Presentation
Empathy Training in First Responders Using Immersive XR Simulation
DescriptionIntroduction
In the post-COVID-19 era, burnout, mental health and overall wellbeing of healthcare workers have become more prevalent concerns. Empathy has been singled out as a key quality in successful crisis management. This project explores the innovative application of XR (Extended Reality, including virtual reality, augmented reality, and mixed reality) and AI (artificial intelligence) technologies to train empathy in first responders. First responders such as police officers, firefighters, and Emergency Medical Technicians (EMT) regularly encounter high-pressure, emotionally charged situations that demand not only technical expertise but also a high degree of emotional intelligence. In such environments, the ability to demonstrate empathy can be crucial in delivering patient-centered care and improving communication with individuals in distress. We aim to leverage immersive simulations to enhance empathy through a structured training framework.
Methods
A literature review was conducted to define the construct of empathy and its properties, identifying existing models of empathy across various application domains. The five-factor empathy model by Gerdes et al that led to the development of the Empathy Assessment Index (EAI) was selected to frame the problem space for clinical empathy. The five factors are: affective response (AR), perspective-taking (PT), self-other awareness (SA), emotion regulation (ER), and empathic attitudes (EA).
In parallel, a preliminary ontology was developed to describe an interaction between an EMT and a scared and severely injured patient. The ontology defines key entities in the interaction, such as the EMT, the patient, and their respective emotional states, and describes the relationship between them—such as how the EMT responds to the patient’s emotional cues and manages their own emotional state. We utilized common ontology development procedures to build an initial framework for an empathy-driven ontology for EMTs. Using Protege, a popular ontology development tool, we developed and visualized the ontology. Classes were established based on situational factors and clinical functions that occur between a patient and healthcare professional, within emergency response settings. Initial properties highlight basic relationships between the large classes in our class hierarchy. Additional steps for ontology development include incorporating facets and increasing slots (properties) as the scope of each class is further defined. Instances of each class were also created for demonstration purposes.
This structured framework allows us to map each of the five factors from the empathy model to specific actions and behaviors during the interaction. For example, the ontology tracks how well the physician mirrors the patient’s emotional state, such as fear or distress (AR), understands the patient’s perspective (PT), differentiates their emotional state from the patient’s, remains emotionally grounded (SA), regulates their emotions in the face of distress (ER), and translates empathetic understanding into real-world behaviors (EA).
Results
The core of this on-going research lies in the application of a five-factor empathy model to measure empathy in clinical interactions. The five factors are key to understanding the complex emotional and cognitive processes involved in empathy, making the model a robust framework for assessing and enhancing empathy in training and real-world settings. We measure empathy levels at two key points: first as a baseline before the training, and again post-training to assess the impact of the simulations.
This ontology-driven approach allows first responders to engage in immersive, scenario-based training that reflects real-world emotional challenges. By simulating realistic emergency scenarios, the training allows participants to navigate these five empathy factors in a controlled, adaptive environment.
We will present results of pilot studies, demonstrating how the five-factor empathy model can be used to design simulation scenarios that include all five factors in the empathy model, while the empathy ontology helps define the measurable outcomes along each factor. It is expected that results will show an increase in learners’ empathy level along some, but not all, of these factors, emphasizing the tension between work efficiency/competency and empathic communication.
Takeaway Points:
1. A novel XR simulation for empathy training in first responders, structured around a five-factor empathy model.
2. The five-factor model (affective response, perspective taking, self-awareness, emotion regulation, and empathic attitudes) allows for precise measurement and improvement of empathy in both cognitive and emotional aspects.
3. Immersive, scenario-based training can potentially impact patient care in critical situations thanks to empathy.
In the post-COVID-19 era, burnout, mental health and overall wellbeing of healthcare workers have become more prevalent concerns. Empathy has been singled out as a key quality in successful crisis management. This project explores the innovative application of XR (Extended Reality, including virtual reality, augmented reality, and mixed reality) and AI (artificial intelligence) technologies to train empathy in first responders. First responders such as police officers, firefighters, and Emergency Medical Technicians (EMT) regularly encounter high-pressure, emotionally charged situations that demand not only technical expertise but also a high degree of emotional intelligence. In such environments, the ability to demonstrate empathy can be crucial in delivering patient-centered care and improving communication with individuals in distress. We aim to leverage immersive simulations to enhance empathy through a structured training framework.
Methods
A literature review was conducted to define the construct of empathy and its properties, identifying existing models of empathy across various application domains. The five-factor empathy model by Gerdes et al that led to the development of the Empathy Assessment Index (EAI) was selected to frame the problem space for clinical empathy. The five factors are: affective response (AR), perspective-taking (PT), self-other awareness (SA), emotion regulation (ER), and empathic attitudes (EA).
In parallel, a preliminary ontology was developed to describe an interaction between an EMT and a scared and severely injured patient. The ontology defines key entities in the interaction, such as the EMT, the patient, and their respective emotional states, and describes the relationship between them—such as how the EMT responds to the patient’s emotional cues and manages their own emotional state. We utilized common ontology development procedures to build an initial framework for an empathy-driven ontology for EMTs. Using Protege, a popular ontology development tool, we developed and visualized the ontology. Classes were established based on situational factors and clinical functions that occur between a patient and healthcare professional, within emergency response settings. Initial properties highlight basic relationships between the large classes in our class hierarchy. Additional steps for ontology development include incorporating facets and increasing slots (properties) as the scope of each class is further defined. Instances of each class were also created for demonstration purposes.
This structured framework allows us to map each of the five factors from the empathy model to specific actions and behaviors during the interaction. For example, the ontology tracks how well the physician mirrors the patient’s emotional state, such as fear or distress (AR), understands the patient’s perspective (PT), differentiates their emotional state from the patient’s, remains emotionally grounded (SA), regulates their emotions in the face of distress (ER), and translates empathetic understanding into real-world behaviors (EA).
Results
The core of this on-going research lies in the application of a five-factor empathy model to measure empathy in clinical interactions. The five factors are key to understanding the complex emotional and cognitive processes involved in empathy, making the model a robust framework for assessing and enhancing empathy in training and real-world settings. We measure empathy levels at two key points: first as a baseline before the training, and again post-training to assess the impact of the simulations.
This ontology-driven approach allows first responders to engage in immersive, scenario-based training that reflects real-world emotional challenges. By simulating realistic emergency scenarios, the training allows participants to navigate these five empathy factors in a controlled, adaptive environment.
We will present results of pilot studies, demonstrating how the five-factor empathy model can be used to design simulation scenarios that include all five factors in the empathy model, while the empathy ontology helps define the measurable outcomes along each factor. It is expected that results will show an increase in learners’ empathy level along some, but not all, of these factors, emphasizing the tension between work efficiency/competency and empathic communication.
Takeaway Points:
1. A novel XR simulation for empathy training in first responders, structured around a five-factor empathy model.
2. The five-factor model (affective response, perspective taking, self-awareness, emotion regulation, and empathic attitudes) allows for precise measurement and improvement of empathy in both cognitive and emotional aspects.
3. Immersive, scenario-based training can potentially impact patient care in critical situations thanks to empathy.
Authors
Event Type
Oral Presentations
TimeWednesday, April 210:30am - 11:00am EDT
LocationPier 9
Simulation and Education (SE)