A Data-integrated Simulation-based Optimization Approach For Nurse-patient Assignment

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.