Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints

Area of Science:

• Healthcare Management
• Operations Research
• Medical Informatics

Background:

• High hospital bed utilization rates in China exceed 100%, straining medical resources.
• A critical need exists to balance high-quality medical resource supply with patient hospitalization demand.

Purpose of the Study:

• To optimize the case mix and develop an admission plan for elective patients in a respiratory medicine department.
• To address stochastic patient length of stay and varying resource consumption for efficient hospital bed management.

Main Methods:

• Formulated a general integer programming model incorporating heterogeneous patients and priority constraints.
• Classified elective patients based on registration information prior to admission.
• Developed a mathematical model to generate scientific and reasonable admission planning for multitype patients.

Main Results:

• Model I, considering patient priority, demonstrated superior performance in admissions and revenue compared to Model II (without priority).
• The proposed model allows for adjustable priority parameters to achieve optimal outcomes across different scenarios.
• Daily admission plans generated by Model I aid patient admission management in large hospitals.

Conclusions:

• The integer programming model provides an effective tool for optimizing elective patient admission planning in respiratory medicine.
• Prioritizing patients within the admission plan significantly enhances hospital operational efficiency and financial outcomes.
• The model offers a flexible framework for adapting admission strategies to diverse hospital goals and dynamic conditions.