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

Durai Sundaramoorthi, Victoria C.P. Chen, Jay M. Rosenberger, Seoung Bum Kim, Deborah F. Buckley-Behan

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)252-268
Number of pages17
JournalHealth Care Management Science
Volume12
Issue number3
DOIs
Publication statusPublished - 2009 Jul
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgements This research was supported by the Robert Wood Johnson Foundation grant number 053963. We thank Terry Clark from the northeast Texas hospital and Patricia G. Turpin from the School of Nursing at The University of Texas at Arlington, for providing us data for this research.

Keywords

  • Nurse workload
  • Nurse-patient assignment
  • Simulation

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • General Health Professions

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