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Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

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Optimization Models for Medical Procedures Relocation.

Linh Anh Nguyen^1,2, Andrzej Szałas^1,3

¹Institute of Informatics, University of Warsaw, 02-097 Warsaw, Poland.

Procedia Computer Science

|October 11, 2021

Summary

This summary is machine-generated.

The Covid-19 pandemic disrupted medical procedures. This study developed decision support models to optimize hospital procedure relocation, ensuring efficient healthcare delivery despite disruptions.

Keywords:

Integer linear programming Medical information systems Optimal relocation Public healthcare

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Area of Science:

Operations Research
Health Systems Management
Mathematical Optimization

Background:

The Covid-19 pandemic caused significant disruptions to healthcare services, leading to reduced medical procedures for noncommunicable diseases.
This necessitates innovative solutions for efficient healthcare resource allocation and management.

Purpose of the Study:

To develop and evaluate decision support systems for optimizing the relocation of medical procedures among hospitals.
To address the challenges posed by pandemic-related disruptions in healthcare delivery.

Main Methods:

Formulation of decision problems related to procedure relocation.
Development of linear (mixed integer) programming models.
Experimental verification using real-world data for urological procedures.

Main Results:

The proposed linear programming models effectively address the decision problems.
Even large-scale models with millions of variables were solved within acceptable timeframes.
Demonstrated the practical applicability of the models for optimizing hospital resource allocation.

Conclusions:

The developed mathematical programming models provide an efficient tool for decision support in healthcare.
These models can aid hospitals in optimizing the relocation of medical procedures, enhancing operational efficiency.
The study confirms the feasibility of using advanced optimization techniques for managing healthcare resources during crises.