A data-integrated simulation model to evaluate nurse-patient assignments.

Full 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.

Author information

Author/s: Sundaramoorthi, Durai (D); Chen, Victoria C P (VC); Rosenberger, Jay M (JM); Kim, Seoung Bum (SB); Buckley-Behan, Deborah F (DF);

Affiliation: Steven L. Craig School of Business, Missouri Western State University, Saint Joseph, USA. dsundaramoorthi(-atsign-)missouriwestern.edu

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Health care management science (Health Care Manag Sci), published in Netherlands. (Language: eng)

Reference: 2009-Sep; vol 12 (issue 3) : pp 252-68

Dates: Created 2009/09/10; Completed 2009/09/29;

PMID: 19739359, status: MEDLINE (last retrieval date: 9/29/2009, IMS Date: )

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