Publications by authors named "Sean Shao Wei Lam"

14 Publications

  • Page 1 of 1

Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms.

Int J Med Inform 2021 Dec 14;158:104665. Epub 2021 Dec 14.

Health Services and Systems Research, Duke-NUS Medical School, Singapore; Health Services Research Centre, Singapore Health Services, Singapore; Health Services Research Institute, SingHealth Duke NUS Academic Medical Centre, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address:

Objective: To develop a 2-stage discrete events simulation (DES) based framework for the evaluation of elective surgery cancellation strategies and resumption scenarios across multiple operational outcomes.

Materials And Methods: Study data was derived from the data warehouse and domain knowledge on the operational process of the largest tertiary hospital in Singapore. 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 were extracted for the study. A clustering approach was used in stage 1 of the modelling framework to develop the groups of surgeries that followed distinctive postponement patterns. These clusters were then used as inputs for stage 2 where the DES model was used to evaluate alternative phased resumption strategies considering the outcomes of OR utilization, waiting times to surgeries and the time to clear the backlogs.

Results: The tool enabled us to understand the elective postponement patterns during the COVID-19 partial lockdown period, and evaluate the best phased resumption strategy. Differences in the performance measures were evaluated based on 95% confidence intervals. The results indicate that two of the gradual phased resumption strategies provided lower peak OR and bed utilizations but required a longer time to return to BAU levels. Minimum peak bed demands could also be reduced by approximately 14 beds daily with the gradual resumption strategy, whilst the maximum peak bed demands by approximately 8.2 beds. Peak OR utilization could be reduced to 92% for gradual resumption as compared to a minimum peak of 94.2% with the full resumption strategy.

Conclusions: The 2-stage modelling framework coupled with a user-friendly visualization interface were key enablers for understanding the elective surgery postponement patterns during a partial lockdown phase. The DES model enabled the identification and evaluation of optimal phased resumption policies across multiple important operational outcome measures.

Lay Abstract: During the height of the COVID-19 pandemic, most healthcare systems suspended their non-urgent elective surgery services. This strategy was undertaken as a means to expand surge capacity, through the preservation of structural resources (such as operating theaters, ICU beds, and ventilators), consumables (such as personal protective equipment and medications), and critical healthcare manpower. As a result, some patients had less-essential surgeries postponed due to the pandemic. As the first wave of the pandemic waned, there was an urgent need to quickly develop optimal strategies for the resumption of these surgeries. We developed a 2-stage discrete events simulation (DES) framework based on 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 captured in the Singapore General Hospital (SGH) enterprise data warehouse. The outcomes evaluated were OR utilization, waiting times to surgeries and time to clear the backlogs. A user-friendly visualization interface was developed to enable decision makers to determine the most promising surgery resumption strategy across these outcomes. Hospitals globally can make use of the modelling framework to adapt to their own surgical systems to evaluate strategies for postponement and resumption of elective surgeries.
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http://dx.doi.org/10.1016/j.ijmedinf.2021.104665DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8674476PMC
December 2021

Maximum expected survival rate model for public access defibrillator placement.

Resuscitation 2022 01 6;170:213-221. Epub 2021 Dec 6.

Health Services and Systems Research, Duke-NUS Medical School, Singapore; Health Services Research Centre, Singapore Health Services, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address:

Aim: Mathematical optimization of automated external defibrillator (AED) placement has demonstrated potential to improve survival of out-of-hospital cardiac arrest (OHCA). Existing models mostly aim to improve accessibility based on coverage radius and do not account for detailed impact of delayed defibrillation on survival. We aimed to predict OHCA survival based on time to defibrillation and developed an AED placement model to directly maximize the expected survival rate.

Methods: We stratified OHCAs occurring in Singapore (2010-2017) based on time to defibrillation and developed a regression model to predict the Utstein survival rate. We then developed a novel AED placement model, the maximum expected survival rate (MESR) model. We compared the performance of MESR with a maximum coverage model developed for Canada that was shown to be generalizable to other settings (Denmark). The survival gain of MESR was assessed through 10-fold cross-validation for placement of 20 to 1000 new AEDs in Singapore. Statistical analysis was performed using χ and McNemar's tests.

