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Effects on Long-Term Care Hospital Staff Mixing Level after Implementing Differentiated Inpatient Nursing Fees by Staffing Grades

Donghwan Kim1, Hanju Lee2
Journal of Korean Academy of Nursing Administration 2014;20(1):95-105.
Published online: January 29, 2014

1Health Insurance Review and Assessment Service, Korea.

2Department of Nursing, Sangmyung University, Korea.

Corresponding author: Lee, Hanju. Department of Nursing, Sangmyung University, 300 Anseo-dong, Dongnam-gu, Cheonan 330-720, Korea. Tel: +82-41-550-5429, Fax: +82-41-550-5545, dalbich@smu.ac.kr
• Received: November 9, 2013   • Revised: January 2, 2014   • Accepted: January 7, 2014

Copyright © 2014 Korean Academy of Nursing Administration

This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Purpose
    The purpose of this study was to examine trends in number of nursing staff and skill mix.
  • Methods
    Nursing staff and skill mix were measured using the number of nursing staff including nurse aids and registered nurses per bed. Descriptive and panel data regression analyses were conducted using data on long-term care hospitals which included yearly series data from 2006 to 2010 for 119 hospitals.
  • Results
    The number of nursing staff per bed increased significantly but percentage of registered nurses decreased significantly from 2007 to 2010. The regression model explained this variation as much as 35% and 44%.
  • Conclusion
    The results showed that in long-term care hospitals there were more nurse aids employed instead of registered nurses after the implemention of differentiated inpatient nursing fees. Thus clarifying the job descriptions for nurses and nurse aids is needed and appropriate hospital incentive policies should be implemented.
  • Key Words: Inpatient nursing fees; Long-term care hospital; Staff mix level

This research was supported by the Health Insurance Review and Assessment Service.

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Table 1
General Characteristics of Long-term Care Hospitals (N=119)
jkana-20-95-i001.jpg

*unit: 1,000 won.

Table 2
Correlation of Independent Variables
jkana-20-95-i002.jpg
Table 3
Change in Number of Nursing Staff and Rate of Registered Nurses (Time Fixed Effect Model)
jkana-20-95-i003.jpg
Table 4
Change in Number of Nursing Staff and Rate of Registered Nurses (Group Fixed Effect Model)
jkana-20-95-i004.jpg

Figure & Data

References

    Citations

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    Effects on Long-Term Care Hospital Staff Mixing Level after Implementing Differentiated Inpatient Nursing Fees by Staffing Grades
    J Korean Acad Nurs Adm. 2014;20(1):95-105.   Published online January 31, 2014
    DOI: https://doi.org/10.11111/jkana.2014.20.1.95
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    Effects on Long-Term Care Hospital Staff Mixing Level after Implementing Differentiated Inpatient Nursing Fees by Staffing Grades
    J Korean Acad Nurs Adm. 2014;20(1):95-105.   Published online January 31, 2014
    DOI: https://doi.org/10.11111/jkana.2014.20.1.95
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    Effects on Long-Term Care Hospital Staff Mixing Level after Implementing Differentiated Inpatient Nursing Fees by Staffing Grades
    Effects on Long-Term Care Hospital Staff Mixing Level after Implementing Differentiated Inpatient Nursing Fees by Staffing Grades

    General Characteristics of Long-term Care Hospitals (N=119)

    *unit: 1,000 won.

    Correlation of Independent Variables

    Change in Number of Nursing Staff and Rate of Registered Nurses (Time Fixed Effect Model)

    Change in Number of Nursing Staff and Rate of Registered Nurses (Group Fixed Effect Model)
    Table 1 General Characteristics of Long-term Care Hospitals (N=119)

    *unit: 1,000 won.

    Table 2 Correlation of Independent Variables

    Table 3 Change in Number of Nursing Staff and Rate of Registered Nurses (Time Fixed Effect Model)

    Table 4 Change in Number of Nursing Staff and Rate of Registered Nurses (Group Fixed Effect Model)

    Table 1

    Table 2

    Table 3

    Table 4

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