Did lockdown influence bystanders' willingness to perform cardiopulmonary resuscitation? A worldwide registry-based perspective.

AIM: Bystander cardiopulmonary resuscitation (CPR) significantly increases the survival rate after out-of-hospital cardiac arrest. Using population-based registries, we investigated the impact of lockdown due to Covid-19 on the provision of bystander CPR, taking background changes over time into consideration. METHODS: Using a registry network, we invited all registries capable of delivering data from 1. January 2017 to 31. December 2020 to participate in this study. We used negative binominal regression for the analysis of the overall results. We also calculated the rates for bystander CPR. For every participating registry, we analysed the incidence per 100000 inhabitants of bystander CPR and EMS-treated patients using Poisson regression, including time trends. RESULTS: Twenty-six established OHCA registries reported 742 923 cardiac arrest patients over a four-year period covering 1.3 billion person-years. We found large variations in the reported incidence between and within continents. There was an increase in the incidence of bystander CPR of almost 5% per year. The lockdown in March/April 2020 did not impact this trend. The increase in the rate of bystander CPR was also seen when analysing data on a continental level. We found large variations in incidence of bystander CPR before and after lockdown when analysing data on a registry level. CONCLUSION: There was a steady increase in bystander CPR from 2017 to 2020, not associated with an increase in the number of ambulance-treated cardiac arrest patients. We did not find an association between lockdown and bystanders' willingness to start CPR before ambulance arrival, but we found inconsistent patterns of changes between registries.

Benchmarking emergency department prediction models with machine learning and public electronic health records.

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop prediction models and decision support systems to address these challenges. To date, there is no widely accepted clinical prediction benchmark related to the ED based on large-scale public EHRs. An open-source benchmark data platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. Based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we created a benchmark dataset and proposed three clinical prediction benchmarks. This study provides future researchers with insights, suggestions, and protocols for managing data and developing predictive tools for emergency care.

An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department.

Emergency departments (EDs) are experiencing complex demands. An ED triage tool, the Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable machine learning framework. It achieved a good performance in the Singapore population. We aimed to externally validate the SERP in a Korean cohort for all ED patients and compare its performance with Korean triage acuity scale (KTAS). This retrospective cohort study included all adult ED patients of Samsung Medical Center from 2016 to 2020. The outcomes were 30-day and in-hospital mortality after the patients' ED visit. We used the area under the receiver operating characteristic curve (AUROC) to assess the performance of the SERP and other conventional scores, including KTAS. The study population included 285,523 ED visits, of which 53,541 were after the COVID-19 outbreak (2020). The whole cohort, in-hospital, and 30 days mortality rates were 1.60%, and 3.80%. The SERP achieved an AUROC of 0.821 and 0.803, outperforming KTAS of 0.679 and 0.729 for in-hospital and 30-day mortality, respectively. SERP was superior to other scores for in-hospital and 30-day mortality prediction in an external validation cohort. SERP is a generic, intuitive, and effective triage tool to stratify general patients who present to the emergency department.

Management of Out-of-Hospital Cardiac Arrest during COVID-19: A Tale of Two Cities.

Variations in the impact of the COVID-19 pandemic on out-of-hospital cardiac arrest (OHCA) have been reported. We aimed to, using population-based registries, compare community response, Emergency Medical Services (EMS) interventions and outcomes of adult, EMS-treated, non-traumatic OHCA in Singapore and metropolitan Atlanta, before and during the pandemic. Associations of OHCA characteristics, pre-hospital interventions and pandemic with survival to hospital discharge were analyzed using logistic regression. There were 2084 cases during the pandemic (17 weeks from the first confirmed COVID-19 case) and 1900 in the pre-pandemic period (corresponding weeks in 2019). Compared to Atlanta, OHCAs in Singapore were older, received more bystander interventions (cardiopulmonary resuscitation (CPR): 65.0% vs. 41.4%; automated external defibrillator application: 28.6% vs. 10.1%), yet had lower survival (5.6% vs. 8.1%). Compared to the pre-pandemic period, OHCAs in Singapore and Atlanta occurred more at home (adjusted odds ratio (aOR) 2.05 and 2.03, respectively) and were transported less to hospitals (aOR 0.59 and 0.36, respectively) during the pandemic. Singapore reported more witnessed OHCAs (aOR 1.96) yet less bystander CPR (aOR 0.81) during pandemic, but not Atlanta (p < 0.05). The impact of COVID-19 on OHCA outcomes did not differ between cities. Changes in OHCA characteristics and management during the pandemic, and differences between Singapore and Atlanta were likely the result of systemic and sociocultural factors.

