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.

Effects of Probabilistic Risk Situation Awareness Tool (RSAT) on Aeronautical Weather-Hazard Decision Making.

We argue that providing cumulative risk as an estimate of the uncertainty in dynamically changing risky environments can help decision-makers meet mission-critical goals. Specifically, we constructed a simplified aviation-like weather decision-making task incorporating Next-Generation Radar (NEXRAD) images of convective weather. NEXRAD radar images provide information about geographically referenced precipitation. NEXRAD radar images are used by both pilots and laypeople to support decision-making about the level of risk posed by future weather-hazard movements. Using NEXRAD, people and professionals have to infer the uncertainty in the meteorological information to understand current hazards and extrapolate future conditions. Recent advancements in meteorology modeling afford the possibility of providing uncertainty information concerning hazardous weather for the current flight. Although there are systematic biases that plague people's use of uncertainty information, there is evidence that presenting forecast uncertainty can improve weather-related decision-making. The current study augments NEXRAD by providing flight-path risk, referred to as the Risk Situational Awareness Tool (RSAT). RSAT provides the probability that a route will come within 20 NMI radius (FAA recommended safety distance) of hazardous weather within the next 45 min of flight. The study evaluates four NEXRAD displays integrated with RSAT, providing varying levels of support. The "no" support condition has no RSAT (the NEXRAD only condition). The "baseline" support condition employs an RSAT whose accuracy is consistent with current capability in meteorological modeling. The "moderate" support condition applies an RSAT whose accuracy is likely at the top of what is achievable in meteorology in the near future. The "high" support condition provides a level of support that is likely unachievable in an aviation weather decision-making context without considerable technological innovation. The results indicate that the operators relied on the RSAT and improved their performance as a consequence. We discuss the implications of the findings for the safe introduction of probabilistic tools in future general aviation cockpits and other dynamic decision-making contexts. Moreover, we discuss how the results contribute to research in the fields of dynamic risk and uncertainty, risk situation awareness, cumulative risk, and risk communication.