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A supply model for nurse workforce projection in Malaysia

Author

Listed:
  • Zuraida Abal Abas

    (Universiti Teknikal Malaysia Melaka)

  • Mohamad Raziff Ramli

    (Universiti Teknikal Malaysia Melaka)

  • Mohamad Ishak Desa

    (Universiti Teknikal Malaysia Melaka)

  • Nordin Saleh

    (Planning Division, Ministry of Health Malaysia)

  • Ainul Nadziha Hanafiah

    (Planning Division, Ministry of Health Malaysia)

  • Nuraini Aziz

    (Planning Division, Ministry of Health Malaysia)

  • Zaheera Zainal Abidin

    (Universiti Teknikal Malaysia Melaka)

  • Abdul Samad Shibghatullah

    (Universiti Teknikal Malaysia Melaka)

  • Ahmad Fadzli Nizam Abdul Rahman

    (Universiti Teknikal Malaysia Melaka)

  • Haslinda Musa

    (Universiti Teknikal Malaysia Melaka)

Abstract

The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.

Suggested Citation

  • Zuraida Abal Abas & Mohamad Raziff Ramli & Mohamad Ishak Desa & Nordin Saleh & Ainul Nadziha Hanafiah & Nuraini Aziz & Zaheera Zainal Abidin & Abdul Samad Shibghatullah & Ahmad Fadzli Nizam Abdul Rahm, 2018. "A supply model for nurse workforce projection in Malaysia," Health Care Management Science, Springer, vol. 21(4), pages 573-586, December.
    • Handle: RePEc:kap:hcarem:v:21:y:2018:i:4:d:10.1007_s10729-017-9413-7
    DOI: 10.1007/s10729-017-9413-7
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    References listed on IDEAS

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    1. Tomblin Murphy, Gail & Kephart, George & Lethbridge, Lynn & O'Brien-Pallas, Linda & Birch, Stephen, 2009. "Planning for what? Challenging the assumptions of health human resources planning," Health Policy, Elsevier, vol. 92(2-3), pages 225-233, October.
    2. Tomoko Ono & Gaétan Lafortune & Michael Schoenstein, 2013. "Health Workforce Planning in OECD Countries: A Review of 26 Projection Models from 18 Countries," OECD Health Working Papers 62, OECD Publishing.
    3. Agrawal, Rashmi & Nanda, S.K. & Rao, D. Rama & Rao, B.V.L.N., 2013. "Integrated Approach to Human Resource Forecasting: An Exercise in Agricultural Sector," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 26(2).
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    Cited by:

    1. Negar Darabi & Niyousha Hosseinichimeh, 2020. "System dynamics modeling in health and medicine: a systematic literature review," System Dynamics Review, System Dynamics Society, vol. 36(1), pages 29-73, January.
    2. Mohammad Reza Davahli & Waldemar Karwowski & Redha Taiar, 2020. "A System Dynamics Simulation Applied to Healthcare: A Systematic Review," IJERPH, MDPI, vol. 17(16), pages 1-27, August.
    3. Shaker Ardakani, Elham & Gilani Larimi, Niloofar & Oveysi Nejad, Maryam & Madani Hosseini, Mahsa & Zargoush, Manaf, 2023. "A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources," Omega, Elsevier, vol. 114(C).

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