patient monitoring helps EMS personnel to identify early signs of deterioration and to intervene quickly to prevent complications. Lastly, safety could also help to reduce the risk of accidents and injuries to EMS personnel and patients.
From an AI technology point of view, all of the use-cases that were prioritized in the results section are technologically feasible. There are a number of existing AI technologies that can be used to address these use-cases. For example, machine learning algorithms can be used to develop triage algorithms and decision support systems. Natural language processing algorithms can be used to develop telemedicine systems. Computer vision algorithms can be used to develop patient monitoring systems and safety systems.
However, there are also some challenges that need to be addressed before AI can be widely implemented in EMS systems. The first challenge is data privacy and security. EMS agencies need to ensure that the data that is used to train and operate AI systems is collected and used in a way that protects the privacy and security of patients. Secondly, EMS agencies need to develop ethical guidelines for the use of AI in EMS. These guidelines should address issues such as bias, transparency, and accountability. Third, the Human-AI collaboration aspect should be considered. EMS agencies need to develop strategies for ensuring that EMS personnel are able to collaborate effectively with AI systems. This includes providing training on how to use AI systems and how to interpret their outputs.
In conclusion, the use of AI in EMS has the potential to offer a number of benefits, including improved patient care, reduced workload, improved safety, and reduced costs. However, there are also some challenges that need to be addressed before AI can be widely implemented in EMS systems. These challenges include data privacy and security, ethical considerations, and human-AI collaboration. From this study, we found prioritized use-cases of AI for EMS (e.g., Triage, Decision support). These use-cases were chosen based on their potential impact on patient care, EMS personnel, and EMS systems. All of these use-cases are technologically feasible, and there are a number of existing AI technologies that can be used to address them. EMS agencies should begin to develop plans for implementing AI in their systems. This includes identifying the use-cases that are most relevant to their needs, developing strategies for addressing the challenges, and investing in the necessary training and infrastructure.
Event Type
Oral Presentations
TimeTuesday, March 2610:30am - 11:00am CDT
LocationSalon A-2
Digital Health