A data-integrated nurse activity simulation model

Authors:
Durai Sundaramoorthi;Victoria C. P. Chen;Seoung B. Kim;Jay M. Rosenberger;Deborah F. Buckley-Behan
Affiliations:
The University of Texas at Arlington, Arlington, TX;The University of Texas at Arlington, Arlington, TX;The University of Texas at Arlington, Arlington, TX;The University of Texas at Arlington, Arlington, TX;The University of Texas at Arlington, Arlington, TX
Venue:
Proceedings of the 38th conference on Winter simulation
Year:
2006

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Abstract

This research develops a data-integrated approach for constructing simulation models based on a real data set provided by Baylor Regional Medical Center (Baylor) in Grapevine, Texas. Tree-based models and kernel density estimation were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree model, a data mining tool for prediction and classification, was used to develop two tree structures: (a) a regression tree, from which the amount of time a nurse spends in a location is predicted based on factors, such as the primary diagnosis of a patient and the type of nurse; and (b) a classification tree, from which transition probabilities for nurse movements are determined. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Merits of using our approach for Baylor's nurse activity simulation are discussed.