Browsing by Author "Lam, Sean Shao Wei"

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    An Agile Systems Modeling Framework for Bed Resource Planning During COVID-19 Pandemic in Singapore.
    (Frontiers in public health, 2022-01) Lam, Sean Shao Wei; Pourghaderi, Ahmad Reza; Abdullah, Hairil Rizal; Nguyen, Francis Ngoc Hoang Long; Siddiqui, Fahad Javaid; Ansah, John Pastor; Low, Jenny G; Matchar, David Bruce; Ong, Marcus Eng Hock

    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.
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    Association of quality-of-care indicators with asthma outcomes: A retrospective observational study for asthma care in Singapore
    (Annals of the Academy of Medicine Singapore, 2023-10-01) Lam, Sean Shao Wei; Chen, Jingwei; Wu, Jun Tian; Lee, Chun Fan; Ragavendran, Narayanan; Ong, Marcus Eng Hock; Tan, Ngiap Chuan; Loo, Chian Min; Matchar, David Bruce; Koh, Mariko Siyue
    Introduction: Asthma guidelines have advocated for the use of quality-of-care indicators (QCIs) in asthma management. To improve asthma care, it is important to identify effective QCIs that are actionable. This study aimed to evaluate the effect of the presence of 3 QCIs: asthma education, Asthma Control Test (ACT) and spirometry testing on the time to severe exacerbation (TTSE). Method: Data collected from the SingHealth COPD and Asthma Data Mart (SCDM), including asthma patients managed in 9 SingHealth polyclinics and Singapore General Hospital from January 2015 to December 2020, were analysed. Patients receiving Global Initiative for Asthma (GINA) Steps 3–5 treatment, with at least 1 QCI recorded, and at least 1 severe exacerbation within 1 year before the first QCI record, were included. Data were analysed using multivariate Cox regression and quasi-Poisson regression models. Results: A total of 3849 patients in the registry fulfilled the criteria. Patients with records of asthma education or ACT assessment have a lower adjusted hazard ratio (HR) for TTSE (adjusted HR=0.88, P=0.023; adjusted HR=0.83, P<0.001). Adjusted HR associated with spirometry is higher (adjusted HR=1.22, P=0.026). No QCI was significantly associated with emergency department (ED)/inpatient visits. Only asthma education and ACT showed a decrease in the number of exacerbations for multivariate analysis (asthma education estimate:-0.181, P<0.001; ACT estimate:-0.169, P<0.001). No QCI was significant for the number of exacerbations associated with ED/inpatient visits. Conclusion: Our study suggests that the performance of asthma education and ACT was associated with increased TTSE and decreased number of exacerbations, underscoring the importance of ensuring quality care in clinical practice.
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    Coronavirus disease 2019 (COVID-19): an evidence map of medical literature.
    (BMC medical research methodology, 2020-07-02) Liu, Nan; Chee, Marcel Lucas; Niu, Chenglin; Pek, Pin Pin; Siddiqui, Fahad Javaid; Ansah, John Pastor; Matchar, David Bruce; Lam, Sean Shao Wei; Abdullah, Hairil Rizal; Chan, Angelique; Malhotra, Rahul; Graves, Nicholas; Koh, Mariko Siyue; Yoon, Sungwon; Ho, Andrew Fu Wah; Ting, Daniel Shu Wei; Low, Jenny Guek Hong; Ong, Marcus Eng Hock

    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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    Development of a real-world database for asthma and COPD: The SingHealth-Duke-NUS-GSK COPD and Asthma Real-World Evidence (SDG-CARE) collaboration.
    (BMC medical informatics and decision making, 2023-01) Lam, Sean Shao Wei; Fang, Andrew Hao Sen; Koh, Mariko Siyue; Shantakumar, Sumitra; Yeo, See-Hwee; Matchar, David Bruce; Ong, Marcus Eng Hock; Poon, Ken Mei Ting; Huang, Liming; Harikrishan, Sudha; Milea, Dominique; Burke, Des; Webb, Dave; Ragavendran, Narayanan; Tan, Ngiap Chuan; Loo, Chian Min

    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.
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    Multifactorial influences underpinning a decision on COVID-19 vaccination among healthcare workers: a qualitative analysis.
    (Human vaccines & immunotherapeutics, 2022-06-10) Yoon, Sungwon; Goh, Hendra; Matchar, David; Sung, Sharon C; Lum, Elaine; Lam, Sean Shao Wei; Low, Jenny Guek Hong; Chua, Terrance; Graves, Nicholas; Ong, Marcus Eh
    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.
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    Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study.
    (BMJ open, 2019-09-26) Xie, Feng; Liu, Nan; Wu, Stella Xinzi; Ang, Yukai; Low, Lian Leng; Ho, Andrew Fu Wah; Lam, Sean Shao Wei; Matchar, David Bruce; Ong, Marcus Eng Hock; Chakraborty, Bibhas
    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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    Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms.
    (International journal of medical informatics, 2021-12-14) Abdullah, Hairil Rizal; Lam, Sean Shao Wei; Ang, Boon Yew; Pourghaderi, Ahmadreza; Nguyen, Francis Ngoc Hoang Long; Matchar, David Bruce; Tan, Hiang Khoon; Ong, Marcus Eng Hock

    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.