Modeling Task Scheduling Decisions of Emergency Department Physicians

OBJECTIVE

This study evaluated task-scheduling decisions in the context of emergency departments by comparing patterns of emergency physicians' task-scheduling models across levels of experience.

BACKGROUND

Task attributes (priority, difficulty, salience, and engagement) influence task-scheduling decisions. However, it is unclear how attributes interact to affect decisions, especially in complex contexts. An existing model of task scheduling, strategic task overload management-no priority (STOM-NP), found that an equal weighting of attributes can predict task-scheduling behavior. Alternatively, mathematical modeling estimated that priority alone could make similar predictions as STOM-NP in a parsimonious manner. Experience level may also influence scheduling decisions.

METHOD

An experimental design methodology shortened a judgment analysis approach to compare a priori task-scheduling decision strategies. Emergency physicians with two levels of experience rank-ordered 10 sets of 3 tasks varying on 4 task attributes in this complex environment.

RESULTS

Bayesian statistics were used to identify best-fit decision strategies. STOM-NP and priority-only provided the best model fits. STOM-NP fit the lower-experienced physicians best, whereas priority-only-using only one cue-fit the higher-experienced physicians best.

CONCLUSION

Models of decision strategies for task-scheduling decisions were extended to complex environments. Experts' level of experience influenced task-scheduling decisions, where the scheduling decisions of more-experienced experts was consistent with a more frugal decision process. Findings have implications for training and evaluation.

APPLICATION

We assessed models of cues that influence task-scheduling decisions, including a parsimonious model for task priority only. We provided a sample approach for shortening methods for understanding decisions.

The Strategic Task Overload Model: History, Status, Challenges, and Extensions into New Domains

In this symposium we present the history, current status and challenges of the Strategic Task Overload Management (STOM) model of task switching, positing that parameters of priority, interest difficulty, salience and time-on-task are the factors that drive switching. We present four papers examining extensions of the model. In the first presentation, Wickens & Gutzwiller describe in detail the nature and background of the five parameters and their roots in models of scanning. In the second presentation, Gutzwiller & Sitzman consider the effects of multitasking and priority on task selection. Then Gilbert & Wickens show the extension of STOM to business tasks by presenting results of an experiment that systematically evaluate the role of task priority and goal setting. In the final presentation, Barg-Walkow & Rogers demonstrate the relevance of STOM to vitally important task management in the emergency room.

Modeling Task Scheduling in Complex Healthcare Environments: Identifying Relevant Factors

Multiple task coordination involves scheduling tasks, completing tasks, and integrating tasks into a workflow. Task scheduling can influence outcomes of safety, satisfaction, and efficiency when completing tasks. This is especially important in complex life-critical environments such as healthcare, which incurs many situations where there are multiple tasks and limited resources for addressing all tasks. One approach for understanding tasks coordination is the Strategic Task Overload Management (STOM) model, which is a model for task scheduling behavior. In this theoretical paper, we discuss how this model can be extended to a complex healthcare environment. There are additional considerations (e.g., time) which must be considered when applying this model to healthcare. Ultimately, understanding how emergency physicians make multiple task scheduling decisions will advance theories and models, such as STOM, which can then in turn be implemented to improve scheduling behaviors in complex healthcare environments.

Space Exploration Illuminates the Next Frontier for Teams Research

Effective teamwork is beneficial for organizations on Earth, but is a sine qua non for teams venturing into outer space. The prospect of sending a team to Mars by the year 2030 invites organizational scientists to take stock of what we know and what we still need to know about teams. The team endeavoring to Mars will be multicultural and interdisciplinary, living and working in uncomfortable and dangerous conditions, and doing so in close collaboration with distant teams back on Earth. Tackling the teamwork challenges associated with a mission to Mars present an opportunity to rapidly accelerate the science of teams. In this conceptual review, we explore seven complexities of teams that are both important and understudied. Results of structured interviews with experts on human space exploration regarding the nature of teamwork in long-duration space exploration illuminate seven complexities, or key features of teams, in general, that serve as a catalyst for identifying, informing, and motivating future directions of inquiry about teams. These features, and the research they inspire, may enable organizations to build more effective teams on Earth and beyond.

Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching

OBJECTIVE

The aim of this study was to validate the strategic task overload management (STOM) model that predicts task switching when concurrence is impossible.

