A Systems Engineering Approach to Early Detection in the Intensive Care Unit Open Access
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The research was designed to determine whether systems engineering tools applied to a medical screening system could detect medical patterns in retrospective data sets. The methodology defined an evidence-based bundle as a multi-dimensional system that conformed to a parameter diagram. The Mahalanobis distance (MD) was calculated for the retrospective healthy observations and the retrospective unhealthy observations. Signal-to-noise ratios (SNR) were calculated to determine the relative strength of detection of twenty-one delirium pre-indicators. The research discovered that sufficient variation in the Confusion Assessment Method for the intensive care unit, the standard for delirium assessment, would benefit from the knowledge of how different the MD is from the healthy average. The Mahalanobis Taguchi System (MTS) applied to the delirium evidence-based bundle could detect medical patterns in retrospective data sets. The implication of this research for systems engineering research is that a problem management support tool for the evidence-based delirium bundle, to provide an early detection capability, is needed today.