Using classification and regression trees for a nurse activity simulation

This research develops a novel approach for constructing simulation models based on a real data set provided by Baylor Regional Medical Center (Baylor) in Grapevine, Texas. Kernel density estimates and trees were utilized to extract important knowledge about the workload of nurses from an encrypted data set provided by Baylor for four care units. The four units include two medical/surgical units, one mom/baby unit, and one high-risk labor-and-delivery unit. Classification and Regression Trees, a data mining tool for prediction and classification, was applied to the Baylor data 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. Application of this approach for simulation-based nurse-patient assignments is discussed.