Title: Debriefings on Prehospital Care Scenarios in MedDbriefer—A Tool to Support Peer Learning
Across the healthcare professions, many students don’t get enough practice doing simulated clinical interactions during course labs to feel confident about passing certification exams and treating actual patients. To address this problem, we are developing MedDbriefer, a web-based tutoring system that runs on a tablet. MedDbriefer allows peers to engage in supplemental clinical scenarios on their own. With its current focus on paramedic train-ing, one student “voice treats” a simulated patient as the leader of a mock emergency medical services team while a peer uses MedDbriefer’s check-lists to log the team leader’s verbalized actions. The system then analyzes the event log and generates a debriefing, which highlights errors such as as-sessment actions and treatment interventions that the team leader missed or performed late. This paper focuses on how the system analyzes event logs to generate adaptive debriefings. more »« less
MedDbriefer is a web based ITS designed to enable healthcare students to do clinical scenarios anytime, anywhere. While one student “voice treats” a scenario’s patient(s) as the leader of a mock Emergency Medical Services (EMS) team, a peer records the team’s actions by using the system’s checklists, on a tablet. When the scenario ends, MedDbriefer analyzes the event log and generates a debriefing. MedDbriefer also provides a platform for research on simulation-based training. This paper describes how the system’s debriefing engine could be extended to deliver feedback during a scenario, as well as afterwards. MedDbriefer could then be used to compare the effectiveness of different ways of timing feedback delivery in computer-based simulation systems.
Katz, S.; Albacete, P.; Gallagher, J.; Jordan, P.; Platt, T.; Silliman, S.; Yang, T.
(, Intelligent Tutoring Systems: 18th International Conference, ITS 2022)
This poster describes an early-stage project. It introduces MedDbriefer, a tablet-based tool that allows small groups of paramedic students to practice realistic prehospital emergency care scenarios. While two or more students collaborate as members of an emergency medical service (EMS) team, a peer uses the tablet’s checklists to record the team’s actions. The system then analyzes the event log to provide an automated debriefing on the team’s performance. Although debriefing is purported to be one of simulation-based training’s most critical components, there is little research to guide human and automated debriefing. We are imple-menting two approaches to automated debriefing and will compare their effective-ness in an upcoming randomized controlled trial.
Popov, Vitaliy; Rochlen, Lauryn R
(, BMC medical education)
Background Effective team communication is crucial for managing medical emergencies like malignant hyperthermia (MH), but current assessment methods fail to capture the dynamic and temporal nature of teamwork processes. The lack of reliable measures to inform feedback to teams is likely limiting the overall effectiveness of simulation training. This study demonstrates the application of ordered network analysis (ONA) to model communication sequences during the simulated MH scenario. Methods Twenty-two anesthesiologists participated in video-recorded MH simulations. Each scenario involved one participant as the primary anesthesiologist with confederates in supporting roles. Team communication was coded using the Team Reflection Behavioral Observation (TuRBO) framework, capturing behaviors related to information gathering, evaluation, planning, and implementation. ONA modeled the sequences of these coded behaviors as dynamic networks. Teams were classified as high- or low-performing based on timely dantrolene administration and appropriate MH treatment actions. Network visualizations and statistical tests compared communication patterns between groups. Results Five of 22 teams (23%) were high-performing. ONA revealed high-performers transitioned more effectively from situation assessment (information seeking/evaluation) to planning and implementation, while low-performers cycled between assessment behaviors without progressing (p = 0.04, Cohen’s d = 1.72). High-performers demonstrated stronger associations between invited input, explicitly assessing the situation, stating plans, and implementation. Conclusions Integrating video coding with ONA provides an innovative approach for examining team behaviors. Leveraging ONA can uncover patterns in communication timing and sequences, guiding targeted interventions to improve team coordination in various real-world clinical and simulated settings (e.g., operating room, EMS, ICU).
Y. E. Kalay, D. Schaumann
(, Symposium on Simulation in Architecture + Urban Design (SimAUD), 2021)
We present a simulation-powered dynamic building activities management system, intended to help coordinate distributed decision-making activities in sensor-equipped complex buildings, such as healthcare facilities. It provides overall “awareness” of the current state of the facility and analyzes the impact of simulated alternative future actions of each actor in every space, simultaneously. These analytics are evaluated according to Key Performance Indicators (KPI), resulting in a recommendation for enacting the most desirable outcome. A preliminary simulation study based on St. Bernardine Medical Center (SBMC) Cardiac Catheterization Lab (CCL) is presented.
Kalay Yehuda, Schaumann Davide
(, Symposium on Simulation for Architecture and Urban Design (SimAUD))
null
(Ed.)
We present a simulation-powered dynamic building activities management system, intended to help coordinate distributed decision-making activities in sensor-equipped complex buildings, such as healthcare facilities. It provides overall “awareness” of the current state of the facility and analyzes the impact of simulated alternative future actions of each actor in every space, simultaneously. These analytics are evaluated according to Key Performance Indicators (KPI), resulting in a recommendation for enacting the most desirable outcome. A preliminary simulation study based on St. Bernardine Medical Center (SBMC) Cardiac Catheterization Lab (CCL) is presented.
Katz, S., Jordan, P., and Silliman, S. Debriefings on Prehospital Care Scenarios in MedDbriefer—A Tool to Support Peer Learning. Retrieved from https://par.nsf.gov/biblio/10443696. Proceedings of the 3rd International Conference on Novel and Intelligent Digital Systems, NIDS 2023 .
Katz, S., Jordan, P., & Silliman, S. Debriefings on Prehospital Care Scenarios in MedDbriefer—A Tool to Support Peer Learning. Proceedings of the 3rd International Conference on Novel and Intelligent Digital Systems, NIDS 2023, (). Retrieved from https://par.nsf.gov/biblio/10443696.
Katz, S., Jordan, P., and Silliman, S.
"Debriefings on Prehospital Care Scenarios in MedDbriefer—A Tool to Support Peer Learning". Proceedings of the 3rd International Conference on Novel and Intelligent Digital Systems, NIDS 2023 (). Country unknown/Code not available. https://par.nsf.gov/biblio/10443696.
@article{osti_10443696,
place = {Country unknown/Code not available},
title = {Debriefings on Prehospital Care Scenarios in MedDbriefer—A Tool to Support Peer Learning},
url = {https://par.nsf.gov/biblio/10443696},
abstractNote = {Across the healthcare professions, many students don’t get enough practice doing simulated clinical interactions during course labs to feel confident about passing certification exams and treating actual patients. To address this problem, we are developing MedDbriefer, a web-based tutoring system that runs on a tablet. MedDbriefer allows peers to engage in supplemental clinical scenarios on their own. With its current focus on paramedic train-ing, one student “voice treats” a simulated patient as the leader of a mock emergency medical services team while a peer uses MedDbriefer’s check-lists to log the team leader’s verbalized actions. The system then analyzes the event log and generates a debriefing, which highlights errors such as as-sessment actions and treatment interventions that the team leader missed or performed late. This paper focuses on how the system analyzes event logs to generate adaptive debriefings.},
journal = {Proceedings of the 3rd International Conference on Novel and Intelligent Digital Systems, NIDS 2023},
author = {Katz, S. and Jordan, P. and Silliman, S.},
}
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