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Medical resource allocation planning by integrating machine learning and optimization models.

Tasquia Mizan1, Sharareh Taghipour2

  • 1Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria St., Toronto, ON M5B 2K3, Canada.

Artificial Intelligence in Medicine
|December 3, 2022
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Summary
This summary is machine-generated.

This study introduces a new machine learning framework to predict patient arrivals and optimize radiological resource allocation, significantly reducing healthcare waiting times. The proposed model improved accuracy by 10.81% and decreased patient waiting time by 8.17%.

Keywords:
Machine learningMulti-objective optimizationMulti-target forecastingResource allocation planningWorkload distribution

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Area of Science:

  • Healthcare Operations Research
  • Machine Learning in Healthcare
  • Health Systems Management

Background:

  • Patient waiting time is a critical issue in Canadian healthcare, influenced by resource allocation and workload distribution.
  • Accurate prediction of patient arrivals and associated workload is challenging due to inherent uncertainties and variations.
  • Inefficient planning leads to prolonged waiting periods, impacting patient satisfaction and healthcare system efficiency.

Purpose of the Study:

  • To reduce patient waiting times in Canadian healthcare settings through improved radiological resource allocation and workload distribution.
  • To develop and evaluate a novel three-phase solution framework integrating machine learning and optimization for healthcare planning.
  • To enhance the prediction of uncertain parameters like patient arrivals and optimize resource management.

Main Methods:

  • A three-phase framework was developed, starting with an Ensemble of Pruned Regressor Chain (EPRC) model for offline prediction of patient arrivals.
  • The EPRC model was compared against popular multi-target prediction methods for accuracy assessment.
  • A multi-objective optimization model, using the weighted-sum method, determined workload allocation based on real-time forecasts from the EPRC model.

Main Results:

  • The proposed EPRC model demonstrated 10.81% higher accuracy in predicting uncertain parameters compared to alternative multi-target models.
  • The forecasting capabilities led to an approximate 25% reduction in total workload.
  • Efficient workload distribution resulted in an 8.17% decrease in average patient waiting times.

Conclusions:

  • The integrated machine learning and optimization framework effectively addresses patient waiting time challenges in Canadian healthcare.
  • The EPRC model provides accurate predictions crucial for dynamic resource and workload management.
  • Optimized resource allocation significantly improves healthcare operational efficiency and patient flow.