Stochastic integer programming for multi-disciplinary outpatient clinic planning

Area of Science:

• Operations Research
• Healthcare Management
• Clinical Informatics

Background:

• Multi-disciplinary clinic scheduling is complex due to the need for inter-disciplinary coordination.
• Existing scheduling methods do not fully address the integrated optimization required for open-access multi-disciplinary clinics.
• A Dutch hospital's cancer clinic faces challenges in coordinating patient appointments for diagnosis, treatment planning, and regular follow-ups.

Purpose of the Study:

• To develop and evaluate an integrated optimization approach for designing blueprint schedules in multi-disciplinary clinics.
• To address the open research question of jointly optimizing all appointment schedules within such clinics.
• To minimize patient waiting times, clinician idle time, and clinician overtime.

Main Methods:

• The problem is modeled as a stochastic integer program due to multi-interval scheduling decisions and stochastic patient routing.
• A sample average approximation (SAA) approach is adapted and utilized to solve the stochastic integer program.
• Numerical experiments are conducted to assess the performance and suitability of the SAA approach.

Main Results:

• The SAA approach effectively generates robust blueprint schedules for the multi-disciplinary clinic.
• The proposed method demonstrates significant improvements compared to the current hospital schedules.
• Associated savings and operational efficiencies are quantified.

Conclusions:

• The integrated optimization approach provides a viable solution for complex multi-disciplinary clinic scheduling.
• This research offers a novel method for optimizing clinic operations, specifically for cancer care settings.
• The findings are directly applicable to the studied Dutch hospital, offering practical benefits.