Intensive Care Unit Operational Modeling and Analysis

Intensive Care Unit Operational Modeling and Analysis

Yue Dong (Mayo Clinic, USA), Huitian Lu (South Dakota State University, USA), Ognjen Gajic (Mayo Clinic, USA) and Brian Pickering (Mayo Clinic, USA)
DOI: 10.4018/978-1-60960-872-9.ch006
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Abstract

The outcome of critical illness depends not only on life threatening pathophysiologic disturbances, but also on several complex “system” dimensions: health care providers’ performance, organizational factors, environmental factors, family preferences and the interactions between each component. Systems engineering tools offer a novel approach which can facilitate a “systems understanding” of patient-environment interactions enabling advances in the science of healthcare delivery. Due to the complexity of operations in critical care medicine, certain assumptions are needed in order to understand system behavior. Patient variation and uncertainties underlying these assumptions present a challenge to investigators wishing to model and improve health care delivery processes. In this chapter we present a systems engineering approach to modeling critical care delivery using sepsis resuscitation as an example condition.
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Rationale For A Systems Approach To Reducing Error In The Icu

ICU systems are complex. The components of the system include patients, family members, physicians, nurses, allied health staff, support staff, physical and electronic infrastructure, equipment, supplies, processes and culture (Schmidt & Taylor, 1970). Ideally these components work together to optimize patient-centered outcomes. In order to ensure that ICU systems deliver high quality care to patients we need to understand how each system component (patient, provider, equipment, etc.) interacts and works together. This understanding is not easily derived from conventional analytic approaches and optimization of the system can not rely solely on single interventions such as those aimed at improving an individual provider’s skills. , Systems-based approaches to error prevention have proven markedly effective in reducing medical errors and iatrogenic complications. For example, Dr. Pronovost’s use and distribution of a simple checklist during central line insertion has already “saved more lives than that of any laboratory science discoveries in the past decade” (Gawande, 2007). Similarly, a simple systems intervention, the WHO Surgical Safety Checklist, has been shown to reduce morbidity and mortality in a diverse group of hospitals around the world (Haynes et al., 2009). These examples demonstrate that the systems of care delivery are an important determinant of critical illness outcomes.

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