Sean Shao Wei Lam1, Clarence Boon Liang Ng2, Francis Ngoc Hoang Long Nguyen3, Yih Yng Ng4, Marcus Eng Hock Ong5. 1. Health Services Research Centre, Singapore Health Services; Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore. Electronic address: lam.shao.wei@singhealth.com.sg. 2. Department of Industrial and Systems Engineering, National University of Singapore, Singapore. Electronic address: clarenceng@u.nus.edu. 3. Health Services Research Centre, Singapore Health Services, Singapore. Electronic address: nguyen.ngoc.hoang.long@singhealth.com.sg. 4. Medical Department, Singapore Civil Defence Force, Singapore. Electronic address: ng_yih_yng@scdf.gov.sg. 5. Health Services Research Centre, Singapore Health Services; Department of Emergency Medicine, Singapore General Hospital, Singapore; Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore. Electronic address: marcus.ong.e.h@sgh.com.sg.
Abstract
OBJECTIVE: Dynamic ambulance redeployment policies tend to introduce much more flexibilities in improving ambulance resource allocation by capitalizing on the definite geospatial-temporal variations in ambulance demand patterns over the time-of-the-day and day-of-the-week effects. A novel modelling framework based on the Approximate Dynamic Programming (ADP) approach leveraging on a Discrete Events Simulation (DES) model for dynamic ambulance redeployment in Singapore is proposed in this paper. METHODS: The study was based on the Singapore's national Emergency Medical Services (EMS) system. Based on a dataset comprising 216,973 valid incidents over a continuous two-years study period from 1 January 2011-31 December 2012, a DES model for the EMS system was developed. An ADP model based on linear value function approximations was then evaluated using the DES model via the temporal difference (TD) learning family of algorithms. The objective of the ADP model is to derive approximate optimal dynamic redeployment policies based on the primary outcome of ambulance coverage. RESULTS: Considering an 8min response time threshold, an estimated 5% reduction in the proportion of calls that cannot be reached within the threshold (equivalent to approximately 8000 dispatches) was observed from the computational experiments. The study also revealed that the redeployment policies which are restricted within the same operational division could potentially result in a more promising response time performance. Furthermore, the best policy involved the combination of redeploying ambulances whenever they are released from service and that of relocating ambulances that are idle in bases. CONCLUSION: This study demonstrated the successful application of an approximate modelling framework based on ADP that leverages upon a detailed DES model of the Singapore's EMS system to generate approximate optimal dynamic redeployment plans. Various policies and scenarios relevant to the Singapore EMS system were evaluated.
OBJECTIVE: Dynamic ambulance redeployment policies tend to introduce much more flexibilities in improving ambulance resource allocation by capitalizing on the definite geospatial-temporal variations in ambulance demand patterns over the time-of-the-day and day-of-the-week effects. A novel modelling framework based on the Approximate Dynamic Programming (ADP) approach leveraging on a Discrete Events Simulation (DES) model for dynamic ambulance redeployment in Singapore is proposed in this paper. METHODS: The study was based on the Singapore's national Emergency Medical Services (EMS) system. Based on a dataset comprising 216,973 valid incidents over a continuous two-years study period from 1 January 2011-31 December 2012, a DES model for the EMS system was developed. An ADP model based on linear value function approximations was then evaluated using the DES model via the temporal difference (TD) learning family of algorithms. The objective of the ADP model is to derive approximate optimal dynamic redeployment policies based on the primary outcome of ambulance coverage. RESULTS: Considering an 8min response time threshold, an estimated 5% reduction in the proportion of calls that cannot be reached within the threshold (equivalent to approximately 8000 dispatches) was observed from the computational experiments. The study also revealed that the redeployment policies which are restricted within the same operational division could potentially result in a more promising response time performance. Furthermore, the best policy involved the combination of redeploying ambulances whenever they are released from service and that of relocating ambulances that are idle in bases. CONCLUSION: This study demonstrated the successful application of an approximate modelling framework based on ADP that leverages upon a detailed DES model of the Singapore's EMS system to generate approximate optimal dynamic redeployment plans. Various policies and scenarios relevant to the Singapore EMS system were evaluated.
Authors: Nan Liu; Mingxuan Liu; Xinru Chen; Yilin Ning; Jin Wee Lee; Fahad Javaid Siddiqui; Seyed Ehsan Saffari; Andrew Fu Wah Ho; Sang Do Shin; Matthew Huei-Ming Ma; Hideharu Tanaka; Marcus Eng Hock Ong Journal: EClinicalMedicine Date: 2022-05-06