Semi-online patient scheduling in pathology laboratories

Area of Science:

• Operations Research
• Healthcare Management
• Computational Biology

Background:

• Effective patient scheduling is crucial for healthcare facilities to improve patient satisfaction and operational efficiency.
• Pathology laboratories face challenges with patient flow due to test precedence constraints and resource limitations.
• Optimizing scheduling in semi-online environments is vital for reducing patient waiting times.

Purpose of the Study:

• To develop and evaluate an optimized patient scheduling model for a pathology laboratory.
• To address the semi-online patient scheduling problem considering real-world constraints.
• To reduce patient waiting times and improve the overall efficiency of laboratory operations.

Main Methods:

• Formulation of the problem as a semi-online hybrid shop scheduling problem.
• Development of a mixed integer linear programming model.
• Implementation of a genetic algorithm (GA) with response surface methodology for parameter tuning.

Main Results:

• Simulation experiments demonstrated a significant reduction in patient waiting times.
• The proposed approach led to substantial improvements in laboratory operational efficiency.
• Empirical data from a Tehran pathology laboratory validated the algorithm's effectiveness.

Conclusions:

• The developed scheduling approach successfully handles real-world complexities, including test precedence and multi-machine constraints.
• The semi-online nature of the problem was effectively managed by the proposed model.
• This research offers a practical solution for optimizing patient scheduling in pathology laboratories.