A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong
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A data‑driven multi‑fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong Hainan Guo1 · Haobin Gu1 · Yu Zhou2 · Jiaxuan Peng1 Accepted: 15 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Overcrowding at emergency departments in Hong Kong has been a critical issue for hospital managers recently. In this study, we focus on optimizing the medical staff configuration to alleviate overcrowding. According to the service requirements proposed by the Hong Kong government, 90% of urgent patients should receive treatment within 30 min. However, this condition is rarely satisfied in the practical situation. Therefore, we formulate the problem as minimizing the proportion of urgent patients that violate the service requirements while satisfying the service requirements of the other categories and cost constraints, thereby resulting in an optimization problem with a stochastic objective and several stochastic constraints. To solve this problem efficiently, we proposed a multi-fidelity simulation optimization framework containing a low- and a high-fidelity process. We utilize an evolutionary algorithm with violation-constrained handling assisted by a surrogate model as a lowfidelity process to shrink the solution space and generate an elite population. In the high-fidelity process, we exploit the optimal computing budget allocation method to identify the best solution in the elite population based on a data-driven simulation model. A case study is also discussed, and the results demonstrated that with a limited labor cost, there is a 52.05% reduction on average in the waiting time of urgent patients. Meanwhile, our proposed multi-fidelity simulation optimization framework proves to save 98.4% of the simulation time. Keywords Data-driven simulation optimization · Surrogate-based evolutionary algorithm · Optimal computing budget allocation · Healthcare operations management
* Yu Zhou [email protected]; [email protected] Extended author information available on the last page of the article
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1 Introduction Nowadays, public hospitals in Hong Kong are continuously facing problems associated with overcrowding. Recently, Hong Kong Hospital Authority (HKHA) released key statistical results on service demands of the emergency department (ED), indicating that 14 out of 15 public hospitals were overcrowded (HKH 2017). The ED services in Hong Kong (HK) adopt a triage system, which classifies the patients in the ED into five categories based on their conditions: Category I (critical), Category II (emergency), Category III (urgent), Category IV (semiurgent), and Category V (non-urgent). According to the service requirements proposed by HKHA, 90% of the urgent patients should receive treatment within 30 min. However, this condition is rarely satisfied in practical situations (HKH 2019). In addition, the ED manager of our collaborative hospital mentioned that they have an average of 500 first attendance
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