Presentation
Leveraging Behavioral and Biological Stress Response Instrumentation to Evaluate Healthcare Professional and Pre-Professional Perceptions of Complex Clinical Events
DescriptionIntroduction:
Clinical simulation is increasingly recognized as a vital component of healthcare education, offering an immersive and controlled environment for providing continuing education to healthcare professionals and training for pre-professionals (Cant & Cooper, 2017). These simulations enable learners to engage with complex clinical scenarios in low-stakes settings, facilitating the development of critical skills such as sustained attention, stress management, and clinical problem solving. As demands on healthcare professionals increase, clinical simulations can also be used as a tool to understand the interaction between behavioral, cognitive, and physiological responses to stressful clinical events. An interdisciplinary team of nursing, engineering, and computer scientists sought to observe the relationship between self-reported behavioral and biological stress responses when healthcare professionals were exposed to a complex patient clinical event. Experimentation via a simulated clinical scenario with a patient manikin and wearable device instrumentation measured the healthcare professional’s biobehavioral responses, which provided insights into how simulation-based education and research can inform strategies for supporting healthcare professionals in high-stress and demanding clinical environments.
Methods:
In this study, 22 registered/advanced practice nurses, medical assistants, and nursing and medical students were recruited to participate in a patient monitoring experiment. Of the participants, 8 (36%) were registered/advanced practice nurses or practicing medical assistants (MAs), and 14 (64%) were nursing, pre-nursing, or medical students. Prior to the experiment, each participant was asked to complete a demographic survey and questionnaire that included generalized anxiety (GAD-7) and resilience (CD-RISC- 25) surveys. Biometric monitoring instrumentation was then connected to the subject to understand sustained attention, emotional responses, and cognitive loading during a sustained vigilance task. Galvanic Skin Response (GSR), a measurement of stress, was collected through sensors placed on the participants’ pointer and middle fingers. Blood pressure and heart rate were collected at the beginning and end of the experiment. Facial Expression Analysis (FEA) and screen-based eye tracking were collected using iMotions Biometric Suite (Version 9.4 iMotions A/S, Copenhagen, Denmark).
Baseline measurements were taken at the start of the experiment with the subject sitting at rest with their eyes closed. Subjects then completed a patient monitoring simulation, in which a simulated patient (S5301 Advanced HAL, Gaumard Scientific) went through a series of preprogrammed states before deteriorating rapidly with symptoms of a myocardial infarction. At the beginning of the monitoring task, the patient manikin was programmed in a baseline stable state for 5 minutes, after which changed to a change in vital signs (increased heart rate, blood pressure) and then returned to the baseline state. This pattern repeated once more before a pattern of clinical deterioration, culminating with a myocardial infarction. The patient monitoring task lasted 20 minutes in total. Once the patient monitoring simulation was completed, each subject filled out a NASA and SURG TLX questionnaire to quantify the perceived task load of the simulation.
Results and Discussion:
Galvanic Skin Response (GSR) was analyzed to understand the relationship between mental activity and an electrodermal change. Control state GSR levels were compared to GSR levels during the patient’s deterioration at the end of the simulation. During the control state, subjects had a mean of 4.00 peaks per minute (SD = 4.28) and a mean peak amplitude of 0.0195 micro siemens (SD = 0.0235). In contrast, participants had significantly higher levels during the patient deterioration: 8.35 peaks per minute (SD = 4.95) and a mean peak amplitude of 0.0971 micro siemens (SD = 0.0801). One-sided paired T-tests were used to test the significance of the difference between the patient deterioration and control GSR levels. The results were highly significant for both the number of peaks per minute and the peak amplitude, with p-values of 0.002 and 0.000, respectively. These results indicate that stressful clinical scenarios can be effectively simulated in practice and teaching settings with low stakes.
The NASA/SURG TLX survey that participants completed after the patient monitoring simulation was adapted from the NASA TLX to be useful in clinical settings. It is composed of 9 questions which participants rate on a scale of 0 to 100 points in 5-point increments, with 100 being the maximum task load for that category. The categories include mental demand, physical demand, temporal demand, performance, effort, frustration, situational stress, task complexity, and distractions. Participants reported highest mean scores in the following categories: effort (38.2, SD = 25.2), situational stress (37.5, SD = 23.3), and mental demand (34.1, SD = 20.3).
The GAD-7 questionnaire was used as a baseline measurement of the subjects’ anxiety levels. It is composed of seven questions about how the subject has been feeling over the past two weeks, which are scored on a scale of 0 (Not at all) to 3 (Nearly every day). Higher scores indicate higher levels of anxiety. The mean score was 5.59 (SD = 3.89). There was a wide range of scores, from 0 to 16 points. GAD-7 scores and total TLX scores were moderately positively correlated, with a Pearson correlation value of 0.387, indicating that higher anxiety levels are related to increased perceived task load. Interestingly, the change in GSR levels, as measured by the number of peaks per minute and the average peak amplitude, was only slightly positively correlated to GAD-7 and TLX scores. While physiological measures of stress (GSR) may not change significantly as a function of a healthcare professional’s general anxiety levels, the individual with higher baseline anxiety levels may have a heightened perception of how challenging a task was to complete. These results suggest a feasible method to appraise the effects of clinical simulation on healthcare professionals with different experiences and baseline behavioral responses/perceptions to complex clinical care scenarios.
References:
Cant, R. P., & Cooper, S. J. (2017). Use of simulation-based learning in undergraduate nurse education: An umbrella systematic review. Nurse education today, 49, 63-71.
