Surgery scheduling problem considering the affinity and preferences in the surgical team

Area of Science:

• Healthcare Management
• Operations Research
• Surgical Team Dynamics

Background:

• Surgery scheduling is vital for hospital efficiency.
• Integrating surgical team affinity and preferences is understudied.
• Existing methods struggle to balance team compatibility with scheduling efficiency.

Purpose of the Study:

• To develop novel mathematical programming models for elective surgery scheduling.
• To simultaneously integrate surgical team affinity and preferences.
• To maximize scheduled surgery priority while considering team dynamics.

Main Methods:

• Proposed score-based penalty mathematical programming models.
• Integrated affinity and preferences into scheduling optimization.
• Evaluated against baseline and constraint-based models using integer linear programming and constraint programming.

Main Results:

• Demonstrated the feasibility of incorporating affinity and preferences.
• Achieved enhanced surgical team quality with negligible impact on surgery volume.
• Showcased a method to strengthen team relationships.

Conclusions:

• The proposed score-based penalty approach effectively integrates team affinity and preferences into surgery scheduling.
• This method improves surgical team quality and human relationships.
• Positive impacts on patient surgical outcomes are anticipated.