BACKGROUND

The STOM model predicts that in overload, tasks will be switched to, to the extent that they are attractive on task attributes of high priority, interest, and salience and low difficulty. But more-difficult tasks are less likely to be switched away from once they are being performed.

METHOD

In Experiment 1, participants performed four tasks of the Multi-Attribute Task Battery and provided task-switching data to inform the role of difficulty and priority. In Experiment 2, participants concurrently performed an environmental control task and a robotic arm simulation. Workload was varied by automation of arm movement and both the phases of environmental control and existence of decision support for fault management. Attention to the two tasks was measured using a head tracker.

RESULTS

Experiment 1 revealed the lack of influence of task priority and confirmed the differing roles of task difficulty. In Experiment 2, the percentage attention allocation across the eight conditions was predicted by the STOM model when participants rated the four attributes. Model predictions were compared against empirical data and accounted for over 95% of variance in task allocation. More-difficult tasks were performed longer than easier tasks. Task priority does not influence allocation.

CONCLUSIONS

The multiattribute decision model provided a good fit to the data.

APPLICATIONS

The STOM model is useful for predicting cognitive tunneling given that human-in-the-loop simulation is time-consuming and expensive.

State of science: mental workload in ergonomics

Mental workload (MWL) is one of the most widely used concepts in ergonomics and human factors and represents a topic of increasing importance. Since modern technology in many working environments imposes ever more cognitive demands upon operators while physical demands diminish, understanding how MWL impinges on performance is increasingly critical. Yet, MWL is also one of the most nebulous concepts, with numerous definitions and dimensions associated with it. Moreover, MWL research has had a tendency to focus on complex, often safety-critical systems (e.g. transport, process control). Here we provide a general overview of the current state of affairs regarding the understanding, measurement and application of MWL in the design of complex systems over the last three decades. We conclude by discussing contemporary challenges for applied research, such as the interaction between cognitive workload and physical workload, and the quantification of workload 'redlines' which specify when operators are approaching or exceeding their performance tolerances.

Effort in human factors performance and decision making

OBJECTIVE

The aim of this study was to demonstrate the importance of effort in human factors.

BACKGROUND

Effort has made its appearance in several diverse formats and applications. Eight of these are integrated in the current writing related to learning, looking, task switching, visual search termination, information access, choosing decision strategies, and behaving safely.

METHOD

This is based upon a literature review.

RESULTS

The common elements of these different effort applications are highlighted, particularly, their manifestations in either implicit or explicit expected value decisions.

CONCLUSIONS

There is a need to show how the metrics of effort and workload assessment influence decisions in human factors, particularly, those related to safety.

Effects of sleep restriction, sleep inertia, and overload on complex cognitive performance before and after workload transition

We describe performance models and meta-analytic data in a period before and after an unexpected workload transition typical, for example, of a catastrophic automation failure, experienced by a fatigued operator. We first present meta-analytic findings on the effects of sleep restriction and sleep inertia on the complex cognitive performance that might typify such a “failure response”. We then present a workload overload task management model whose parameters are based on meta-analyses, relevant to predicting multi-task performance in the post-transition phase performance (independent of sleep disruption effects). We cite the lack of research intersecting these two areas.

Workload overload modeling

Task switching choice was examined building from a model of task overload management. An experiment using the Multi-Attribute Task Battery (MATB) was undertaken to explore the influence of two parameters of the model, task priority and task difficulty. Participants were free to switch between the four component tasks, with the number of switches and task choice for conflicting events observed. A unique post-experiment survey measured subjective ratings of task attributes. We found that task difficulty, by reducing switching, and task priority, which determined whether increased task difficulty increased time in task, significantly influenced task switching predominantly in line with our predictions. The specific role of priority in multi-task management, and future directions including time-on-task related effects and the role of operator fatigue, are discussed.

Using Empirical Research and Computational Modeling to Predict Operator Response to Unexpected Events

This paper describes an effort to model and predict astronaut performance during sudden workload transitions in long duration missions. Our approach to the work is heavily based on empirical research. We have performed a set of meta-analyses 1) to identify the quantitative effects of poor sleep on task accuracy and task completion time, and 2) to develop a model of operator task selection during multitasking. We are currently developing a model, based on a literature review, to predict the effects of automation design factors on operator task performance. This paper gives an overview of the project, presents the overall model of operator performance during a workload transition, and describes the empirical and theoretical underpinnings of a model that predicts the effects of automation design on operator performance.