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An optimization protocol for MRI examination resource allocation based on demand forecasting and linear programming.

Zhongbin Zhou1, Hanyu Zhou2, Yuanyuan Qiao1

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This summary is machine-generated.

Rising MRI scan demand in China caused long wait times. This study developed a forecasting and optimization method to reduce MRI wait times and improve resource use, showing the NARX model and Integer Linear Programming significantly enhance efficiency.

Keywords:
ARIMA modelInteger linear programmingMRINAR modelOptimization protocol

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Area of Science:

  • Healthcare Management
  • Medical Imaging Operations
  • Operations Research in Healthcare

Background:

  • Increasing medical service accessibility in China has led to a surge in Magnetic Resonance Imaging (MRI) scans.
  • This surge has resulted in significant delays in MRI examination queues at major hospitals.
  • High costs of MRI equipment and procedures necessitate efficient resource utilization to prevent underutilization.

Purpose of the Study:

  • To develop a comprehensive method to reduce patient wait times for MRI examinations.
  • To optimize the utilization of medical resources, specifically MRI machines, within hospitals.
  • To address the challenge of increasing MRI demand and associated operational inefficiencies.

Main Methods:

  • Utilized daily MRI examination application data from July 2017 to November 2022.
  • Developed and compared forecasting models: Autoregressive Integrated Moving Average (ARIMA), AutoRegressive Integrated Moving Average with exogenous (ARIMAX), Non-AutoRegressive (NAR), Non-AutoRegressive with exogenous (NARX), and an integrated ARIMAX-NARX model.
  • Employed Integer Linear Programming (ILP) for optimizing the number of daily MRI examinations per machine and developed an optimization flowchart for resource allocation.

Main Results:

  • Identified a weekly cyclical trend in MRI examination applications.
  • The NARX model demonstrated the highest predictive accuracy for future MRI demand.
  • The ILP model effectively determined optimal examination numbers per machine, leading to cost reduction and improved efficiency via the developed flowchart.

Conclusions:

  • The NARX model provides superior forecasting accuracy for MRI examination demand.
  • Integrating the NARX model with an ILP model and an optimization flowchart offers a robust protocol for efficient MRI resource allocation.
  • This systematic approach enhances operational efficiency, reduces patient wait times, and optimizes the use of costly MRI resources.