Dissertation/Thesis Abstract

A hybrid methodology for modeling risk of adverse events in complex healthcare settings
by Kazemi-Tabriz, Reza, Ph.D., University of Maryland, College Park, 2011, 303; 3478953
Abstract (Summary)

Despite efforts to provide safe, effective medical care, adverse events still occur with some regularity. While risk cannot be entirely eliminated from healthcare activities, an important goal is to develop effective and durable mitigation strategies to render the system ‘safer’. In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the healthcare domain, this can be extremely challenging due to the wide variability in the way that healthcare processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study we have developed a generic methodology for evaluating dynamic changes in adverse event risk in acute care hospitals as a function of organizational and non-organizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational level and policy level contributions to risk evolve over time, and how policies and decisions may affect the general system-level contribution to adverse event risk. It also captures the feedback of organizational factors and decisions over time and the non-linearities in these feedback effects. Second, Bayesian Belief Network (BBN) framework is used to represent patient-level factors and also physician level decisions and factors in the management of an individual patient, which contribute to the risk of hospital-acquired adverse event. The model is intended to support hospital decisions with regards to staffing, length of stay, and investment in safeties, which evolve dynamically over time. The methodology has been applied in modeling the two types of common adverse events; pressure ulcers and vascular catheter-associated infection , and has been validated with eight years of clinical data.

Indexing (document details)
Advisor: Mosleh, Ali, Dierks, Meghan
Commitee: BAECHER, GREGORY, MODARRES, MOHAMMAD, ROUSH, MARVIN, SPRINKLE, ROBERT
School: University of Maryland, College Park
Department: Reliability Engineering
School Location: United States -- Maryland
Source: DAI-B 73/02, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Biomedical engineering, Health care management, Systems science
Keywords: Adverse events, Bayesian belief networks, Healthcare risk, Line infection, Pressure ulcer, Risk analysis
Publication Number: 3478953
ISBN: 9781124970004
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