Results: During the study period, 15,345 OHCAs occurred. The power-law approximation with R of 91.33% performed best among investigated models. It predicted a survival of 54.9% with defibrillation within the first two minutes after collapse that was reduced by more than 60% without defibrillation within the first 4 minutes. MESR outperformed the maximum coverage model with P-value < 0.05 (<0.0001 in 22 of 30 experiments).

Conclusion: We developed a novel AED placement model based on the impact of time to defibrillation on OHCA outcomes. Mathematical optimization can improve OHCA survival.
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http://dx.doi.org/10.1016/j.resuscitation.2021.11.039DOI Listing
January 2022

Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework.

Resuscitation 2022 01 26;170:126-133. Epub 2021 Nov 26.

Pre-hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address:

Background: Accurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC.

Methods: We utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010-2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses.

Results: 5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84-0.90) within the testing cohort.

Conclusion: We developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.
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http://dx.doi.org/10.1016/j.resuscitation.2021.11.029DOI Listing
January 2022

Coronavirus disease 2019 (COVID-19): an evidence map of medical literature.

BMC Med Res Methodol 2020 07 2;20(1):177. Epub 2020 Jul 2.

Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.

Background: Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises.

Methods: In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps.

Results: The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16).

Conclusions: Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises.
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http://dx.doi.org/10.1186/s12874-020-01059-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330264PMC
July 2020

Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study.

BMJ Open 2019 09 26;9(9):e031382. Epub 2019 Sep 26.

Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.

Objectives: To identify risk factors for inpatient mortality after patients' emergency admission and to create a novel model predicting inpatient mortality risk.

Design: This was a retrospective observational study using data extracted from electronic health records (EHRs). The data were randomly split into a derivation set and a validation set. The stepwise model selection was employed. We compared our model with one of the current clinical scores, Cardiac Arrest Risk Triage (CART) score.

Setting: A single tertiary hospital in Singapore.

Participants: All adult hospitalised patients, admitted via emergency department (ED) from 1 January 2008 to 31 October 2017 (n=433 187 by admission episodes).

Main Outcome Measure: The primary outcome of interest was inpatient mortality following this admission episode. The area under the curve (AUC) of the receiver operating characteristic curve of the predictive model with sensitivity and specificity for optimised cut-offs.

Results: 15 758 (3.64%) of the episodes were observed inpatient mortality. 19 variables were observed as significant predictors and were included in our final regression model. Our predictive model outperformed the CART score in terms of predictive power. The AUC of CART score and our final model was 0.705 (95% CI 0.697 to 0.714) and 0.817 (95% CI 0.810 to 0.824), respectively.

Conclusion: We developed and validated a model for inpatient mortality using EHR data collected in the ED. The performance of our model was more accurate than the CART score. Implementation of our model in the hospital can potentially predict imminent adverse events and institute appropriate clinical management.
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http://dx.doi.org/10.1136/bmjopen-2019-031382DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6773418PMC
September 2019

Predicting hospital admission at the emergency department triage: A novel prediction model.

Am J Emerg Med 2019 08 29;37(8):1498-1504. Epub 2018 Oct 29.

Duke-NUS Medical School, National University of Singapore, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore. Electronic address:

Background: Emergency department (ED) overcrowding is a growing international patient safety issue. A major contributor to overcrowding is long wait times for inpatient hospital admission. The objective of this study is to create a model that can predict a patient's need for hospital admission at the time of triage.

Methods: Retrospective observational study of electronic clinical records of all ED visits over ten years to a large urban hospital in Singapore. The data was randomly divided into a derivation set and a validation set. We used the derivation set to develop a logistic regression model that predicts probability of hospital admission for patients presenting to the ED. We tested the model on the validation set and evaluated the performance with receiver operating characteristic (ROC) curve analysis.

Results: A total of 1,232,016 visits were included for final analysis, of which 38.7% were admitted. Eight variables were included in the final model: age group, race, postal code, day of week, time of day, triage category, mode of arrival, and fever status. The model performed well on the validation set with an area under the curve of 0.825 (95% CI 0.824-0.827). Increasing age, increasing triage acuity, and mode of arrival via private patient transport were most predictive of the need for admission.

