Emergency departments (ED) in the USA treat 136.9 million cases annually and account for nearly half of all medical care delivered. Due to high demand and limited resource (such as doctors and beds) availability, the median waiting time for ED patients is around 90 minutes. This research is motivated by a real-life case study of an ED located in central Missouri, USA, which faces the problem of congestion and improper workload distribution (e.g., overburdened ED doctors). The objective of this paper is to minimize patient waiting time and efficiently allocate workload among resources in an economical manner. The systematic framework of Business Process Reengineering (BPR), along with discrete-event simulation modeling approach, is employed to analyze current operations and potential improvement strategies. Alternative scenarios pertaining to process change, workforce planning, and capacity expansion are proposed. Besides process performance measures (waiting time and resource utilization), other criteria, such as responsiveness, cost of adoption, and associated risk, are also considered for evaluating an alternative. The experimental analysis indicates that a change in the triage process (evenly distributing medium-acuity patients among doctors and mid-level providers) is economical, easy to implement, reduces physician workload, and improves average waiting time by 20%, thereby making it attractive for short-term adoption. On the other hand, optimizing the workforce level based on historical demand patterns while adopting a triage process change delivers the best performance (84% reduction in waiting time and balanced resource utilization), and is recommended as a long-term solution.

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Although researchers have made various models of driving behavior, the behavior model under divided attention is not well studied. In this paper, the driver’s behavior differences under divided-attention were studied in a simulated driving environment. A driving scenario was developed to simulate hazards on the highway in dynamic driving conditions. Based on crash and non-crash cases through eye tracking videos from the experiment, Hierarchical task analysis (HTA) was conducted, and decomposed different complex driving behaviors into drivers’ perception, cognition, and decision. Also, their reaction times were compared by using the cognitive-perceptual model in GOMS. Through this study, different driving behaviors and corresponding cognitive factors, which contributed to a slower reaction were identified. The results from this study could be as a valuable input to develop advanced driver assistance systems which could provide smart collision warnings based on the driver’s attention.

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