Distributed Situation Awareness Framework to Assess and Design Complex Systems

Communication and coordination in complex sociotechnical systems require continuous assessment on its artefacts and how they are utilized to improve system performance. Situation Awareness (SA) is considered as a fundamental concept in designing and understanding interactions between human and non-human agents (i.e., information systems) that impact system performance. The interaction efficiency is partly determined by quality of information or SA distributed across agents to ensure the accuracy of decision making and resource allocations. Disrupting SA distribution between agents can significantly affect operations of the system with financial and safety consequences.

This research applied the Distributed Situation Awareness (DSA) theory to study and improve patient flow management. The main objective of this research was to advance methodology in the DSA literature for (1) deriving design implications from DSA models, and (2) developing quantitative DSA models to formally compare system designs. This DSA research was situated in the domain of patient flow management. Data were collected using the three-part method of data elicitation, extraction, and representation to investigate DSA at a patient flow command and control center at Carilion Clinic in Roanoke, VA. The data used were elicited from observations and interviews on workers daily activities and available historical database (i.e., TeleTracking). Then, data were represented into a combined network to highlight social, task and knowledge elements in patient flows for studying and assessing patient flow management.

The influence of the DSA on complex systems was examined qualitatively and quantitatively. The DSA combined network qualitatively characterized patient flow management and identified deficiencies of the command-and-control center functions. The network characterized admission, clinical (inside-hospital) transportation, discharge, and environmental services functions managed by Carilion Transfer and Communications Center (CTaC). These characterizations led to the identification of design principles on job roles, tasks performed, and SA transactions and distribution adopted by the state-of-the-art patient flow management facility. In addition, the network representing the current operation of CTaC illustrated the connection between functional groups, arbitration of resources, and job roles that could become the bottlenecks in transmitting SA. The network also helped identify inefficient task loops, which resulted in delay due to missing/poor SA, and task orders that could be modified to improve the patient flow and thus reduce the likelihood of delay.

The qualitative (i.e., combined network) model was partially translated into a quantitative model based on discrete event simulation (DES) and agent-based modeling (ABM) to simulate patient transportation inside the hospital. The simulation model consisted of 28 patient origins, 29 equipment origins, 12 destinations, and more than 200 entities (i.e., simulation objects). The model was validated by lack of significant difference on various outcome metrics between 100 simulation replications and historical data using one-way t-tests. The simulation model captured the distribution and transactions of knowledge elements between agents within the modeled processes. Further, the model successfully verified the deficiencies in the existing system (i.e., delay and cancelation), attributing various instances of deficiency to be either SA related or non-SA related.

The simulation model tested two interventions for eliminating SA deficiencies revealed by the qualitative model: (1) updating the wards nurse before picking up patients from inpatient floor, and (2) updating the X-ray nurse/team before arriving with the patient. Both interventions involved updates from the transporters to nurses, transmitting SA on the estimated time of arrival and patient information for the nurse to become aware of the transport status. The simulation ran for 1500 replications for results on transport time and cancellation rate on these two interventions. One-way t-tests revealed that the intervention to update the wards nurse resulted in significant reductions in mean transport and cancellation rate time compared to historical data (i.e., TeleTracking), yielding 0.42 minutes to 1.24 minutes reduction in transport time and 2% to 5% less cancelations. However, the second intervention resulted in a significant increase in transport time and thus was ineffective.

DES and ABM supplemented the qualitative modeling with quantitative evidence on DSA concepts and assessment of potential interventions for improving DSA in patient flow management. Specifically, the DES and ABM enabled comparison and prediction of performance outcome from recommended changes to communication protocols. These findings indicate that DSA is a promising framework for analyzing communication and coordination in complex systems and assessing improvement on SA design quantitatively.