Conclusions: We developed a model that accurately predicts admission for patients presenting to the ED using demographic, administrative, and clinical data routinely collected at triage. Implementation of the model into the electronic health record could help reduce the burden of overcrowding.
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http://dx.doi.org/10.1016/j.ajem.2018.10.060DOI Listing
August 2019

Health impacts of the Southeast Asian haze problem - A time-stratified case crossover study of the relationship between ambient air pollution and sudden cardiac deaths in Singapore.

Int J Cardiol 2018 Nov;271:352-358

Department of Emergency Medicine, Singapore General Hospital, Outram Road, 169608, Singapore; Health Services & Systems Research, Duke-NUS Medical School, 8 College Road, 169857, Singapore. Electronic address:

Objectives: To investigate the association between air pollution and out-of-hospital cardiac arrest (OHCA) incidence in Singapore.

Design: A time-stratified case-crossover design study.

Setting: OHCA incidences of all etiology in Singapore.

Participants: 8589 OHCA incidences reported to Pan-Asian Resuscitation Outcomes Study (PAROS) registry in Singapore between 2010 and 2015.

Main Outcome Measures: A conditional Poisson regression model was applied to daily OHCA incidence that included potential confounders such as daily temperature, rainfall, wind speed, Pollutant Standards Index (PSI) and age. All models were adjusted for over-dispersion, autocorrelation and population at risk. We assessed the relationship with OHCA incidence and PSI in the entire cohort and in predetermined subgroups of demographic and clinical characteristics.

Results: 334 out of 8589 (3.89%) cases survived. Moderate (Risk ratio/RR = 1.1, 95% CI = 1.07-1.15) and unhealthy (RR =1.37, 95% CI = 1.2-1.56) levels of PSI showed significant association with increased OHCA occurrence. Sub-group analysis based on individual demographic and clinical features showed generally significant association between OHCA incidence and moderate/unhealthy PSI, except in age < 65, Malay and other ethnicity, traumatic arrests and history of heart disease and diabetes. The association was most pronounced among cases age > 65, male, Indian and non-traumatic. Each increment of 30 unit in PSI on the same day and previous 1-5 days was significantly associated with 5.8-8.1% increased risk of OHCA (p < 0.001).

Conclusions: We found a transient effect of short-term air pollution on OHCA incidence after adjusting for meteorological indicators and individual characteristics. These finding have public health implications for prevention of OHCA and emergency health services during haze.
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http://dx.doi.org/10.1016/j.ijcard.2018.04.070DOI Listing
November 2018

Automating the Renal Cell Carcinoma Registry in Singapore: A Case Study on the Integration of the Research Electronic Data Capture System with the Enterprise Data Warehouse.

J Registry Manag 2018 ;45(4):156-160

The renal cell carcinoma registry (RCCR) at the Singapore General Hospital was established in the 1980s. In 2012, the registry transited to a partially automated system using Research Electronic Data Capture (REDCap) and Oracle Business Intelligence Enterprise Edition (OBIEE), which is a platform for retrieval of electronic data from the Electronic Health Intelligence System (eHIntS). A committee was formed of experts from the department of urology and the health services research center, as well as an information technology (IT) team to evaluate the efficacy of the partially automated system. In the 5 years after the new system was implemented, 1,751 cases were recorded in the RCCR. The casefinding completeness increased by 1.9%, the data accuracy rate was 97%, and the efficiency increased by 12%. Strengths of the new system after partial automation were: (1) secure access to the registry via the hospital Web, (2) direct access to REDCap via the electronic medical records system, (3) automated and timely data extraction, and (4) visual presentation of data. On the other hand, we also encountered several challenges in the process of automating the registry, including limited IT support, limited expertise in matching data variables from RCCR and eHIntS, and limited availability and accessibility of eHIntS information for import into REDCap. In summary, despite these challenges, partial automation was achieved with the REDCap/OBIEE system, enhancing efficiency, data security, and data quality.
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January 2018

Simulation-based decision support framework for dynamic ambulance redeployment in Singapore.

Int J Med Inform 2017 10 30;106:37-47. Epub 2017 Jun 30.

