Presented a Framework of Computational Modeling to Identify the Patient Admission Scheduling Problem in the...

Area of Science:

• Operations Research
• Healthcare Management
• Applied Mathematics

Background:

• Operating room scheduling is complex and critical in healthcare systems.
• Increasing literature necessitates updated evaluations to identify emerging trends.
• Deterministic models often fall short in dynamic scheduling environments.

Purpose of the Study:

• To develop and present a mathematical model for patient admission scheduling with stochastic arrivals and departures.
• To demonstrate the application of this model to real-world operational challenges.
• To compare the efficacy of stochastic versus deterministic scheduling approaches.

Main Methods:

• Mathematical modeling of patient admission scheduling with stochastic elements.
• Development of a solution technique for efficient computation.
• Numerical analysis and case study examples to illustrate the methodology.
• Application of heuristics for objective function estimation in large-scale problems.

Main Results:

• The proposed stochastic model significantly outperforms traditional deterministic models.
• Numerical examples confirm the model's effectiveness in handling uncertainty in surgery length and emergency patient arrivals.
• The methodology is shown to be rapidly applicable to large, real-world datasets.

Conclusions:

• Stochastic modeling provides superior solutions for operating room scheduling compared to deterministic methods.
• The developed model and solution techniques are practical for real-world healthcare operations.
• This research facilitates quicker adaptation to new trends in operating room management.