A Retrospective Analysis of Emergency Department Patients who Leave Without Being Seen (LWBS) and Design and Simulation of a Rapid Medical Evaluation Framework; Radiation Overdoses in Computed Tomography (CT): An Actor-Network Theory Analysis of the 2008-09 Cedars-Sinai Hospital CT Brain Perfusion Radiation Overdoses

My STS research and technical project are related through their analysis of different factors that lead to potentially adverse medical outcomes. The two projects differ in how they analyze these factors, as the STS research takes a qualitative approach, and the technical project takes a quantitative approach. My STS research uses actor-network theory (ANT) to qualitatively explain how multiple factors contributed to the destabilization of the network that provides computed tomography perfusion (CTP) imaging for patients at Cedars-Sinai Hospital, resulting in radiation overdoses. My technical project involves performing a quantitative analysis of patient acuity for those who return to the emergency department (ED) after leaving without being seen (LWBS) by a medical provider. While my technical project and STS research both approach analyzing factors that lead to adverse medical outcomes, they examine specific cases at different hospitals where patient care is adversely impacted.

The technical project involves construction of a data analysis algorithm to address the problem of being unable to easily evaluate ED LWBS data in a timely fashion. The algorithm uses anonymized patient data from the electronic health record (EHR) for all University of Virginia (UVA) ED patients who LWBS and had a subsequent ED visit within 48 hours for the same chief complaint. The algorithm performs a paired analysis for a change in quantitative patient acuity and evaluates the admission rate for this population against the overall UVA ED admission rate. The findings from this analysis were applied in the design of a framework for the rapid medical evaluation (RME) area of the ED to reduce LWBS rates. A discrete event simulation (DES) model for this framework was designed and developed in MATLAB to evaluate the effect of implementing the RME framework on LWBS rates prior to its introduction in the ED on a pilot trial basis.

Furthermore, the STS research uses ANT to argue that a missing hospital policy as a sole actor is an insufficient explanation for the destabilization of the CTP imaging network at Cedars-Sinai Hospital that led to the hundreds of radiation overdoses in 2008. This research examines evidence of multiple simultaneous failures at the hospital from the California Department of Health Services’ (CDHS) investigative report and the Food & Drug Administration’s (FDA) statements to show that the CTP overdoses were complex and multifactorial. The findings from this analysis suggest that the outcomes of events are frequently better explained through analysis of the complex interactions of several actors and their cumulative effects on the network and its stability.

Simultaneously pursuing the technical and STS projects was advantageous because it enables the STS projects’ insights of several actors interacting to destabilize a network to be applied to the technical project’s analysis of factors leading to patients LWBS. Additionally, careful consideration of the types of health data in the EHR was necessary to design the analysis algorithm in the technical project. Ultimately, considering both the technical and social aspects of a problem simultaneously enabled the RME framework to be more robust. It is critical for engineers to understand the convoluted relationship between technical design and social factors to synthesize an optimal end product, as their designs exist in a society with innumerable actors and not in a vacuum.

School of Engineering and Applied Science
Bachelor of Science in Biomedical Engineering

Technical Advisor: Mary Murray, MD
STS Advisor: Benjamin Laugelli, PhD