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:

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.
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http://dx.doi.org/10.1016/j.ijmedinf.2017.06.005DOI Listing
October 2017

Is bronchial thermoplasty cost-effective as treatment for problematic asthma patients? Singapore's perspective on a global model.

Respirology 2017 08 31;22(6):1102-1109. Epub 2017 Mar 31.

Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore.

Background And Objective: Bronchial thermoplasty (BT) has been shown to be effective at reducing asthma exacerbations and improving asthma control for patients with severe persistent asthma but it is also expensive. Evidence on its cost-effectiveness is limited and inconclusive. In this study, we aim to evaluate the incremental cost-effectiveness of BT combined with optimized asthma therapy (BT-OAT) relative to OAT for difficult-to-treat and severe asthma patients in Singapore, and to provide a general framework for determining BT's cost-effectiveness in other healthcare settings.

Methods: We developed a Markov model to estimate the costs and quality-adjusted life years (QALYs) gained with BT-OAT versus OAT from the societal and health system perspectives. The model was populated using Singapore-specific costs and transition probabilities and utilities from the literature. Sensitivity analyses were conducted to identify the main factors determining cost-effectiveness of BT-OAT.

Results: BT-OAT is not cost-effective relative to OAT over a 5-year time horizon with an incremental cost-effectiveness ratio (ICER) of $US138 889 per QALY from the societal perspective and $US139 041 per QALY from the health system perspective. The cost-effectiveness of BT-OAT largely depends on a combination of the cost of the BT procedure and the cost of asthma-related hospitalizations and emergency department (ED) visits.

Conclusion: Based on established thresholds for cost-effectiveness, BT-OAT is not cost-effective compared with OAT in Singapore. Given its current clinical efficacy, BT-OAT is most likely to be cost-effective in a setting where the cost of BT procedure is low and costs of hospitalization and ED visits are high.
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http://dx.doi.org/10.1111/resp.13027DOI Listing
August 2017

Factors affecting the ambulance response times of trauma incidents in Singapore.

Accid Anal Prev 2015 Sep 27;82:27-35. Epub 2015 May 27.

Department of Emergency Medicine, Singapore General Hospital; Health Services Research and Biostatistics Unit, Division of Research, Singapore General Hospital; Health Services and Systems Research, Duke-NUS Graduate Medical School, 226 Outram Road, Singapore 169039, Singapore. Electronic address:

Objectives: Time to definitive care is important for trauma outcomes, thus many emergency medical services (EMS) systems in the world adopt response times of ambulances as a key performance indicator. The objective of this study is to examine the underlying risk factors that can affect ambulance response times (ART) for trauma incidents, so as to derive interventional measures that can improve the ART.

Material And Methods: This was a retrospective study based on two years of trauma data obtained from the national EMS operations centre of Singapore. Trauma patients served by the national EMS provider over the period from 1 January 2011 till 31 December 2012 were included. ART was categorized into "Short" (<4min), "Intermediate" (4-8min) and "Long" (>8min) response times. A modelling framework which leveraged on both multinomial logistic (MNL) regression models and Bayesian networks was proposed for the identification of main and interaction effects.

Results: Amongst the process-related risk factors, weather, traffic and place of incident were found to be significant. The traffic conditions on the roads were found to have the largest effect-the odds ratio (OR) of "Long" ART in heavy traffic condition was 12.98 (95% CI: 10.66-15.79) times higher than that under light traffic conditions. In addition, the ORs of "Long ART" under "Heavy Rain" condition were significantly higher (OR 1.58, 95% CI: 1.26-1.97) than calls responded under "Fine" weather. After accounting for confounders, the ORs of "Long" ART for trauma incidents at "Home" or "Commercial" locations were also significantly higher than that for "Road" incidents.

Conclusion: Traffic, weather and the place of incident were found to be significant in affecting the ART. The evaluation of factors affecting the ART enables the development of effective interventions for reducing the ART.
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http://dx.doi.org/10.1016/j.aap.2015.05.007DOI Listing
September 2015

Dynamic ambulance reallocation for the reduction of ambulance response times using system status management.

