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Teleconsultation dynamic scheduling with a deep reinforcement learning approach.

Wenjia Chen1, Jinlin Li2

  • 1School of Economics and Management, Beijing Information Science and Technology University, Beijing, 100192, China.

Artificial Intelligence in Medicine
|March 10, 2024
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Summary

This study optimizes teleconsultation scheduling using a deep reinforcement learning algorithm (DQN-S) to enhance healthcare efficiency. The method significantly reduces patient waiting times and service costs in tertiary hospitals.

Keywords:
Deep Q-network (DQN)Deep reinforcement learningMarkov decision process (MDP)Teleconsultation scheduling

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Area of Science:

  • Healthcare Management
  • Operations Research
  • Artificial Intelligence

Background:

  • Teleconsultations are crucial for tertiary hospitals but face challenges in service quality and efficiency.
  • Demand intermittency and specialist mobility complicate traditional scheduling models.

Purpose of the Study:

  • To optimize teleconsultation start times in Class A tertiary hospitals.
  • To improve overall service quality and operational efficiency.

Main Methods:

  • Formulated a general teleconsultation scheduling model, incorporating Number of Services (NS) as an objective.
  • Converted the model into a Markov Decision Process (MDP).
  • Applied Deep Reinforcement Learning (DRL), specifically a Deep Q Network with a semi-fixed policy (DQN-S), to solve the MDP, incorporating an early stop strategy.

Main Results:

  • The DQN-S algorithm demonstrated effectiveness in improving teleconsultation scheduling.
  • Achieved reductions of 9%-41% in average demand waiting time.
  • Reduced the number of services by 3%-42% and total service costs by 3%-33%.

Conclusions:

  • The proposed DQN-S model and solving method effectively enhance teleconsultation quality and efficiency.
  • This approach offers a practical solution for optimizing healthcare service delivery in complex environments.