Timely and Accurate Decision-making During U.S. Public Health Emergencies: Incremental Dynamic Decision-making (IDD) for Public Health Emergency Response Open Access
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The current application of traditional public health decision-making and management practices during emergencies in the United States can result in adverse outcomes with negative impacts on the public, the environment or both. This dissertation identifies the various decision-making risk factors that contribute to these adverse outcomes and proposes an operational solution for timely and accurate public health decision-making during emergencies. The proposed solution and doctoral research product is the Incremental Dynamic Decision-making (IDD) model. The IDD process is a cyclical and incremental approach to adaptive decision-making during public health incidents. The IDD model is composed of a detailed process description and concept of operations applied to enhance the timeliness and accuracy of public health incident decision-making. IDD represents a potentially valuable tool in the evolving field of public health emergency management. This doctoral research 1) reviews the published literature on decision-making during public health emergencies to identify key decision-making risk factors that can adversely impact decision-making and response outcomes in public health emergencies; 2) reviews the published literature available from a range of different professional disciplines to identify decision-making strategies that have proven successful in dynamic situations with great uncertainty, urgency, and high stakes (i.e. incident conditions); 3) uses a systems engineering approach to develop a process-based and National Incident Management System (NIMS) compliant solution for timely and accurate decision-making during public health emergencies; and 4) assesses the validity of the IDD model by means of a highly structured case study method for analyzing 38 U.S. public health incident cases that occurred between October 2001 and September 2008. The research results yielded 1) quantitative descriptive data on the occurrence of both decision-making risk factors and successful decision-making strategies (i.e. decision-making best practices) observed during the `incident recognition' stage of a public health emergency and 2) qualitative case study data that to establish `proof of concept' for the IDD model.