scholarly journals The Use of Simulation Games and Tabletop Exercises in Disaster Preparedness Training of Emergency Medicine Residents

2019 ◽  
Vol 34 (s1) ◽  
pp. s82-s82 ◽  
Author(s):  
Lourdes Rodriguez Rivera ◽  
Cynthia Rodriguez Rivera ◽  
Alberto Zabala Soler ◽  
Rey Pagan Rivera ◽  
Luis Rodriguez ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Emergency Preparedness ◽  
Clinical Decision Making ◽  
Group Versus ◽  
Simulation Game ◽  
Exam Performance ◽  
Interactive Simulation ◽  
Knowledge Based ◽  
Teaching Interventions ◽  
Exam Scores

Introduction:Emergency physicians play a frontline role in hospital disaster responses and require appropriate training.Aim:The aim of the current study was to pilot and compare the effectiveness of two emergency preparedness teaching interventions: the first employing traditional lecture-based instruction (LEC) and the second utilizing interactive simulation/game-based teaching (SIM).Methods:A two-group randomized pre- and post-test design was implemented into the didactic curriculum of the Emergency Medicine (EM) Residency Training Program at the San Lucas Episcopal Hospital in Ponce, Puerto Rico. Residents (n=23) completed either a LEC (control) or SIM teaching module (single day, one to two hours) focusing on emergency preparedness concepts, disaster-related clinical decision-making, and physician responsibilities during hospital disaster protocols. Knowledge-based multiple-choice exams and scenario-based competency exams were administered at three different time points: one-week pre-intervention, immediately post-training, and two-weeks post-training. Test scores were compared between groups at each time point using the Mann-Whitney U test.Results:Following the teaching interventions, no significant differences were found between the LEC group versus the SIM group in knowledge-based exam performance (LEC 81.1%[9.4] vs. SIM 74.9%[12.1]; U=42.50, p=0.15) and scenario-based exam performance (LEC 80.0%[9.7] vs. SIM 80.2%[9.2]; U=62.00, p=0.83), suggesting both teaching methods were similarly effective. Indeed, knowledge-based exam scores improved two-fold and scenario-based exam scores improved by over 50% immediately following training relative to baseline exam scores. Two-weeks post-training, a significant decrease in scenario-based exam performance was found in the LEC group relative to the SIM group (LEC 63.1%[11.6] vs. SIM 75.4%[11.5]; U=91.50, p=0.036), suggesting residents who train with simulations show greater retention of scenario-based concepts compared to those who train with lecture-based training alone.Discussion:The current study highlights the potential dual value of incorporating simulation training in EM emergency preparedness curriculums in improving both knowledge and concept retention of physician disaster responsibilities.

scholarly journals An Interactive Simulation Game to Enhance Learners' Experience on Ubiquitous Computing Devices

10.5772/8108 ◽  
2009 ◽  
Author(s):  
Vincent Tam ◽  
Zexian Liao ◽  
C.H. Leung ◽  
Lawrence Yeung ◽  
Alvin C.M.
Keyword(s):  
Ubiquitous Computing ◽  
Simulation Game ◽  
Interactive Simulation

Emergency Medicine

2021 ◽  
pp. 219-226
Keyword(s):  
Emergency Medicine ◽  
Knowledge Based ◽  
Questions And Answers ◽  
Short Explanation

This chapter is comprised of 10 clinically based and also knowledge based questions and answers. The corresponding answers to the questions can be found at the end of the chapter, each of which has a short explanation and at least one reference.

scholarly journals P071: Artificial intelligence in emergency medicine: A scoping review

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S90-S90
Author(s):  
A. Kirubarajan ◽  
A. Taher ◽  
S. Khan ◽  
S. Masood
Keyword(s):  
Artificial Intelligence ◽  
Emergency Medicine ◽  
Language Processing ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
A Priori ◽  
Clinical Decision ◽  
Hospital Environment ◽  
Clinical Implementation

