System dynamics modeling for general practitioner workforce forecasting in Kazakhstan

System dynamics modeling for general practitioner workforce forecasting in Kazakhstan

Authors

  • B. Koichubekov
  • A. Kharin
  • M. Sorokina
  • I. Korshukov
  • B. Omarkulov

Keywords:

Human resources forecasting, healthcare workforce, system dynamics, public health

Abstract

Background. Primary health care has been proven to be a highly effective and efficient way to address the main causes and risks of poor health and well-being today, as well as handling the emerging challenges that will threaten health and well-being tomorrow. In our study we used the System Dynamics approach to develop a model for the population and General Practitioner workforce to include multiple inputs and their relationships in the equations for each stock and flow.

Methods. We built the model in the Any Logic software to cover the flow of medical workers, demographic data of the population and the prevalence of the disease over time. Three scenarios were examined for forecasting primary health care personnel resources. The base year for forecasting was 2018, and the modeling was carried out until 2030.

Results. All of three scenarios indicate that with the current number of graduated General Practitioners, the shortage of primary care physicians will be exacerbated. In general, the shortage can reach more than 2,000 on a population of 18.3 million (2018).

Conclusion. The projected shortage of doctors in the primary health care system requires special attention to human resource planning. Only one third of medical graduates in Kazakhstan go to work in the primary health care system. The government needs to develop measures to stimulate and support young medical doctors to become general practitioners.

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Published

2025-09-04

Issue

Section

Original research

How to Cite

1.
Koichubekov B, Kharin A, Sorokina M, Korshukov I, Omarkulov B. System dynamics modeling for general practitioner workforce forecasting in Kazakhstan. Ann Ig. 2025;33(3):242-253. doi:10.7416/ai.2020.2391