James Niehaus

Prevention of Surgical Skill Decay

The U.S. military medical community spends a great deal of time and resources training its personnel to provide them with the knowledge and skills necessary to perform life-saving tasks, both on the battlefield and at home. However, personnel may fail to retain specialized knowledge and skills if they are not applied during the typical periods of nonuse within the military deployment cycle, and retention of critical knowledge and skills is crucial to the successful care of warfighters. For example, we researched the skill and knowledge loss associated with specialized surgical skills such as those required to perform laparoscopic surgery (LS) procedures. These skills are subject to decay when military surgeons perform combat casualty care during their deployment instead of LS. This article describes our preliminary research identifying critical LS skills, as well as their acquisition and decay rates. It introduces models that identify critical skills related to laparoscopy, and proposes objective metrics for measuring these critical skills. This research will provide insight into best practices for (1) training skills that are durable and resistant to skill decay, (2) assessing these skills over time, and (3) introducing effective refresher training at appropriate intervals to maintain skill proficiency.

Modeling ground soldier situational awareness for constructive simulation with rules

The behavior models that control simulated warfighters in most modeling and simulation (M&S) efforts are fairly simple, relying predominantly on behavior scripting and simple rules to produce actions. As a result, the simulated entities do not reflect critical situational awareness factors used by Ground Soldiers or allow for the modeling of devices that influence situational awareness, such as user defined operating pictures (UDOPs). This paper describes our approach to this challenge, providing 1) a rule-based method for modeling Ground Soldier situational awareness and devices that influence situational awareness and 2) a user friendly graphical authoring tool for creating these rules. We present a requirements analysis of this modeling task and discuss and provide examples of how our method may be employed for modeling Soldier perception and inferences as well as devices that affect situational awareness.

Design of Training Protocol for Perceptual and Technical Skills in a Minimally Invasive Surgery

This study investigates how the technical and perceptual skills in laparoscopic surgery, typically acquired separately in the initial learning phases, can be trained together. A task analysis and cognitive task analysis were conducted using a cholecystectomy procedure and a fundoplication procedure. An experiment was conducted to examine the interaction of technical and perceptual skill learning. Subjects were divided into three groups based on order of skills training: 1) technical-perceptual-combined skills training order, 2) perceptual-technical-combined skills training order, and 3) combined skills training. After the training sessions, performance was evaluated using the combined skill. Preliminary results indicate that performance of the group trained in the combined skills condition performed equally quickly as those who trained the technical and perceptual skills separately first. In addition, the number of technical errors and perceptual errors committed were lower. This suggests that surgical skills training may be more efficient if perceptual learning is combined with motor skills during the initial phases of training. This has implications for the design of surgical training simulators and surgical education in general.© 2012 ASME

A method for generating narrative discourse to prompt inferences

Narratives that prompt inferences can be more interesting in that they provide the reader with the opportunity to reason about the narrative world, participating in its construction. These narratives can also be more concise and direct, as details can be filled in by the reader. On the other hand, narratives that leave out important information without the opportunity to infer this information may be incoherent. To generate narratives that prompt inferences a system must 1) employ a theory of how inferences are prompted and 2) provide a capacity for creating narratives that satisfy inference goals. This paper presents is a novel algorithm for generating discourse plans that prompt inferences according to a theory of online inferencing in narrative discourse. Though other approaches have generated narrative and discourse structures to influence the readers perception of the narrative, this is the first approach to present an empirically based cognitive model of online inference generation. The algorithm is a partial-order planning approach to discourse generation, selecting events to tell the reader from an input story plan.

Towards an architecture for collaborative human/AI control of interactive characters

Interactive characters - widely used for entertainment, education, and training - may be controlled by human operators or software agents. Human operators are extremely capable in supporting this interaction, but the cost per interaction is high. Believable agents are software controlled characters that attempt natural and engaging interaction. Believable agents are inexpensive to operate, but they cannot currently support a full range of interaction. To combine the strengths of human operators and believable agents, this paper presents steps toward an architecture for collaborative human/AI control of interactive characters. A human operator monitors the interactions of users with a group of believable agents and acts to intervene and improve interaction. We identify challenges in constructing this architecture and propose an architecture design to address these challenges. We discuss technologies that enable the operator to monitor many believable agents at once and act to intervene quickly and on many levels of granularity. To increase speed and usability, we employ principles of narrative structure in the design of our architectural components.

Strengthening Health and Improving Emotional Defenses (SHIELD)

Relaxation techniques such as deep breathing, and meditation can be used to gain more control of how individuals respond to stressful situations. While these techniques are becoming increasingly mainstream, there is still a stigma that can deter some users. Unfortunately, these populations stand to gain the most from developing these psychological tools. We set out to develop a mobile application to make relaxation training more appealing and approachable for the targeted population, which we believe is critical in order to gain wide scale usage. The Department of Defense has devoted substantial resources to developing stress prevention and resilience programs to combat the effects of stress; however, there is limited evidence to justify the cost and scope of current programs. We aimed to develop a low-cost, evidence-based mobile application tailored for the Marine Corps. Our solution, Strengthening Health and Improving Emotional Defenses (SHIELD), is designed to be a comprehensive approach based on the latest evidence-based strategies to train Marines to develop psychological resilience and promote healthy responses to adverse and stressful events. The overall SHIELD program is designed to promote gradual, self-paced practice, allowing Marines to complete training on a schedule that works for them.

Development of Virtual Simulations for Medical Team Training: An Evaluation of Key Features

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.

Thematic Issues in Analysis and Visualization of Emergency Department Patient Flow

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.

Modeling and Comparing Nurse and Physician Trauma Assessment Skills

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 curriculas 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.

Modeling Training Efficiency in GIFT

The US Army Learning Model (ALM) emphasizes the importance of deployable, individualized, adaptive training technologies to help Soldiers better learn and improve critical skills in dynamic and challenging environments. The Army is developing one such technology known as the Generalized Intelligent Framework for Tutoring (GIFT). GIFT is an open-source, domain-independent intelligent tutoring framework that facilitates reuse of components in an effort to reduce the expense of developing and delivering adaptive training. Adaptive training offers the promise of higher levels of proficiency, but another important benefit is that it is more efficient than one-size-fits-all training. Put another way, intelligent, adaptive training should require less time to train a population of learners to a given level of proficiency than non-adaptive training. The gains in efficiency should be a function of several factors including learner characteristics (e.g., aptitude, reading ability, prior knowledge), learning methods employed by the adaptive training system, course content (e.g., difficulty and length, adaptability), and test characteristics (e.g., difficulty, number of items). Optimizing training efficiency requires one to tune the instructional design and course content to the characteristics of the learners. GIFT currently lacks the ability to model or predict the efficiency with which training can be delivered based on these factors. This paper presents a process, and proposed architecture to enable GIFT to make estimates of training efficiency. How this architecture supports authoring and how machine learning can be used to improve the predictive model are also discussed.