A data-integrated simulation model to evaluate nurse-patient assignments.
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.
Sundaramoorthi
Durai
D
Steven L. Craig School of Business, Missouri Western State University, Saint Joseph, USA. dsundaramoorthi@missouriwestern.edu
Chen
Victoria C P
VC
Rosenberger
Jay M
JM
Kim
Seoung Bum
SB
Buckley-Behan
Deborah F
DF
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