Medical errors are now estimated to be the third leading cause of death in the United States (Makary & Daniel, 2016). Look-alike, sound- alike prescription drug mix-ups contribute to this figure. The US Food and Drug Administration (FDA) and Institute for Safe Medication Practices (ISMP) have recommended the use of Tall Man lettering since 2008, in which dissimilar portions of confusable drug names pairs are capitalized in order to make them more distinguishable. Research on the efficacy of Tall Man lettering in differentiating confusable drug name pairs has been inconclusive and it is imperative to investigate potential efficacy further considering the clinical implications (Lambert, Schroeder & Galanter, 2015). The present study aimed to add to the body of research on Tall Man lettering while also investigating another possibility for the mechanism behind Tall Man’s efficacy, if it in fact exists. Studies indicate that the first letter in a word offers an advantage over other positions, resulting in more accurate and faster recognition (Adelman, Marquis & Sabatos-DeVito, 2010; Scaltritti & Balota, 2013). The present study used a 2x3 repeated measures design to analyze the effect of position on Tall Man lettering efficacy. Participants were shown a prime drug, followed by a brief mask, and then either the same drug name or its confusable pair and asked to identify whether they were the same or different. All participants completed both lowercase and Tall Man conditions. Overall performance measured by accuracy and reaction time revealed lowercase to be more effective than Tall Man. With regard to the position of Tall Man letters, a first position advantage was seen both in accuracy and reaction time. A first position advantage was seen in the lowercase condition as well, suggesting the location of the differing portion of the word matters more than the format used. These findings add to the body of inconclusive research on the efficacy of Tall Man lettering in drug name confusion. Considering its impact on patient safety, more research should be conducted to definitively answer the question as to whether or not Tall Man should be used in practice.

The American Heart Association (AHA) estimates that there are approximately 200,000 in-hospital cardiac arrests (IHCA) annually with low rates of survival to discharge at about 22%. Training programs for cardiac arrest teams, also termed code teams, have been recommended by the Institute of Medicine (IOM) and in the AHA's consensus statement to help improve these dismal survival rates. Historically, training programs in the medical field are procedural in nature and done at the individual level, despite the fact that healthcare providers frequently work in teams. The rigidity of procedural training can cause habituation and lead to poor team performance if the situation does not match the original training circumstances. Despite the need for team training, factors such as logistics, time, personnel coordination, and financial constraints often hinder resuscitation team training. This research was a three-step process of: 1) development of a metric specific for the evaluation of code team performance, 2) development of a communication model that targeted communication and leadership during a code blue resuscitation, and 3) training and evaluation of the code team leader using the communication model. This research forms a basis to accomplish a broad vision of improving outcomes of IHCA events by applying conceptual and methodological strategies learned from collaborative and inter-disciplinary science of teams.

We experience spatial separation and temporal asynchrony between visual and

haptic information in many virtual-reality, augmented-reality, or teleoperation systems.

Three studies were conducted to examine the spatial and temporal characteristic of

multisensory integration. Participants interacted with virtual springs using both visual and

haptic senses, and their perception of stiffness and ability to differentiate stiffness were

measured. The results revealed that a constant visual delay increased the perceived stiffness,

while a variable visual delay made participants depend more on the haptic sensations in

stiffness perception. We also found that participants judged stiffness stiffer when they

interact with virtual springs at faster speeds, and interaction speed was positively correlated

with stiffness overestimation. In addition, it has been found that participants could learn an

association between visual and haptic inputs despite the fact that they were spatially

separated, resulting in the improvement of typing performance. These results show the

limitations of Maximum-Likelihood Estimation model, suggesting that a Bayesian

inference model should be used.

Resilient acquisition of timely, detailed job site information plays a pivotal role in maintaining the productivity and safety of construction projects that have busy schedules, dynamic workspaces, and unexpected events. In the field, construction information acquisition often involves three types of activities including sensor-based inspection, manual inspection, and communication. Human interventions play critical roles in these three types of field information acquisition activities. A resilient information acquisition system is needed for safer and more productive construction. The use of various automation technologies could help improve human performance by proactively providing the needed knowledge of using equipment, improve the situation awareness in multi-person collaborations, and reduce the mental workload of operators and inspectors.

Unfortunately, limited studies consider human factors in automation techniques for construction field information acquisition. Fully utilization of the automation techniques requires a systematical synthesis of the interactions between human, tasks, and construction workspace to reduce the complexity of information acquisition tasks so that human can finish these tasks with reliability. Overall, such a synthesis of human factors in field data collection and analysis is paving the path towards “Human-Centered Automation” (HCA) in construction management. HCA could form a computational framework that supports resilient field data collection considering human factors and unexpected events on dynamic job sites.