Clinical simulation is increasingly recognized as a vital component of healthcare education, offering an immersive and controlled environment for providing continuing education to healthcare professionals and training for pre-professionals (Cant & Cooper, 2017). These simulations enable learners to engage with complex clinical scenarios in low-stakes settings, facilitating the development of critical skills such as sustained attention, stress management, and clinical problem solving. As demands on healthcare professionals increase, clinical simulations can also be used as a tool to understand the interaction between behavioral, cognitive, and physiological responses to stressful clinical events. An interdisciplinary team of nursing, engineering, and computer scientists sought to observe the relationship between self-reported behavioral and biological stress responses when healthcare professionals were exposed to a complex patient clinical event. Experimentation via a simulated clinical scenario with a patient manikin and wearable device instrumentation measured the healthcare professional’s biobehavioral responses, which provided insights into how simulation-based education and research can inform strategies for supporting healthcare professionals in high-stress and demanding clinical environments.
Methods:
In this study, 22 registered/advanced practice nurses, medical assistants, and nursing and medical students were recruited to participate in a patient monitoring experiment. Of the participants, 8 (36%) were registered/advanced practice nurses or practicing medical assistants (MAs), and 14 (64%) were nursing, pre-nursing, or medical students. Prior to the experiment, each participant was asked to complete a demographic survey and questionnaire that included generalized anxiety (GAD-7) and resilience (CD-RISC- 25) surveys. Biometric monitoring instrumentation was then connected to the subject to understand sustained attention, emotional responses, and cognitive loading during a sustained vigilance task. Galvanic Skin Response (GSR), a measurement of stress, was collected through sensors placed on the participants’ pointer and middle fingers. Blood pressure and heart rate were collected at the beginning and end of the experiment. Facial Expression Analysis (FEA) and screen-based eye tracking were collected using iMotions Biometric Suite (Version 9.4 iMotions A/S, Copenhagen, Denmark).
Baseline measurements were taken at the start of the experiment with the subject sitting at rest with their eyes closed. Subjects then completed a patient monitoring simulation, in which a simulated patient (S5301 Advanced HAL, Gaumard Scientific) went through a series of preprogrammed states before deteriorating rapidly with symptoms of a myocardial infarction. At the beginning of the monitoring task, the patient manikin was programmed in a baseline stable state for 5 minutes, after which changed to a change in vital signs (increased heart rate, blood pressure) and then returned to the baseline state. This pattern repeated once more before a pattern of clinical deterioration, culminating with a myocardial infarction. The patient monitoring task lasted 20 minutes in total. Once the patient monitoring simulation was completed, each subject filled out a NASA and SURG TLX questionnaire to quantify the perceived task load of the simulation.
Results and Discussion:
Galvanic Skin Response (GSR) was analyzed to understand the relationship between mental activity and an electrodermal change. Control state GSR levels were compared to GSR levels during the patient’s deterioration at the end of the simulation. During the control state, subjects had a mean of 4.00 peaks per minute (SD = 4.28) and a mean peak amplitude of 0.0195 micro siemens (SD = 0.0235). In contrast, participants had significantly higher levels during the patient deterioration: 8.35 peaks per minute (SD = 4.95) and a mean peak amplitude of 0.0971 micro siemens (SD = 0.0801). One-sided paired T-tests were used to test the significance of the difference between the patient deterioration and control GSR levels. The results were highly significant for both the number of peaks per minute and the peak amplitude, with p-values of 0.002 and 0.000, respectively. These results indicate that stressful clinical scenarios can be effectively simulated in practice and teaching settings with low stakes.
The NASA/SURG TLX survey that participants completed after the patient monitoring simulation was adapted from the NASA TLX to be useful in clinical settings. It is composed of 9 questions which participants rate on a scale of 0 to 100 points in 5-point increments, with 100 being the maximum task load for that category. The categories include mental demand, physical demand, temporal demand, performance, effort, frustration, situational stress, task complexity, and distractions. Participants reported highest mean scores in the following categories: effort (38.2, SD = 25.2), situational stress (37.5, SD = 23.3), and mental demand (34.1, SD = 20.3).
The GAD-7 questionnaire was used as a baseline measurement of the subjects’ anxiety levels. It is composed of seven questions about how the subject has been feeling over the past two weeks, which are scored on a scale of 0 (Not at all) to 3 (Nearly every day). Higher scores indicate higher levels of anxiety. The mean score was 5.59 (SD = 3.89). There was a wide range of scores, from 0 to 16 points. GAD-7 scores and total TLX scores were moderately positively correlated, with a Pearson correlation value of 0.387, indicating that higher anxiety levels are related to increased perceived task load. Interestingly, the change in GSR levels, as measured by the number of peaks per minute and the average peak amplitude, was only slightly positively correlated to GAD-7 and TLX scores. While physiological measures of stress (GSR) may not change significantly as a function of a healthcare professional’s general anxiety levels, the individual with higher baseline anxiety levels may have a heightened perception of how challenging a task was to complete. These results suggest a feasible method to appraise the effects of clinical simulation on healthcare professionals with different experiences and baseline behavioral responses/perceptions to complex clinical care scenarios.
References:
Cant, R. P., & Cooper, S. J. (2017). Use of simulation-based learning in undergraduate nurse education: An umbrella systematic review. Nurse education today, 49, 63-71.
Authors
Research Scientist
Research Analyst
Event Type
Oral Presentations
TimeTuesday, April 14:10pm - 4:30pm EDT
LocationPier 9
Simulation and Education (SE)