Learning from accident and error: avoiding the hazards of workload, stress, and routine interruptions in the emergency department

Abstract

This article presents a model of how a build-up of interruptions can shift the dynamics of the emergency department (ED) from an adaptive, self-regulating system into a fragile, crisis-prone one. Drawing on case studies of organizational disasters and insights from the theory of high-reliability organizations, the authors use computer simulations to show how the accumulation of small interruptions could have disproportionately large effects in the ED. In the face of a mounting workload created by interruptions, EDs, like other organizational systems, have tipping points, thresholds beyond which a vicious cycle can lead rather quickly to the collapse of normal operating routines and in the extreme to a crisis of organizational paralysis. The authors discuss some possible implications for emergency medicine, emphasizing the potential threat from routine, non-novel demands on EDs and raising the concern that EDs are operating closer to the precipitous edge of crisis as ED crowding exacerbates the problem.

Figure 1

A model of disaster dynamics. Reprinted from “Disaster Dynamics: Understanding the Role of Quantity in Organizational Collapse,” by Rudolph and Repenning, published in Administrative Science Quarterly (Vol. 47, 2002) by permission of Administrative Science Quarterly.

Figure 2

Response to a temporary surge in interruption arrivals. Reprinted from “Disaster Dynamics: Understanding the Role of Quantity in Organizational Collapse,” by Rudolph and Repenning, published in Administrative Science Quarterly (Vol. 47, 2002) by permission of Administrative Science Quarterly.

Figure 3

Response to variability in the stream of interruption arrivals. Reprinted from “Disaster Dynamics: Understanding the Role of Quantity in Organizational Collapse,” by Rudolph and Repenning, published in Administrative Science Quarterly (Vol. 47, 2002) by permission of Administrative Science Quarterly.

Figure 4

Location of the tipping point under two scenarios for baseline system utilization. Adapted from Rudolph and Repenning.