title: A data-integrated simulation model to evaluate nurse-patient assignments.
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.
authors.affiliation: Steven L. Craig School of Business, Missouri Western State University, Saint Joseph, USA. dsundaramoorthi@missouriwestern.edu
authors.forename: Durai
authors.initials: D
authors.lastname: Sundaramoorthi
authors.forename: Victoria C P
authors.initials: VC
authors.lastname: Chen
authors.forename: Jay M
authors.initials: JM
authors.lastname: Rosenberger
authors.forename: Seoung Bum
authors.initials: SB
authors.lastname: Kim
authors.forename: Deborah F
authors.initials: DF
authors.lastname: Buckley-Behan
mesh_terms: D003663:Decision Trees; D006801:Humans; D008962:Models, Theoretical; D009741:Nursing Staff, Hospital / Q000458:organization & administration*; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration*; D010562:Personnel Staffing and Scheduling Information Systems