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

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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.

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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.

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Authors and Affiliations

  1. Steven L. Craig School of Business, Missouri Western State University, Saint Joseph, USA

    Durai Sundaramoorthi

  2. Department of Industrial & Manufacturing Systems Engineering, University of Texas at Arlington, Arlington, USA

    Victoria C. P. Chen, Jay M. Rosenberger & Seoung Bum Kim

  3. School of Nursing, University of Texas at Arlington, Arlington, USA

    Deborah F. Buckley-Behan

Authors
  1. Durai Sundaramoorthi
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  2. Victoria C. P. Chen
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  3. Jay M. Rosenberger
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  4. Seoung Bum Kim
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  5. Deborah F. Buckley-Behan
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Correspondence to Durai Sundaramoorthi.

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Sundaramoorthi, D., Chen, V.C.P., Rosenberger, J.M. et al. A data-integrated simulation model to evaluate nurse–patient assignments. Health Care Manag Sci 12, 252–268 (2009). https://doi.org/10.1007/s10729-008-9090-7

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