Am J Emerg Med 2015 Feb 8;33(2):159-66. Epub 2014 Nov 8.

Department of Emergency Medicine, Singapore General Hospital, Singapore; Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore. Electronic address:

Objectives: Dynamically reassigning ambulance deployment locations throughout a day to balance ambulance availability and demands can be effective in reducing response times. The objectives of this study were to model dynamic ambulance allocation plans in Singapore based on the system status management (SSM) strategy and to evaluate the dynamic deployment plans using a discrete event simulation (DES) model.

Methods: The geographical information system-based analysis and mathematical programming were used to develop the dynamic ambulance deployment plans for SSM based on ambulance calls data from January 1, 2011, to June 30, 2011. A DES model that incorporated these plans was used to compare the performance of the dynamic SSM strategy against static reallocation policies under various demands and travel time uncertainties.

Results: When the deployment plans based on the SSM strategy were followed strictly, the DES model showed that the geographical information system-based plans resulted in approximately 13-second reduction in the median response times compared to the static reallocation policy, whereas the mathematical programming-based plans resulted in approximately a 44-second reduction. The response times and coverage performances were still better than the static policy when reallocations happened for only 60% of all the recommended moves.

Conclusions: Dynamically reassigning ambulance deployment locations based on the SSM strategy can result in superior response times and coverage performance compared to static reallocation policies even when the dynamic plans were not followed strictly.
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http://dx.doi.org/10.1016/j.ajem.2014.10.044DOI Listing
February 2015

Application of Queuing Analytic Theory to Decrease Waiting Times in Emergency Department: Does it Make Sense?

Arch Trauma Res 2013 Dec 1;2(3):136-7. Epub 2013 Dec 1.

Department of Emergency Medicine, Singapore General Hospital, Singapore.

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http://dx.doi.org/10.5812/atr.11376DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950918PMC
December 2013

Reducing ambulance response times using discrete event simulation.

Prehosp Emerg Care 2014 Apr-Jun;18(2):207-16. Epub 2013 Oct 17.

from the Health Services Research and Biostatistics Unit, Division of Research, Singapore General Hospital , Singapore (SSWL) , Department of Emergency Medicine, Singapore General Hospital , Singapore (ZCZ, MEHO) , Centre for Health Services Research, Singapore Health Services Pte Ltd , Singapore (HCO) , Medical Department, Singapore Civil Defence Force , Singapore (YYN) , Centre for Infectious Disease Epidemiology and Research, Saw Swee Hock School of Public Health, National University of Singapore , Singapore (WW) .

Objectives: The objectives of this study are to develop a discrete-event simulation (DES) model for the Singapore Emergency Medical Services (EMS), and to demonstrate the utility of this DES model for the evaluation of different policy alternatives to improve ambulance response times.

Methods: A DES model was developed based on retrospective emergency call data over a continuous 6-month period in Singapore. The main outcome measure is the distribution of response times. The secondary outcome measure is ambulance utilization levels based on unit hour utilization (UHU) ratios. The DES model was used to evaluate different policy options in order to improve the response times, while maintaining reasonable fleet utilization.

Results: Three policy alternatives looking at the reallocation of ambulances, the addition of new ambulances, and alternative dispatch policies were evaluated. Modifications of dispatch policy combined with the reallocation of existing ambulances were able to achieve response time performance equivalent to that of adding 10 ambulances. The median (90th percentile) response time was 7.08 minutes (12.69 minutes). Overall, this combined strategy managed to narrow the gap between the ideal and existing response time distribution by 11-13%. Furthermore, the median UHU under this combined strategy was 0.324 with an interquartile range (IQR) of 0.047 versus a median utilization of 0.285 (IQR of 0.051) resulting from the introduction of additional ambulances.

Conclusions: Response times were shown to be improved via a more effective reallocation of ambulances and dispatch policy. More importantly, the response time improvements were achieved without a reduction in the utilization levels and additional costs associated with the addition of ambulances. We demonstrated the effective use of DES as a versatile platform to model the dynamic system complexities of Singapore's national EMS systems for the evaluation of operational strategies to improve ambulance response times.
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http://dx.doi.org/10.3109/10903127.2013.836266DOI Listing
November 2014
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