STOCHASTIC SIMULATION TO PROJECT SUPPLY OF THE KAZAKHSTAN GENERAL PRACTICE WORKFORCE TO 2030

Abstract

Our aim was to explore the effect of the uncertainty of some parameters on total General Practitioner (GP) supply in Kazakhstan to 2030. System dynamics simulation was used to develop model for General Practitioner workforce forecasting. Sensitivity analysis (Monte Carlo method) was performed to account for changes in the number of GP as each and set of model parameters is varied. Three key input parameters were explored: retirement rate, attrition rate, recruitment rate. For each parameter relative sensitivity was calculated. Created model is the least sensitive to the parameter of the retirement rate, but is very sensitive to attrition rate. Staff turnover is one of the main problems of primary health care. The attrition rate parameter has great potential for controlling the flow of labor. The level of recruitment (other than new GPs) is another factor that has a significant impact on forecasting GPs. The sensitivity to this parameter is comparable to the sensitivity to the rate of depletion. We believe that measures taken to reduce dropouts will simultaneously increase recruitment. We also evaluated the effect of all three parameters simultaneously. If the most likely scenario is realized, then the proposal of a General Practitioner will almost completely cover the needs with a small deficit of 68 to 305 doctors. If the high limit or low limit scenarios in the labor market are implemented, Healthcare Ministry will face serious problems.