Although the consequences of hospital ED crowding have been studied extensively, the causes of crowding are still not well understood. Throughput factors in ED crowding models are difficult to study in a controlled fashion in a dynamic environment where healthcare demand changes rapidly, and physical and human resources suddenly become limited. Opportunities for automated, simultaneous, and low-cost observation of the location and movement of multiple units, patients and staff have recently arisen with the introduction of small, non-intrusive real-time location systems (RTLS). One such RTLS deployment reported here has initiated renewed consideration of quality and industrial statistics as applied to healthcare operations management. Novel metrics for essential constructs of throughput factors in ED crowding such as efficiency and effectiveness are proposed. In particular, causality is explained in terms of understanding of each construct, modeled in terms of entropy, information, and order. Experimental demonstration is given of how labor reduction and the probability of patients, personnel and equipment meeting in terms of less uncertainty can be explained. These novel metrics are expected to facilitate monitoring of how an ED reacts to different levels of crowding, provide insight into crowding dynamics, help evaluate interventions to decrease crowding, and ultimately improve care. © 2021 The Author(s).