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Forecasts on the Evolution of Human Resources in the Health System in Romania Using the Arima Method

Author

Listed:
  • Mirescu Lucian

    (PhD student, University of Craiova, Faculty of Economics and Business Administration, Craiova, Romania)

  • Popescu Liviu

    (Professor PhD, University of Craiova, Faculty of Economics and Business Administration, Craiova, Romania)

Abstract

In this paper, forecasts are made regarding the number of employees in the public and private health sector in Romania for different professional categories such as: physicians, dentists, pharmacists, family physicians, medical assistants, auxiliary personnel. Using the Dickey-Fuller test, it was initially checked whether the time series were stationary and if not, the first or second difference was applied to make them stationary. Using the autoregressive integrated moving average (ARIMA) method with the stages of identification, estimation, diagnosis and prediction, the forecasts for the period 2023-2025 together with the associated confidence intervals were determined, using data provided by the National Institute of Statistics. Regarding the public sector health personnel, a decrease is expected, in general, for the period 2024-2025. Instead, the private sector continues its previous trend of constant growth in the number of employees. Thus, authorities can use these forecasts to adjust strategies for training and recruiting medical personnel according to the estimated need. Forecasts are an essential tool to ensure the balance between demand and supply in the health sector, thus contributing to the improvement of medical services and the long-term sustainability of the health system.

Suggested Citation

Handle: RePEc:vrs:timjeb:v:17:y:2024:i:1:p:65-112:n:1004
DOI: 10.2478/tjeb-2024-0004
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File URL: https://doi.org/10.2478/tjeb-2024-0004
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More about this item

Keywords

Forecast; Health system; Human resources; ARIMA method;
All these keywords.

JEL classification:

  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
  • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
  • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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