During a given incident, dispatchers are under high stress balancing the workload of listening, understanding, recording, and responding to an unfolding event. These personnel work in a high stakes environment, where seconds can mean life and death. They are called upon to be the voice of reason and calm during traumatic events, and must follow policy and procedures in effectively communicating event information to a number of different user groups. The goal of this work was to demonstrate how a predictive workload equation can be used to evaluate cognitive workload of a dispatcher during a representative call, thus providing insights into where, when and why periods of high workload/overload may occur. The use case presented here included an active shooter on a US military base that transitioned to a suspicious package incident. Utilizing insights from predictive workload equations, there is an opportunity to enhance the operator workflow through improved system design and integration such that the dispatcher is able to effectively work at the speed of the unfolding event, maintaining communications and documentation throughout.
Emergency call centers are safety critical systems. Any failure in the system can lead to loss of life or damage to infrastructure.1 Dispatchers are key in maintaining operational effectiveness and efficiency of these centers, holding a significant role in emergency response as gateways to responders and resources. Dispatchers first are positioned to gather critical incident information from individuals on scene, which can present a challenge as these individuals are often in active distress and cannot provide completely accurate information. Furthermore, dispatchers are responsible for continued communications to emergency response personnel as an event unfolds, updating incident and response details fluidly to document and provide shared awareness to all involved. Due to the criticality of their task, it is essential that dispatcher tools and systems support an efficient workflow that does not hinder or add unnecessary cognitive burden to their work. Such hindrances combined with sheer information volume can result in significant errors to emergency response planning and execution.
This article presents common definitions of workload in terms of psychological models, reviews cognitive aspects of the dispatcher role, and identifies qualitative and quantitative metrics used to evaluate dispatcher workload. A use case based on an exercise executed at Wright Patterson Air Force Base (WPAFB) Fire Dispatch is summarized, providing an example of how dynamic, predictive workload calculations can be used to identify periods of potential cognitive overload, which may impact response effectiveness and efficiency. Analysis of these peaks of high workload/overload can identify opportunities for improvement in human-systems engineering such as adapting the workflow or improving system design to support user tasks. The exercise use case focused on an active shooter that transitioned into a bomb threat scenario, and involved multiple responding agencies from on base and surrounding areas.
Cognitive Load is a term that describes how much mental effort is needed to work on a task. The mind has a finite amount of attention and information processing capacity that it can give at any time, and cognitive load helps to describe how much of those resources are being consumed by ongoing tasks. Current understanding of cognition is that humans actively attend to and process information using distinct, coordinated components of memory.
- Knight, J.C. (2002). Safety critical systems: challenges and directions. Proceedings of the 24th International Conference on Software Engineering. Association for Computing Machinery: New York, NY. doi: 10.1145/581339.581406
- Baddeley A. The episodic buffer: a new component of working memory? Trends in Cognitive Sciences. 2000;4(11):417-423. doi:10.1016/s1364-6613(00)01538-2
- Miller GA. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review. 1994;101(2):343-352. doi:10.1037/0033-295x.101.2.343
- Yin B, Chen F. Towards automatic cognitive load measurement from speech analysis. Human-Computer Interaction Interaction Design and Usability. 2007;4550(HCI 2007):1011-1020. doi:https://doi.org/10.1007/978-3-540-73105-4_111
- Wickens CD, Davies R, Parasuraman R. Processing resources in attention. In: Varieties of Attention. New York: Academic Press; 1985:63-101.
- Wickens CD. Multiple resources and mental workload. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2008;50(3):449-455. doi:10.1518/001872008x288394
- Stanney K, Samman S, Reeves L, et al. A paradigm shift in interactive computing: deriving multimodal design principles from behavioral and neurological foundations. International Journal of Human-Computer Interaction. 2004;17(2):229-257. doi:10.1207/s15327590ijhc1702_7
- Miller G.A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63, 81-97. https://doi.org/10.1037/h0043158
- North RA, Riley VA. W/index: a predictive model of operator workload. Applications of Human Performance Models to System Design. 1989:81-89. doi:10.1007/978-1-4757-9244-7_6
- Mitchell D.K. (2000). Mental workload and ARL workload modeling tools. ARL report ARL-TN-161. Available online at: https://apps.dtic.mil/sti/pdfs/ADA377300.pdf.