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An Application of Deep Reinforcement Learning for Ambulance Allocation to Emergency Departments under Overcrowding Situation

Taher Gandomabadi, Mohammad Mahdi | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54301 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Akhavan Niaki, Taghi
  7. Abstract:
  8. In the last decade, emergency department (ED) overcrowding has become a national crisis for the US healthcare system. Increasing mortality rates, decreasing quality of care, financial losses due to walkouts, and ambulance diversion are some of the consequences of ED overcrowding. Given the increasing demand in terms of ambulance utilization which we can see an instance of it in the COVID-19 pandemic, being able to allocate service requests to EDs efficiently, becomes a key function of emergency medical services. in this investigation, an algorithm of deep reinforcement learning called deep Q-learning is used to address this problem and to assign ambulances to ED's appropriately. under certain assumptions, reinforcement learning agent allocates ambulances to ED's in a simulated environment of Tehran. The goal of this allocation is to decrease the total waiting time for treatment which includes traveling time and patient waiting time in ED for vacant bed. The results show that the proposed method decreases this important factor (total waiting time) for all types of patients for about 10.8% compared to the current policy of Tehran Urgency which assigns ambulances to the nearest ED
  9. Keywords:
  10. Deep Reinforcement Learning ; Emergency Department Overcrowing ; Ambulance Allocation ; Overcrowding ; Emergency Department ; Pandemic

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