Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution.

INTRODUCTION: Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reducing fatigue and the time needed for diagnosis. They serve as a decision support system, partially automating the diagnosis process by providing probability predictions for abnormalities. METHODS: We demonstrated the use of deep learning models in CE image analysis, specifically by piloting a bowel preparation model (BPM) and an abnormality detection model (ADM) to determine frame-level view quality and the presence of abnormal findings, respectively. We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. We then combined the open-source Kvasir-Capsule dataset (n = 43) and locally collected CE data (n = 29). RESULTS: Model performance was compared using averaged five-fold and two-fold cross-validation for BPMs and ADMs, respectively. The best BPM model based on a pre-trained ResNet50 architecture had an area under the receiver operating characteristic and precision-recall curves of 0.969±0.008 and 0.843±0.041, respectively. The best ADM model, also based on ResNet50, had top-1 and top-2 accuracies of 84.03±0.051 and 94.78±0.028, respectively. The models could process approximately 200-250 images per second and showed good discrimination on time-critical abnormalities such as bleeding. CONCLUSION: Our pilot models showed the potential to improve time to diagnosis in CE workflows. To our knowledge, our approach is unique to the Singapore context. The value of our work can be further evaluated in a pragmatic manner that is sensitive to existing clinician workflow and resource constraints.

The impact of elective surgery postponement during COVID-19 on emergency bellwether procedures in a large tertiary centre in Singapore.

The coronavirus disease 2019 (COVID-19) pandemic drove many healthcare systems worldwide to postpone elective surgery to increase healthcare capacity, manpower, and reduce infection risk to staff. The aim of this study was to assess the impact of an elective surgery postponement policy in response to the COVID-19 pandemic on surgical volumes and patient outcomes for three emergency bellwether procedures. A retrospective cohort study of patients who underwent any of the three emergency procedures [Caesarean section (CS), emergency laparotomy (EL), and open fracture (OF) fixation] between 1 January 2018 and 31 December 2021 was conducted using clinical and surgical data from electronic medical records. The volumes and outcomes of each surgery were compared across four time periods: pre-COVID (January 2018-January 2020), elective postponement (February-May 2020), recovery (June-November 2020), and postrecovery (December 2020-December 2021) using Kruskal-Wallis test and segmented negative binomial regression. There was a total of 3886, 1396, and 299 EL, CS, and OF, respectively. There was no change in weekly volumes of CS and OF fixations across the four time periods. However, the volume of EL increased by 47% [95% confidence interval: 26-71%, P = 9.13 × 10-7) and 52% (95% confidence interval: 25-85%, P = 3.80 × 10-5) in the recovery and postrecovery period, respectively. Outcomes did not worsen throughout the four time periods for all three procedures and some actually improved for EL from elective postponement onwards. Elective surgery postponement in the early COVID-19 pandemic did not affect volumes of emergency CS and OF fixations but led to an increase in volume for EL after the postponement without any worsening of outcomes.

Frameworks for measuring population health: A scoping review.

INTRODUCTION: Many regions in the world are using the population health approach and require a means to measure the health of their population of interest. Population health frameworks provide a theoretical grounding for conceptualization of population health and therefore a logical basis for selection of indicators. The aim of this scoping review was to provide an overview and summary of the characteristics of existing population health frameworks that have been used to conceptualize the measurement of population health. METHODS: We used the Population, Concept and Context (PCC) framework to define eligibility criteria of frameworks. We were interested in frameworks applicable for general populations, that contained components of measurement of health with or without its antecedents and applied at the population level or used a population health approach. Eligible reports of eligible frameworks should include at least domains and subdomains, purpose, or indicators. We searched 5 databases (Pubmed, EMBASE, Web of Science, NYAM Grey Literature Report, and OpenGrey), governmental and organizational sites on Google and websites of selected organizations using keywords from the PCC framework. Characteristics of the frameworks were summarized descriptively and narratively. RESULTS: Fifty-seven frameworks were included. The majority originated from the US (46%), Europe (23%) and Canada (19%). Apart from 1 framework developed for rural populations and 2 for indigenous populations, the rest were for general urban populations. The numbers of domains, subdomains and indicators were highly variable. Health status and social determinants of health were the most common domains across all frameworks. Different frameworks had different priorities and therefore focus on different domains. CONCLUSION: Key domains common across frameworks other than health status were social determinants of health, health behaviours and healthcare system performance. The results in this review serve as a useful resource for governments and healthcare organizations for informing their population health measurement efforts.

COVID-19 Pandemic Simulation Modelling in Anaesthesia Residency Training to Predict Delays and Workforce Deficiencies: A Case Study of the Singapore Residency Training Program.

Background COVID-19 has been the worst pandemic of this century, resulting in economic, social, and educational disruptions. Residency training is no exception, with training restrictions delaying the progression and graduation of residents. We sought to utilize simulation modelling to predict the impact on future cohorts in the event of repeated and prolonged movement restrictions due to COVID-19 and future pandemics of a similar nature. Method A Delphi study was conducted to determine key Accreditation Council for Graduate Medical Education-International (ACGME-I) training variables affected by COVID-19. Quantitative resident datasets on these variables were collated and analysed from 2018 to 2021. Using the Vensim® software (Ventana Systems, Inc., Harvard, MA), historical resident data and pandemic progression delays were used to create a novel simulation model to predict future progression delay. Various durations of delay were also programmed into the software to simulate restrictions of varying severity that would impact resident progression. Results Using the model with scenarios simulating varying pandemic length, we found that the estimated average delay for residents in each accredited year ranged from an increase of one month for year 2 residents to more than three months for year 4 residents. Movement restrictions lasting a year would require up to six years before the program returned to a pre-pandemic equilibrium. Conclusion Systems dynamic modelling can be used to predict delays in residency training programs during a pandemic. The impact on the workforce can thus be projected, allowing residency programs to institute mitigating measures to avoid progression delay.

AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.

Aim: Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field. Main text: We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models. Conclusion: In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.

Development of a real-world database for asthma and COPD: The SingHealth-Duke-NUS-GSK COPD and Asthma Real-World Evidence (SDG-CARE) collaboration.

PURPOSE: The SingHealth-Duke-GlaxoSmithKline COPD and Asthma Real-world Evidence (SDG-CARE) collaboration was formed to accelerate the use of Singaporean real-world evidence in research and clinical care. A centerpiece of the collaboration was to develop a near real-time database from clinical and operational data sources to inform healthcare decision making and research studies on asthma and chronic obstructive pulmonary disease (COPD). METHODS: Our multidisciplinary team, including clinicians, epidemiologists, data scientists, medical informaticians and IT engineers, adopted the hybrid waterfall-agile project management methodology to develop the SingHealth COPD and Asthma Data Mart (SCDM). The SCDM was developed within the organizational data warehouse. It pulls and maps data from various information systems using extract, transform and load (ETL) pipelines. Robust user testing and data verification was also performed to ensure that the business requirements were met and that the ETL pipelines were valid. RESULTS: The SCDM includes 199 data elements relevant to asthma and COPD. Data verification was performed and found the SCDM to be reliable. As of December 31, 2019, the SCDM contained 36,407 unique patients with asthma and COPD across the spectrum from primary to tertiary care in our healthcare system. The database updates weekly to add new data of existing patients and to include new patients who fulfil the inclusion criteria. CONCLUSIONS: The SCDM was systematically developed and tested to support the use RWD for clinical and health services research in asthma and COPD. This can serve as a platform to provide research and operational insights to improve the care delivered to our patients.

Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study.

The scheduling of operating room (OR) slots requires the accurate prediction of surgery duration. We evaluated the performance of existing Moving Average (MA) based estimates with novel machine learning (ML)-based models of surgery durations across two sites in the US and Singapore. We used the Duke Protected Analytics Computing Environment (PACE) to facilitate data-sharing and big data analytics across the US and Singapore. Data from all colorectal surgery patients between 1 January 2012 and 31 December 2017 in Singapore and, 1 January 2015 to 31 December 2019 in the US were used, and 7585 cases and 3597 single and multiple procedure cases from Singapore and US were included. The ML models were based on categorical gradient boosting (CatBoost) models trained on common data fields shared by both institutions. The procedure codes were based on the Table of Surgical Procedure (TOSP) (Singapore) and the Current Procedural Terminology (CPT) codes (US). The two types of codes were mapped by surgical experts. The CPT codes were then transformed into the relative value unit (RVU). The ML models outperformed the baseline MA models. The MA, scheduled durations and procedure codes were found to have higher loadings as compared to surgeon factors. We further demonstrated the use of the Duke PACE in facilitating data-sharing and big data analytics.

An Agile Systems Modeling Framework for Bed Resource Planning During COVID-19 Pandemic in Singapore.

Background: The COVID-19 pandemic has had a major impact on health systems globally. The sufficiency of hospitals' bed resource is a cornerstone for access to care which can significantly impact the public health outcomes. Objective: We describe the development of a dynamic simulation framework to support agile resource planning during the COVID-19 pandemic in Singapore. Materials and Methods: The study data were derived from the Singapore General Hospital and public domain sources over the period from 1 January 2020 till 31 May 2020 covering the period when the initial outbreak and surge of COVID-19 cases in Singapore happened. The simulation models and its variants take into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes in Singapore. Results: The models were calibrated against historical data for the Singapore COVID-19 situation. Several variants of the resource planning model were rapidly developed to adapt to the fast-changing COVID-19 situation in Singapore. Conclusion: The agility in adaptable models and robust collaborative management structure enabled the quick deployment of human and capital resources to sustain the high level of health services delivery during the COVID-19 surge.

Multifactorial influences underpinning a decision on COVID-19 vaccination among healthcare workers: a qualitative analysis.

COVID-19 vaccination in healthcare workers (HCW) is essential for improved patient safety and resilience of health systems. Despite growing body of literature on the perceptions of COVID vaccines in HCWs, existing studies tend to focus on reasons for 'refusing' the vaccines, using surveys almost exclusively. To gain a more nuanced understanding, we explored multifactorial influences underpinning a decision on vaccination and suggestions for decision support to improve vaccine uptake among HCWs in the early phase of vaccination rollout. Semi-structured interviews were undertaken with thirty-three HCWs in Singapore. Transcribed data was thematically analyzed. Decisions to accept vaccines were underpinned by a desire to protect patients primarily driven by a sense of professional integrity, collective responsibility to protect others, confidence in health authorities and a desire to return to a pre-pandemic way of life. However, there were prevailing concerns with respect to the vaccines, including long-term benefits, safety and efficacy, that hampered a decision. Inadequate information and social media representation of vaccination appeared to add to negative beliefs, impeding a decision to accept while low perceived susceptibility played a moderate role in the decision to delay or decline vaccination. Participants made valuable suggestions to bolster vaccination. Our findings support an approach to improving vaccine uptake in HCWs that features routine tracking and transparent updates on vaccination status, use of institutional platforms for sharing of experience, assuring contingency management plans and tailored communications to emphasize the duty of care and positive outlook associated with vaccination.

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.

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.

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

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.

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

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.

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

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.

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

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.

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.

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.

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.

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.

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

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.

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

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.

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

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.