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.

The Psychological Well-Being of Southeast Asian Frontline Healthcare Workers during COVID-19: A Multi-Country Study.

OBJECTIVES: This study examined the prevalence of anxiety, depression, and job burnout among frontline healthcare workers (HCWs) across six Southeast Asian countries (Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam) during the COVID-19 pandemic in 2021. We also investigated the associated risk and protective factors. METHODS: Frontline HCWs (N = 1381) from the participating countries participated between 4 January and 14 June 2021. The participants completed self-reported surveys on anxiety (GAD-7), depression (PHQ-8), and job burnout (PWLS). Multivariate logistic regressions were performed with anxiety, depression, and job burnout as outcomes and sociodemographic and job characteristics and HCW perceptions as predictors. RESULTS: The average proportion of HCWs reporting moderate anxiety, moderately severe depression, and job burnout across all countries were 10%, 4%, and 20%, respectively. Working longer hours than usual (Odds ratio [OR] = 1.82; 3.51), perceived high job risk (1.98; 2.22), and inadequate personal protective equipment (1.89; 2.11) were associated with increased odds of anxiety and job burnout while working night shifts was associated with increased risk of depression (3.23). Perceived good teamwork was associated with lower odds of anxiety (0.46), depression (0.43), and job burnout (0.39). CONCLUSION: Job burnout remains a foremost issue among HCWs. Potential opportunities to improve HCW wellness are discussed.

The Psychological Well-Being of Southeast Asian Frontline Healthcare Workers during COVID-19: A Multi-Country Study

Objectives: This study examined the prevalence of anxiety, depression, and job burnout among frontline healthcare workers (HCWs) across six Southeast Asian countries (Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam) during the COVID-19 pandemic in 2021. We also investigated the associated risk and protective factors. Methods: Frontline HCWs (N = 1381) from the participating countries participated between 4 January and 14 June 2021. The participants completed self-reported surveys on anxiety (GAD-7), depression (PHQ-8), and job burnout (PWLS). Multivariate logistic regressions were performed with anxiety, depression, and job burnout as outcomes and sociodemographic and job characteristics and HCW perceptions as predictors. Results: The average proportion of HCWs reporting moderate anxiety, moderately severe depression, and job burnout across all countries were 10%, 4%, and 20%, respectively. Working longer hours than usual (Odds ratio [OR] = 1.82;3.51), perceived high job risk (1.98;2.22), and inadequate personal protective equipment (1.89;2.11) were associated with increased odds of anxiety and job burnout while working night shifts was associated with increased risk of depression (3.23). Perceived good teamwork was associated with lower odds of anxiety (0.46), depression (0.43), and job burnout (0.39). Conclusion: Job burnout remains a foremost issue among HCWs. Potential opportunities to improve HCW wellness are discussed.

Variation in community and ambulance care processes for out-of-hospital cardiac arrest during the COVID-19 pandemic: a systematic review and meta-analysis.

Bystander cardiopulmonary resuscitation (BCPR), early defibrillation and timely treatment by emergency medical services (EMS) can double the chance of survival from out-of-hospital sudden cardiac arrest (OHCA). We investigated the effect of the COVID-19 pandemic on the pre-hospital chain of survival. We searched five bibliographical databases for articles that compared prehospital OHCA care processes during and before the COVID-19 pandemic. Random effects meta-analyses were conducted, and meta-regression with mixed-effect models and subgroup analyses were conducted where appropriate. The search yielded 966 articles; 20 articles were included in our analysis. OHCA at home was more common during the pandemic (OR 1.38, 95% CI 1.11-1.71, p = 0.0069). BCPR did not differ during and before the COVID-19 pandemic (OR 0.94, 95% CI 0.80-1.11, p = 0.4631), although bystander defibrillation was significantly lower during the COVID-19 pandemic (OR 0.65, 95% CI 0.48-0.88, p = 0.0107). EMS call-to-arrival time was significantly higher during the COVID-19 pandemic (SMD 0.27, 95% CI 0.13-0.40, p = 0.0006). Resuscitation duration did not differ significantly between pandemic and pre-pandemic timeframes. The COVID-19 pandemic significantly affected prehospital processes for OHCA. These findings may inform future interventions, particularly to consider interventions to increase BCPR and improve the pre-hospital chain of survival.

