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This research develops a novel data -integrated simulation to evaluate nurse -patient assignments (SIMNA ) 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 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 Baylor are discussed . With the aid of SIMNA , in addition to evaluating assignments at the beginning of a shift , two policies named OPT and HEU are introduced to make nurse -patient assignments for patient admits during a shift . Results from fifty problems created with different initial assignments to evaluate the policies are presented . |
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