A data-integrated simulation model to evaluate nurse–patient assignments

A data-integrated simulation model to evaluate nurse–patient assignments

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Article ID: iaor200971204
Country: Netherlands
Volume: 12
Issue: 3
Start Page Number: 252
End Page Number: 268
Publication Date: Sep 2009
Journal: Health Care Management Science
Authors: , , , ,
Keywords: simulation: applications, programming: assignment
Abstract:

This research develops a novel data-integrated simulation to evaluate nurse–patient assignments (SIMNA) based on a real data set provided by a northeast Texas hospital. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree models, data mining tools for prediction and classification, were used to develop five tree structures: (a) four classification trees from which transition probabilities for nurse movements are determined, and (b) 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. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Results obtained from SIMNA to evaluate nurse–patient assignments in Medical/Surgical unit I of the northeast Texas hospital are discussed.

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