Virtual Environments (VEs) are used extensively for training systems because of their ability to provide interactive experiences. Effective use of such environments requires the ability to generate consistent scenarios, which involve one or more realistic yet repeatable interactions between the trainee and the virtual humans in the training environment. This paper presents a framework that facilitates the development of rich interactive scenarios between one or more virtual humans and a trainee. The framework utilizes a scenario authoring system with runtime elements that ensure the trainee encounters realistic and stochastic, yet repeatable, well-orchestrated and precisely choreographed situations to meet specific training goals. Core elements of the framework include the use of probabilistic state machines for controlling the behavior of virtual elements along with triggers which ensure pre-determined outcomes even when utilizing stochastic behavioral models for the virtual humans.
With the constant push to do more for less, the use of virtual environments (VE), simulations and serious games has exploded. Human Factors (HF) personnel often use these tools to support a range of activities, including modeling process or the effects of humans in the system; designing, testing and validating new systems and processes; training skills, procedures and techniques; and even for therapeutic activities. This session will describe and demonstrate some of the diverse uses for virtual environments (VEs) in an alternate demonstration format. The session will begin with demonstrators providing a brief description of their VE, and how they’ve used it to answer a critical research question or address a unique need, including a video demonstration of the VE in action. After these introductions, all demonstrations will be set up around the room, and session attendees can move around the room for direct interaction with the demonstrations. This session should provoke ideas among attendees for how VEs, simulations and serious games can help address their research, training, education, evaluation or therapeutic needs.
Introduction/Background Simulation engineers at the Virginia Modeling, Analysis, and Simulation Center (VMASC) and physicians at Englewood Hospital and Medical Center have collaborated in developing a tablet app for advancing patient safety via training Patient Blood Management (PBM). PBM is a clinical approach to improving patient outcome by minimizing or eliminating allogenic blood transfusions often associated with increased adverse effects to the patient and high monetary costs to the medical facilities administering them.1,2 The tool aims to train or newly-educate physicians through interactive, immersive instruction focusing on blood management decision-making. The 2013 Patient Safety, Science, and Technology Summit cited transfusion overuse as the third leading challenge to patient safety.3 Red blood cell (RBC) transfusion is the most frequent procedure performed in U.S. hospitals with one in ten inpatients receiving one or more units.4 RBC transfusions rates or practices are highly variable by institution, procedure, and physician.5 Evidence from observational studies shows RBC transfusions can increase mortality by 69% and morbidity by 88%, while restrictive transfusion practices have been proven safe in multiple randomized controlled trials.6,7 This tablet app addresses these significant health concerns as well as practice variability, safety and effectiveness, blood supply burden, and financial cost of blood – the tool serves as a means to increase knowledge of transfusion avoidance. There was no medical simulation training tool that provided the educational substance, experiential learning, and self-assessment for clinicians in this emerging field of study. This app meets that need as it is the most efficient and effective teaching modality for closing the PBM education gap. Methods The technical team began mapping PBM into a system dynamics model. This yielded a visual representation of procedures, factor inter-relationships, and validation of model development for the tool prior to programming and software design. The tool is constructed as a multi-platform application, targeted at tablet computers (Android and IOS), but also accessible on pc’s and through web pages. The tool contains actual patient case studies (for credibility), executes real-time simulation (for temporal decision-making experience), and possesses end-of-the session assessment for trainee to compare his decisions with the actual case (for lessons learned). The tool is scalable, extendable, facilitating the registration of a large number of case studies. The tool presents a 3D environment to the user including responsive patient avatars and simulated medical equipment. The visual presentation is developed in the Unity3D game engine. Interactive user interface presents information ranging from patient demographic information to displaying patient medical status via ECG waveforms, and capturing user input including prescription choices. Unity3D enables the software to be presented on most devices (Android, IOS, PC, Mac, web), though tablets are specifically targeted. The software mirrors the specific procedures and techniques used in PBM dividing the training into pre-, intra-, and post-operative phases of patient care. Results: Conclusion This tablet training app will be populated with case studies from various surgeons and anesthesiologists who have drawn upon their own patient cases and experiences. The cases express their decision-making process throughout the peri-operative care, and it ties those decisions to the current literature for the trainee to review. The app will contain a variety of cases to include abdominal cardio-vascular, orthopedic, trauma, and pediatric procedures. This tablet app is developed with continuous simulation capability for hands-on, real-time exercises. It satisfies an unmet need for simulation training modalities in this medical sub-field and as such it provides the means for comprehensive and effective training to mitigate the issues and challenges surrounding blood transfusion through immersive simulation training of the fundamental principles of PBM. References 1. Speiss B, Spence R, Shander A. Perioperative Transfusion Medicine. New York: Lippincott Williams Wilkins, 2006, pp. 671-672. 2. Shander A, et.al, “Activity based costs of blood transfusions.” Transfusion 50 (4): 753-765, 2010. 3. Patient Safety Summit 2013. Website http://www.patientsafetysummit.org/ Accessed 20 July 2013. 4. AHRQ. Inpatient Sample. 1997-2007. Website http://www.hcup-us.ahrq.gov/reports/factsandfigures/2007/section2_TOC.jsp. Accessed 20 July 2013. 5. Frank S., et al. Anesthesiology. 2012. 117(1): 99-106. 6. Marik PE., et.al. Critical Care Medicine. 2008;36(9):2667-74. 7. Carson J., et al. Cochrane Database Syst Rev. 2012 Apr 18;4:CD002042. Disclosures None.
Purpose Agent-based modelling and simulation (ABMS) has seen wide-spread success through its applications in the sciences and social sciences over the last 15 years. As ABMS is used to model more and more complex systems, there is going to be an increase in the number of input variables used within the simulation. Any uncertainty associated with these input variables can be investigated using sensitivity analysis, but when there is uncertainty surrounding several of these input variables, a single parameter sensitivity analysis is not adequate. Latin hypercube sampling (LHS) offers a way to sample variations in multiple parameters without having to consider all of the possible permutations. This paper introduces the application of LHS to ABMS via a case study that investigates the mortgage foreclosure contagion effect. This paper aims to discuss these issues.
Efforts directed at enhancing and maturing NASA Langley Research Center's Airspace and Traffic Operations Simulation (ATOS) and Aircraft Simulation for Traffic Operations Research (ASTOR) software frameworks are described. ATOS and ASTOR were created to explore future concepts for air traffic management in the national airspace, commonly referred to as Next Generation Air Transportation Systems (NextGen). This software allows for multiple aircraft, both manually controlled and autonomously controlled, to be simulated during airport operations including phases such as approach and departure, landing and takeoff, and taxi and ground movement. This simulation can be used to study the effects of modification to traffic patterns, runway usage, separation constraints, traffic management strategy, technology infusion, and uninhabited air vehicle integration. Specific enhancements addressed development of a dual-crew research station, increased surface operation capability, expanded cockpit view scenery, and improved ground handling model characteristics. The goal is to develop NextGen concepts that will provide high capacity and efficient air operations with enhanced safety and reduced risk in the national airspace system.
