Development of simulation optimization methods for solving patient referral problems in the hospital-collaboration...

Area of Science:

• Healthcare Operations Research
• Medical Imaging Logistics
• Simulation Optimization

Background:

• Hospitals face challenges in efficiently referring patients for shared imaging services.
• Uncertainty in patient types, arrival, and operation times complicates referral processes.
• Existing referral systems may not be optimized for dynamic, multi-hospital environments.

Purpose of the Study:

• To develop and evaluate an optimized patient referral system for cooperative hospitals offering shared imaging services.
• To address the complexities of patient flow and waiting times in an uncertain healthcare environment.
• To improve the efficiency and effectiveness of inter-hospital patient referrals for diagnostic imaging.

Main Methods:

• System simulation was employed to model the patient referral process.
• A simulation optimization method was developed, integrating a heuristic algorithm (patient referral mechanism) with particle swarm optimization (PSO).
• The model was rigorously verified and validated before proposing and testing three distinct patient referral mechanisms.

Main Results:

• Three patient referral mechanisms were proposed and simulated.
• Mechanism 2, which directs patients to the hospital with the shortest waiting time, demonstrated superior performance.
• This mechanism effectively facilitated patient referrals among all hospitals while minimizing patient waiting times.

Conclusions:

• The developed simulation optimization approach provides an effective solution for complex patient referral problems.
• Mechanism 2 is recommended for its ability to balance referral volume and patient waiting times in shared imaging service networks.
• Future research should explore further refinements and applications of this optimization strategy in healthcare settings.