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• W4310773165 abstract "Abstract Earthquakes can disrupt the healthcare system heavily, leading to long wait times and many untreated patients for years after the event. Emergency services, in particular, must return to preearthquake functionality as soon as possible such that patients, especially critically injured ones, can be treated promptly. However, reconstruction and restoration of emergency services can take years. Due to limited reconstruction resources, decision‐makers cannot reconstruct all hospitals simultaneously. They are typically forced to prioritize the reconstruction order, and this process is often poorly planned. This article models emergency services as an M/M/s queuing system that accounts for prioritized treatment of critical patients and formulates a greedy algorithm to plan for an effective healthcare system reconstruction. The algorithm finds the reconstruction ordering of hospital buildings such that emergency patients have the shortest time to receiving medical care possible. We show our greedy algorithm's good performance for small problem instances, with average deviations from the optimal solution below 16%. Further, we apply our methodology to a case study of Lima, Peru, under a hypothetical M8.0 earthquake. The application demonstrates that compared to typically implemented policies, a policy guided by our formulation results in shorter time to treatment and reduces the number of untreated patients over the course of the reconstruction period by more than a factor of 3 in a worst‐case scenario with 70% hospital capacity disruption. Finally, we demonstrate that our formulation can be integrated into risk analysis through Monte Carlo simulations to inform decision‐makers of reconstruction plans after future earthquakes." @default.
• W4310773165 created "2022-12-17" @default.
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• W4310773165 date "2022-12-05" @default.
• W4310773165 modified "2023-10-17" @default.
• W4310773165 title "Prioritized reconstruction of healthcare facilities after earthquakes based on recovery of emergency services" @default.
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• W4310773165 doi "https://doi.org/10.1111/risa.14076" @default.
• W4310773165 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36470850" @default.
• W4310773165 hasPublicationYear "2022" @default.
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