Medical team training (MTT) conducted in a virtual environment fosters growth in cognitive, technical, and clinical aptitudes while offering advantages of flexibility, cost, and ease of scheduling over traditional high- fidelity simulations. Growing technology facilitates innovations to improve the ability to emulate roles, rules, resources, and fidelity. Our objective was to evaluate elements of key features that inform technical specifications for virtual simulations. A narrative review included 27 articles as relevant to elaborate on five key features identified as critical to development of virtual environments for MTT: automated assessment, task fidelity, interface modality, virtual teammates, and adaptability. Designers continue to improve the technology of virtual reality to create better and more enhanced training modules. We must better understand how variances in simulation features impacts performance outcomes and learned behavior. Future research can more deeply examine features beyond the five reviewed here to guide development of effective, cost-efficient virtual simulations for MTT.

Medical educators have acknowledged the importance of simulation training in developing procedural skills. While simulation training in other disciplines has benefitted from evaluations of users’ skill acquisition, the majority of medical training simulators continue to be developed from overly simplified descriptions of procedures, such as techniques prescribed by existing instructional material. Our objective was to use a modeling framework to characterize the skill of various users in applying junctional tourniquets in order to design an effective training simulator. We recorded 46 medical first responders performing training exercises applying a junctional tourniquet and used coded video and sensor data to identify the hierarchy of actions they performed in the process. The model provides several insights into trainee performance, such as the way in which advanced users perform more tasks in parallel, or areas where advanced users employ situational awareness to identify ways they can deviate from recommended protocol to improve outcomes. The model successfully identifies variations in tourniquet application technique that correlates with improvement on clinically relevant metrics including application speed, pressure applied, and tourniquet placement stability. This methodology can improve medical training simulations by indicating changes during the course of learning a new task, such as helpful deviations from instructional protocol.

Medical providers must master a large number of complicated tasks to deliver quality care and minimize unwanted clinical outcomes. In order to optimally train these tasks, medical training systems would benefit from models of skill that enable objective assessment of proficiency and define important declarative knowledge, cognitive states, and decision-making rules that are necessary for effective learning and performance. This article describes the Methodology for Annotated Skill Trees (MAST), a skill-modeling framework that facilitates the creation of descriptive and rule-based content that supports skill acquisition. This framework is used to generate models of trauma assessment skills from two existing curricula: Advanced Trauma Life Support (ATLS) and the Trauma Nurse Core Course (TNCC). Key differences between these curricula's teaching methods for the same procedure and skill are highlighted through the use of the model framework. The framework comparison provides insight into the underlying teaching approach and highlights the fact that some skills are not represented in medical education materials.

Educators are challenged to prepare nurses to care for low-frequency, high-stakes problems such as trauma. Computer-based tutors provide a cost-effective teaching strategy without risking patient safety. Evidence for the efficacy of this type of instruction is limited; thus, we tested the learning outcomes of a tutor on trauma care knowledge with senior nursing students. Participants were randomly assigned to either the tutor or a control condition (textbook learning). Instructional design elements incorporated into the tutor included use of multimedia content, emphasis of key points, frequent quizzing with instant feedback, and unfolding case studies to summarize key concepts. Use of the tutor led to a larger increase in trauma nursing knowledge than use of a textbook. In addition, the knowledge was retained as well as book-based learning. The effect size of the tutor, 1.15, was relatively high as well—the average for computer tutors is 0.79. Qualitative focus groups revealed that participants expressed favorable views of the tutor in comparison to textbook learning. They found it more engaging and more enjoyable and reported that it effectively organized the content. The results of this study support the efficacy of a well-designed computer-based tutor for learning key concepts of trauma nursing.

Emergency Department (ED) congestion is a significant problem affecting clinical outcomes, patient satisfaction and hospital costs. Identifying and resolving bottlenecks in the flow of patients from the ED to eventual admission or discharge has the potential to reduce wait times, improve care for individual patients, and increase the volume of patients treated at the hospital over time. Our objective was to review methods commonly used to measure, analyze, and visualize patient flow, characterize drawbacks to these methods, and identify areas in which analysis and visualization can be improved to make bottlenecks easier to identify and resolve. Sixty-five articles obtained from PubMed and Google Scholar searches were reviewed to identify: (1) variables used to measure ED throughput; (2) downstream effects of ED congestion; (3) factors contributing to ED congestion; (4) techniques used to predict or respond to ED congestion; and (5) tools used to visualize data on ED throughput. Hospital resource availability, patient demographics, and environmental factors have all been used to predict contributors to ED congestion. Unfortunately, the hospital practices most critical to ED congestion are unlikely to change as they involve increasing the number of beds and providers or modifying protocols with EMS, insurance, and other care facilities. Therefore, interventions addressing optimization of ED resource allocation and visualization of ED data are the best avenue to yield more efficient ED operation.