Introduction: The study of artificial intelligence (AI) in medicine has become increasingly popular over the last decade. The emergency department (ED) is uniquely situated to benefit from AI due to its power of diagnostic prediction, and its ability to continuously improve with time. However, there is a lack of understanding of the breadth and scope of AI applications in emergency medicine, and evidence supporting its use. Methods: Our scoping review was completed according to PRISMA-ScR guidelines and was published a priori on Open Science Forum. We systematically searched databases (Medline-OVID, EMBASE, CINAHL, and IEEE) for AI interventions relevant to the ED. Study selection and data extraction was performed independently by two investigators. We categorized studies based on type of AI model used, location of intervention, clinical focus, intervention sub-type, and type of comparator. Results: Of the 1483 original database citations, a total of 181 studies were included in the scoping review. Inter-rater reliability for study screening for titles and abstracts was 89.1%, and for full-text review was 77.8%. Overall, we found that 44 (24.3%) studies utilized supervised learning, 63 (34.8%) studies evaluated unsupervised learning, and 13 (7.2%) studies utilized natural language processing. 17 (9.4%) studies were conducted in the pre-hospital environment, with the remainder occurring either in the ED or the trauma bay. The majority of interventions centered around prediction (n = 73, 40.3%). 48 studies (25.5%) analyzed AI interventions for diagnosis. 23 (12.7%) interventions focused on diagnostic imaging. 89 (49.2%) studies did not have a comparator to their AI intervention. 63 (34.8%) studies used statistical models as a comparator, 19 (10.5%) of which were clinical decision making tools. 15 (8.3%) studies used humans as comparators, with 12 of the 15 (80%) studies showing superiority in favour of the AI intervention when compared to a human. Conclusion: AI-related research is rapidly increasing in emergency medicine. AI interventions are heterogeneous in both purpose and design, but primarily focus on predictive modeling. Most studies do not involve a human comparator and lack information on patient-oriented outcomes. While some studies show promising results for AI-based interventions, there remains uncertainty regarding their superiority over standard practice, and further research is needed prior to clinical implementation.

Knowledge-based Multiagent Coordination

1998 ◽  
Vol 7 (6) ◽  
pp. 547-563 ◽  
Author(s):  
John E. Laird ◽  
Randolph M. Jones ◽  
Paul E. Nielsen
Keyword(s):  
Air Force ◽  
Intelligent Agents ◽  
Deep Understanding ◽  
Distributed Artificial Intelligence ◽  
Interactive Simulation ◽  
Multiagent Coordination ◽  
Coordination Strategies ◽  
Knowledge Based ◽  
Distributed Interactive Simulation ◽  
Simple Rules

In many domains, intelligent agents must coordinate their activities in order for them to be successful both individually and collectively. Over the last ten years, research in distributed artificial intelligence has emphasized building knowledge-lean systems, where coordination emerges either from simple rules of behavior or from a deep understanding of general coordination strategies. In this paper, we contend that there is an alternative for domains in which the types and methods of coordination are well structured (even though the environment may be very unstructured and dynamic). The alternative is to build real-time, knowledge-based agents that have a broad—but shallow—understanding of how to coordinate. We demonstrate the viability of this approach by example. Specifically, we have built agents that model the coordination performed by Navy and Air Force pilots and controllers in air-to-air and air-to-ground missions within a distributed interactive simulation environment. The major contribution of the paper is an examination of the requirements and approaches for supporting knowledge-based coordination, in terms of the structure of the domain, the agents' knowledge of the domain, and the underlying AI architecture.

Knowledge-based clinical decision support engine for psychiatric nursing process: design and evaluation (Preprint)

2018 ◽  
Author(s):  
Kuei-Fang Ho ◽  
Po-Hsiang Chou ◽  
Jane C.-J. Chao ◽  
Chien-Yeh Hsu ◽  
Min-Huey Chung
Keyword(s):  
Decision Support ◽  
Sensitivity And Specificity ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Psychiatric Nursing ◽  
Nursing Process ◽  
Knowledge Based ◽  
Nursing Diagnoses

BACKGROUND Nursing assessments used in the psychiatry department considerably differ from those used in other departments. OBJECTIVE We developed a psychiatric knowledge-based clinical decision support system (Psy-KBCDSS) to help nurses in assessing patients’ problems. In addition, we evaluated the sensitivity and specificity of the Psy-KBCDSS and determined whether the Psy-KBCDSS can accurately formulate nursing diagnoses to assist nurses in providing care for psychiatric patients. METHODS Visual Studio 2005 was adopted as the primary software development tool. C# was used as the main development language, and a graphical concept was applied to develop the interface. We established a clinical diagnostic validity inference engine (CDVIE) to calculate the actual nursing assessment scores of nurses engaging in clinical tasks and to compute the nursing diagnosis data registered in the psychiatric nursing process system (Psy-NPS). The sensitivity and specificity of the nursing diagnoses formulated by the senior nurses and junior nurses regarding the same patient were extracted from the Psy-NPS and Psy-KBCDSS databases to conduct effectiveness assessment. RESULTS This study involved 22 nursing diagnoses commonly encountered in psychiatric wards. Of these diagnoses, the top 8 most common diagnoses formulated by the participants were altered thought processes, ineffective coping, sensory and perceptual alterations, insomnia, risk for other-directed violence, anxiety, impaired social interaction, and risk for suicide in Psy-NPS and Psy-KBCDSS. However, the diagnoses that showed significant increase in sensitivity between the Psy-NPS and Psy-KBCDSS were sensory and perceptual alterations, ineffective coping, and insomnia. The specificity of ineffective coping also increased considerably. CONCLUSIONS The Psy-KBCDSS is an empirical patient-oriented nursing clinical decision-making support system that may be used in patients’ individual assessment and helps nurses in formulating appropriate nursing diagnoses according to the nursing process.

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