This dissertation presented an HCA framework for resilient construction field information acquisition and results of examining three HCA approaches that support three use cases of construction field data collection and analysis. The first HCA approach is an automated data collection planning method that can assist 3D laser scan planning of construction inspectors to achieve comprehensive and efficient data collection. The second HCA approach is a Bayesian model-based approach that automatically aggregates the common sense of people from the internet to identify job site risks from a large number of job site pictures. The third HCA approach is an automatic communication protocol optimization approach that maximizes the team situation awareness of construction workers and leads to the early detection of workflow delays and critical path changes. Data collection and simulation experiments extensively validate these three HCA approaches.

This increasing role of highly automated and intelligent systems as team members has started a paradigm shift from human-human teaming to Human-Autonomy Teaming (HAT). However, moving from human-human teaming to HAT is challenging. Teamwork requires skills that are often missing in robots and synthetic agents. It is possible that adding a synthetic agent as a team member may lead teams to demonstrate different coordination patterns resulting in differences in team cognition and ultimately team effectiveness. The theory of Interactive Team Cognition (ITC) emphasizes the importance of team interaction behaviors over the collection of individual knowledge. In this dissertation, Nonlinear Dynamical Methods (NDMs) were applied to capture characteristics of overall team coordination and communication behaviors. The findings supported the hypothesis that coordination stability is related to team performance in a nonlinear manner with optimal performance associated with moderate stability coupled with flexibility. Thus, we need to build mechanisms in HATs to demonstrate moderately stable and flexible coordination behavior to achieve team-level goals under routine and novel task conditions.

The Internet is a major source of online news content. Online news is a form of large-scale narrative text with rich, complex contents that embed deep meanings (facts, strategic communication frames, and biases) for shaping and transitioning standards, values, attitudes, and beliefs of the masses. Currently, this body of narrative text remains untapped due—in large part—to human limitations. The human ability to comprehend rich text and extract hidden meanings is far superior to known computational algorithms but remains unscalable. In this research, computational treatment is given to online news framing for exposing a deeper level of expressivity coined “double subjectivity” as characterized by its cumulative amplification effects. A visual language is offered for extracting spatial and temporal dynamics of double subjectivity that may give insight into social influence about critical issues, such as environmental, economic, or political discourse. This research offers benefits of 1) scalability for processing hidden meanings in big data and 2) visibility of the entire network dynamics over time and space to give users insight into the current status and future trends of mass communication.

Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking.

This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.

Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.

Civilian and military use of remotely piloted aircraft (RPA) has significantly increased in recent years. Specifically, the United States Air Force (USAF) has an insatiable demand for RPA operations, that are responsible for fulfilling critical demands in every theater 24 hours a day, 365 days a year (United States Air Force, 2015). Around the clock operations have led to a manning shortage of RPA pilots in the USAF. The USAF MQ-9 “Reaper” Weapons School trains tactical experts and leaders of Airmen skilled in the art of integrated battle-space dominance (United States Air Force, 2015). Weapons Officers for the MQ-9 platform are also critically under-manned, with only 17% of allocated slots filled (B. Callahan, personal communication, January 28, 2016). Furthermore, the leading cause of training attrition has been attributed to lack of critical thinking and problem solving skills (B. Callahan, personal communication, January 28, 2016); skills not directly screened for prior to entering the RPA pilot career field. The proposed study seeks to discover patterns of student behaviors in the brief and debrief process in Weapons School, with the goal of identifying the competencies that distinguish the top students in Weapons School.

The goal of this experiment is to observe the relation between synchrony and performance in 3-person teams in a simulated Army medic training environment (i.e., Monitoring Extracting and Decoding Indicators of Cognitive workload: MEDIC). The cardiac measure Interbeat-Interval (IBI) was monitored during a physically oriented, and a cognitively oriented task. IBI was measured using NIRS (Near-Infrared Spectrology), and performance was measured using a team task score during a balance board and puzzle task. Synchrony has not previously been monitored across completely different tasks in the same experiment. I hypothesize that teams with high synchrony will show high performance on both tasks. Although no significant results were discovered by the correlational analysis, a trend was revealed that suggests there is a positive relationship between synchrony and performance. This study has contributed to the literature by monitoring physiological measures in a simulated team training environment, making suggestions for future research.