The Telemedicine Demand Index and its Utility in Managing COVID-19 Case Surges.

Introduction: Telemedicine was an integral component in Singapore's COVID-19 management strategy, having been deployed at a national level in a centrally-administered program whereby patients at higher risk of developing severe COVID-19 disease were proactively assigned tele-consultations, whereas those at lower risk and seen by primary care physicians could request ad hoc tele-consultations. To better plan for fluctuations in telemedicine demand during the pandemic, the Telemedicine Demand Index (TDI) was developed. Methods: Three main factors influencing telemedicine demand were considered-characteristics of the Variant of Concern, prevailing health care policies, and the population's healthcare-seeking behaviour-from which 11 coefficients were derived for the TDI formula. The number of tele-consultations demanded is the product of the TDI and the total number of new COVID-19 cases for a given period. Results: Real-world data from January 31 to March 27, 2022 were compared with TDI estimates. A total of 148,485 tele-consultations were conducted against a backdrop of 723,675 new COVID-19 cases for the period. The TDI overestimated demand by an average 11.4%. Data from March 28 to May 1, 2022 were then used to derive new TDI values and applied to a 3-week period starting May 9, 2022, following a policy change. A total of 5,560 tele-consultations were conducted against a backdrop of 77,998 new COVID-19 cases. The TDI underestimated demand by an average of 7.2%. Conclusion: The TDI shows initial promise for quickly estimating telemedicine demand at a population level. By leveraging historical data and applying some informed assumptions, it allows for the estimation of current capabilities and future requirements. There remains scope for more research to refine the TDI's constituent components, as well as its applicability in different population contexts.

Genesis, evolution and effectiveness of Singapore's national sorting logic and home recovery policies in handling the COVID-19 Delta and Omicron waves.

Singapore developed several novel strategies to transition towards "living with COVID-19", while protecting hospital capacity. The Home Recovery Programme (HRP) was a national, centrally-administered programme that leveraged technology and telemedicine to allow low-risk individuals to safely recover at home. The HRP was subsequently expanded by partnering primary care doctors in caring for more cases in the community. A key enabler was the National Sorting Logic (NSL), a multi-step triage algorithm allowing risk-stratification of large numbers of COVID-19 patients at a national-level. At the core of the NSL was a risk assessment criterion, comprising of Comorbidities-of-concern, Age, Vaccination status, Examination/clinical findings and Symptoms (CAVES). The NSL sorted all COVID-19 cases into the various levels of care - Primary Care, HRP, COVID-19 Treatment Facility and Hospital. By adopting a national approach towards managing healthcare capacities and triaging COVID-19 patients, Singapore was able to prioritize healthcare resources for high-risk individuals and prevent hospital capacities from being overwhelmed. As part of the national response strategy to tackle COVID-19, Singapore set up and integrated key national databases to enable responsive data analysis and support evidence-based policy decisions. Using data collected between 30 August 2021 to 8 June 2022, we conducted a retrospective cohort study to evaluate the outcomes and effectiveness of vaccination policies, NSL and home-based recovery. A total of 1,240,183 COVID-19 cases were diagnosed during this period, spanning both Delta and Omicron waves, Overall, Singapore experienced very low severity (0.51%) and mortality (0.11%) rates. Vaccinations significantly lowered severity and mortality risks across all age groups. The NSL was effective in predicting risk of severe outcomes and was able to right-site >93% of cases into home-based recovery. By leveraging high vaccination rates, technology and telemedicine, Singapore was able to safely navigate through two COVID-19 waves without impacting severity/mortality rates nor overwhelming hospital capacities.

The Singapore National Precision Medicine Strategy.

Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic-phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world's population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption.

Singapore's experience in managing the COVID-19 pandemic: Key lessons from the ground.

Singapore managed the COVID-19 pandemic in the past three years and gleaned valuable lessons on patient management when the public healthcare system was inundated with COVID-19 patients. There were several initiatives, which included setting up of community treatment facilities to help hospitals manage in-patient loads that did not require acute monitoring, leveraging telemedicine, and developing heuristics to sort patients based on their clinical disposition to various care pathways and to effectively manage patients of different medical needs. These initiatives were implemented in the second year of the epidemic in 2021 and did not include the dormitory-based migrant workers and migrant workers in the construction, maritime and production sectors who were under the care of the Assurance, Care and Engagement Group (ACE) in the Ministry of Manpower that had its own set of treatment management measures. The different care pathways ensured that patients received appropriate levels of care and allowed healthcare facilities to focus on more acute cases. In 2022 alone, 23,159 patients were discharged from community treatment facilities against the background of 1.9 million COVID-19 patients. These initiatives would not be possible without the oversight of an advisory board comprising senior leadership from the healthcare clusters and the Ministry of Health to align clinical governance with medical policies, and prompt and immense support from medical specialist panels. The strong public-private partnership forged in the process was instrumental in the successful operation of community facilities and implementation of patient care protocols, coupled with harnessing information technology and leveraging on emerging data to refine care protocols.

Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside.

Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside.

Acute appendicitis presenting with MIS-C secondary to COVID-19.

Acute Appendicitis (AA) is among the most common causes of abdominal pain in children. Several physical exam findings, scoring systems, and imaging studies, such as ultrasonography and computed tomography, exist to assist clinicians in diagnosing acute appendicitis. Despite multiple tools for assessing suspected acute appendicitis, it remains a challenge to diagnose acute appendicitis in the pediatric population. A challenge that becomes increasingly more difficult if presenting with a comorbid condition. With the emergence of coronavirus disease 2019 (COVID-19) and subsequent discovery of multisystem inflammatory syndrome in children (MIS-C), this case series presents three pediatric cases of acute appendicitis presenting concurrently with MIS-C secondary to prior COVID-19 infection thus illustrating potential complications to diagnosing and managing acute appendicitis.

Learning offline: memory replay in biological and artificial reinforcement learning.

Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial intelligence (AI) as a way to optimise decision making. A common aspect of both biological and machine reinforcement learning is the reactivation of previously experienced episodes, referred to as replay. Replay is important for memory consolidation in biological neural networks and is key to stabilising learning in deep neural networks. Here, we review recent developments concerning the functional roles of replay in the fields of neuroscience and AI. Complementary progress suggests how replay might support learning processes, including generalisation and continual learning, affording opportunities to transfer knowledge across the two fields to advance the understanding of biological and artificial learning and memory.

Dynamic Kinetic Asymmetric Amination of Alcohols: From A Mixture of Four Isomers to Diastereo- and Enantiopure α-Branched Amines.

The first dynamic kinetic asymmetric amination of alcohols via borrowing hydrogen methodology is presented. Under the cooperative catalysis by an iridium complex and a chiral phosphoric acid, α-branched alcohols that exist as a mixture of four isomers undergo racemization by two orthogonal mechanisms and are converted to diastereo- and enantiopure amines bearing adjacent stereocenters. The preparation of diastereo- and enantiopure 1,2-amino alcohols is also realized using this catalytic system.