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Integrating Fuzzy Multiobjective Programming and System Dynamics to Develop an Approach for Talent Retention Policy

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Reducing new healthcare employee turnover during the trial period is key for talent retention. This study introduces a new simulation method to find the most effective human resource (HR) policies for healthcare institutions.

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Area of Science:

  • Healthcare Management
  • Human Resources
  • Operations Research

Background:

  • Aging populations are increasing demand for medical services globally.
  • High healthcare staff turnover, particularly in nonurban areas, leads to significant costs.
  • Current talent retention strategies are often reactive and lack empirical evaluation.

Purpose of the Study:

  • To develop a novel method for simulating human resource (HR) planning systems.
  • To evaluate the effectiveness of different HR policies for talent retention in healthcare institutions.
  • To identify the most impactful HR policy for reducing staff turnover.

Main Methods:

  • Combined system dynamics modeling with fuzzy multiobjective programming.
  • Developed a multiobjective decision-making model incorporating external insurance policies.
  • Simulated HR planning systems to assess various talent retention strategies.

Main Results:

  • Reducing the turnover rate of new employees during their trial period emerged as the most effective talent retention policy.
  • The developed simulation procedure proved more efficient, effective, and cost-effective than traditional trial-and-error methods.
  • The model provides a robust framework for evaluating HR policies under different external conditions.

Conclusions:

  • Targeting early-stage employee retention is crucial for healthcare talent management.
  • System dynamics and fuzzy multiobjective programming offer a powerful, data-driven approach to HR policy optimization.
  • This methodology enables healthcare institutions to make informed decisions for sustainable talent retention and cost reduction.