ABSTRACT

This paper was designed to determine if the Mahalanobis-Taguchi System (MTS) applied to the delirium-evidence-based bundle could detect medical patterns in retrospective datasets. The methodology defined the evidence-based bundle as a multidimensional 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 were calculated to determine the relative strength of detection of 23 delirium preindicators. This study discovered that the sufficient variation in the CAM-ICU assessment, the standard for delirium assessment, would benefit from knowledge of how different the MD is from the healthy average. The sensitivity of the detection system was 0.89 with a 95% confidence interval of between 0.84 and 0.92. The specificity of the detection system was 0.93 with a 95% confidence interval between 0.90 and 0.95. The MTS applied to the delirium-evidence-based bundle could detect medical patterns in retrospective datasets. The implication of this paper to a biomedical research is an automated decision support tool for the delirium-evidence-based bundle providing an early detection capability needed today.

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Scientists and universities strive to produce a large volume of high‐quality research. Universities supply the resources to enable scientific inquiry, such as the recently developed knowledge management (KM) tools. The use of KM platforms varies substantially in different environments. We examined the association between the use of KM tools and research productivity among four state universities in New York using analysis of variance techniques. We found a significant positive association. Important limitations are noted, and implications for both the study and the use of KM tools are discussed. Copyright © 2015 John Wiley & Sons, Ltd.