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

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

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dc.contributor.author Sundaramoorthi, Duraikannan en_US
dc.date.accessioned 2007-09-17T17:07:34Z
dc.date.available 2007-09-17T17:07:34Z
dc.date.issued 2007-09-17T17:07:34Z
dc.date.submitted August 2007 en_US
dc.identifier.other DISS-1824 en_US
dc.identifier.uri http://hdl.handle.net/10106/614
dc.description.abstract 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. en_US
dc.description.sponsorship Chen, Victoria en_US
dc.language.iso EN en_US
dc.publisher Industrial & Manufacturing Engineering en_US
dc.title A Data-integrated Simulation-based Optimization Approach For Nurse-patient Assignment en_US
dc.type Ph.D. en_US
dc.contributor.committeeChair Chen, Victoria en_US
dc.degree.department Industrial & Manufacturing Engineering en_US
dc.degree.discipline Industrial & Manufacturing Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level doctoral en_US
dc.degree.name Ph.D. en_US
dc.identifier.externalLink https://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=231
dc.identifier.externalLinkDescription Link to Research Profiles

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