ArticleHealthcare (Basel, Switzerland)2020
Improving Emergency Department Efficiency by Patient Scheduling Using Deep Reinforcement Learning.
Article in Healthcare (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- AI assisted prediction of unplanned intensive care admissions using natural language processing in elective neurosurgery.NPJ digital medicine · 2025Article
- Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study.JMIR formative research · 2025Article
- Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.Health care management science · 2025Review
- Principles of artificial intelligence in radiooncology.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Article
- Applying Simulation Optimization to Minimize Drug Inventory Costs: A Study of a Case Outpatient Pharmacy.Healthcare (Basel, Switzerland) · 2022Article
- Using Simulation Optimization to Solve Patient Appointment Scheduling and Examination Room Assignment Problems for Patients Undergoing Ultrasound Examination.Healthcare (Basel, Switzerland) · 2022Article
- Introduction to supervised machine learning in clinical epidemiology.Annals of clinical epidemiology · 2022Article
- Machine learning in patient flow: a review.Progress in biomedical engineering (Bristol, England) · 2021Review
- Overuse of Health Care in the Emergency Services in Chile.International journal of environmental research and public health · 2021Review
- Analyzing Malaria Disease Using Effective Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2020Article
- Analyzing Lung Disease Using Highly Effective Deep Learning Techniques.Healthcare (Basel, Switzerland) · 2020Article
- Artificial intelligence and machine learning in emergency medicine: a narrative review.Acute medicine & surgeryReview
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Authors and funding
2 authors.
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Abstract
Emergency departments (ED) in hospitals usually suffer from crowdedness and long waiting times for treatment. The complexity of the patient's path flows and their controls come from the patient's diverse acute level, personalized treatment process, and interconnected medical staff and resources. One of the factors, which has been controlled, is the dynamic situation change such as the patient's composition and resources' availability. The patient's scheduling is thus complicated in consideration of various factors to achieve ED efficiency. To address this issue, a deep reinforcement learning (RL) is designed and applied in an ED patients' scheduling process. Before applying the deep RL, the mathematical model and the Markov decision process (MDP) for the ED is presented and formulated. Then, the algorithm of the RL based on deep Q-networks (DQN) is designed to determine the optimal policy for scheduling patients. To evaluate the performance of the deep RL, it is compared with the dispatching rules presented in the study. The deep RL is shown to outperform the dispatching rules in terms of minimizing the weighted waiting time of the patients and the penalty of emergent patients in the suggested scenarios. This study demonstrates the successful implementation of the deep RL for ED applications, particularly in assisting decision-makers under the dynamic environment of an ED.
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Registered trials
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