Aim To understand the impact of COVID‐19 on isolation bed capacity requirements, nursing workforce requirements and nurse:patient ratios. Background COVID‐19 created an increased demand for isolation beds and nursing workforce globally. Methods This was a retrospective review of bed capacity, bed occupancy and nursing workforce data from the isolation units of a tertiary hospital in Singapore from 23 January 2020 to 31 May 2020. R v4.0.1 and Tidyverse 1.3.0 library were used for data cleaning and plotly 4.9.2.1 library for data visualization. Results In January to March 2020, isolation bed capacity was low (=<203 beds). A sharp increase in bed capacity was seen from 195 to 487 beds during 25 March to 29 April 2020, after which it plateaued. Bed occupancy remained lower than bed capacity throughout January to May 2020. After 16 April 2020, we experienced a shortage of 1.1 to 70.2 nurses in isolation wards. Due to low occupancy rates, nurse:patient ratio remained acceptable (minimum nurse:patient ratio = 0.26). Conclusion COVID‐19 caused drastic changes in isolation bed capacity and nursing workforce requirements. Implications for Nursing Management Building a model to predict nursing workforce requirements during pandemic surges may be helpful for planning and adequate staffing.

In this article, a system dynamics model is developed to study the complex issues involving nuclear energy in Singapore by assessing four essential aspects, namely (1) Economic, (2) Environment, (3) Social, and (4) Political in various scenarios. The first factor juxtaposes the monetary benefit from deploying nuclear energy with the current situation. The second deals with carbon dioxide emission, nuclear waste, and thermal pollution from nuclear power plant. The third part discusses the safety and social acceptance toward nuclear project, and the last section analyzes the political stability in provision of nuclear energy advent. The proposed system dynamic model incorporates all four components that allow us to run sensitivity analysis based on various scenarios and enables us to gain insight in how each domain evolves with time. Based on the model, we present advantages and disadvantages of possessing nuclear power plants in Singapore and suggest remedies to rectify the drawbacks.

Introduction: Primary care physicians face the increasing burden of managing multimorbidities in an ageing population. Implementing an integrated care team (ICT) with defined roles and accountability to share consultation tasks is an emerging care model to address this issue. This study compared outcomes with ICT versus usual care for patients with multimorbidities in primary care. Methods: Data was retrospectively extracted from the electronic medical records (EMRs) of consecutive adult Asian patients empanelled to ICT and those in UC at a typical primary care clinic (polyclinic) in eastern Singapore in 2018. The study population had hypertension, and/or hyperlipidaemia and/or type 2 diabetes mellitus (T2DM). Clinical outcomes included the proportion of patients (ICT vs. UC) who attained their treatment goals after 12 months. Process outcomes included the proportion of patients who completed annual diabetic eye and foot screenings, where applicable. Results: Data from 3,302 EMRs (ICT = 1,723, UC = 1,579) from January 2016 to September 2017 was analysed. The ICT cohort was more likely to achieve treatment goals for systolic blood pressure (SBP) (adjusted odds ratio [AOR] = 1.52, 95% confidence interval [CI] = 1.38–1.68), low-density lipoprotein cholesterol (AOR = 1.72, 95% CI = 1.49–1.99), and glycated haemoglobin (AOR = 1.28, 95% CI = 1.09–1.51). The ICT group had higher uptake of diabetic retinal screening (89.1% vs. 83.0%, p < 0.001) and foot screening (85.2% vs. 77.9%, p < 0.001). Conclusion: The ICT model yielded better clinical and process outcomes than UC, with more patients attaining treatment goals.

Background: We describe the development of a dynamic simulation modelling framework to support agile resource planning during the COVID-19 pandemic. The framework takes into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes to deal with the ever-changing outbreak scenarios.Methods: A specific use case based on short-term bed resource planning is described within the proposed framework. The simulation model was calibrated against historical data for the Singapore COVID-19 situation. The time period for model calibration was from 1st April till 30th April 2020. The model was used to project for bed resource needs over the period from 1st May 2020 till 31st May 2020. Multivariate sensitivity analysis was also conducted for ICU and general isolation bed demand, length-of-stay (LOS), and age-adjusted conversion rates across different care needs. The unmet needs under various scenarios were also evaluated for planning purposes.Results: Several variants of the agile resource planning model were developed to adapt to the fast-changing COVID-19 situation in Singapore. The use case demonstrated an agile adaptation of the model to account for previously unexpected scenarios. The rapid evolution of the pandemic locally revealed streams of new infections that arose from two distinct sources. The model projections were calibrated with the latest data for short-term projections. The agility in flexing plans and collaborative management structures to rapidly deploy human and capital resources to surge the level of care during the COVID-19 pandemic have proven utility in guiding the allocation of scarce healthcare resources and helped system resiliency.Conclusions: The rapidly evolving COVID-19 pandemic in Singapore has necessitated the development of an agile and adaptable modelling framework that can be quickly calibrated to changes both from demand and supply. The modelling framework is able to deploy systems modelling concepts in a holistic manner. This facilitates the evaluation of complex cause-and-effect relationships. A robust collaborative framework, coupled with the availability of in-depth domain knowledge and accurate and updated data availability ensures a model is realistic, timely and useful.