A supply model for nurse workforce projection in Malaysia.

Area of Science:

• Health Workforce Planning
• System Dynamics Modeling
• Nursing Supply Forecasting

Background:

• Accurate forecasting of registered nurse (RN) supply is critical for effective health workforce planning.
• Existing methods may not adequately predict future RN demand, necessitating advanced modeling approaches.
• Malaysia serves as a case study for developing and applying a robust nurse supply projection model.

Purpose of the Study:

• To develop and validate a System Dynamics simulation model for forecasting the supply of registered nurses.
• To provide insights for health workforce planning policy by predicting future RN needs.
• To support the development of a needs-based nurse workforce projection for Malaysia.

Main Methods:

• A System Dynamics simulation model comprising three sub-models: training, population, and Full-Time Equivalent (FTE).
• The training model forecasts newly registered nurses, the population model estimates the national RN count, and the FTE model quantifies direct patient care providers.
• Model validation employed error analysis, including root mean square percent error and Theil inequality statistics.

Main Results:

• The simulation model provides a 15-year forecast of registered nurse supply.
• Validation confirmed the accuracy and reliability of the simulation results.
• The model's output enables 'what-if' analyses for policy makers.

Conclusions:

• The developed simulation model offers valuable insights for health workforce planning and policy-making.
• Recommendations are proposed to address identified nursing deficits.
• This study's findings enhance predictive capabilities for future nursing workforce needs.