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.

Impact of the COVID-19 pandemic on the epidemiology of out-of-hospital cardiac arrest: a systematic review and meta-analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has significantly influenced epidemiology, yet its impact on out-of-hospital cardiac arrest (OHCA) remains unclear. We aimed to evaluate the impact of the pandemic on the incidence and case fatality rate (CFR) of OHCA. We also evaluated the impact on intermediate outcomes and clinical characteristics. METHODS: PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases were searched from inception to May 3, 2021. Studies were included if they compared OHCA processes and outcomes between the pandemic and historical control time periods. Meta-analyses were performed for primary outcomes [annual incidence, mortality, and case fatality rate (CFR)], secondary outcomes [field termination of resuscitation (TOR), return of spontaneous circulation (ROSC), survival to hospital admission, and survival to hospital discharge], and clinical characteristics (shockable rhythm and etiologies). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42021253879). RESULTS: The COVID-19 pandemic was associated with a 39.5% increase in pooled annual OHCA incidence (p < 0.001). Pooled CFR was increased by 2.65% (p < 0.001), with a pooled odds ratio (OR) of 1.95 for mortality [95% confidence interval (95%CI) 1.51-2.51]. There was increased field TOR (OR = 2.46, 95%CI 1.62-3.74). There were decreased ROSC (OR = 0.65, 95%CI 0.55-0.77), survival to hospital admission (OR = 0.65, 95%CI 0.48-0.89), and survival to discharge (OR = 0.52, 95%CI 0.40-0.69). There was decreased shockable rhythm (OR = 0.73, 95%CI 0.60-0.88) and increased asphyxial etiology of OHCA (OR = 1.17, 95%CI 1.02-1.33). CONCLUSION: Compared to the pre-pandemic period, the COVID-19 pandemic period was significantly associated with increased OHCA incidence and worse outcomes.

A Descriptive Analysis of the Impact of COVID-19 on Emergency Department Attendance and Visit Characteristics in Singapore

Background: The coronavirus disease 2019 (COVID-19) has impacted the utilisation of Emergency Department (ED) services worldwide. This study aims to describe the changes in attendance at a single ED and corresponding patient visit characteristics before and during the COVID-19 period. Methods: In a single-centre retrospective cohort study, we used descriptive statistics to compare ED attendance, patient demographics and visit characteristics during the COVID-19 period (1 January–28 June 2020) and its corresponding historical period in 2019 (2 January–30 June 2019). Results: The mean ED attendance decreased from 342 visits/day in the pre-COVID-19 period to 297 visits/day in the COVID-19 period. This was accompanied by a decline in presentations in nearly every ICD-10-CM diagnosis category except for respiratory-related diseases. Notably, we observed reductions in visits by critically ill patients and severe disease presentations during the COVID-19 period. We also noted a shift in the ED patient case-mix from 'Non-fever’cases to 'Fever’cases, likely giving rise to two distinct trough-to-peak visit patterns during the pre-Circuit Breaker and Circuit Breaker period. Conclusions: This descriptive study revealed distinct ED visit trends across different time periods. The COVID-19 pandemic caused a reduction in ED attendances amongst patients with low-acuity conditions and those with highest priority for emergency care. This raises concern about treatment-seeking delays and the possible impact on health outcomes. The downward trend in low-acuity presentations also presents learning opportunities for ED crowd management planning in a post-COVID-19 era.

Healthcare worker stress, anxiety and burnout during the COVID-19 pandemic in Singapore: A 6-month multi-centre prospective study.

AIM: The long-term stress, anxiety and job burnout experienced by healthcare workers (HCWs) are important to consider as the novel coronavirus disease (COVID-19) pandemic stresses healthcare systems globally. The primary objective was to examine the changes in the proportion of HCWs reporting stress, anxiety, and job burnout over six months during the peak of the pandemic in Singapore. The secondary objective was to examine the extent that objective job characteristics, HCW-perceived job factors, and HCW personal resources were associated with stress, anxiety, and job burnout. METHOD: A sample of HCWs (doctors, nurses, allied health professionals, administrative and operations staff; N = 2744) was recruited via invitation to participate in an online survey from four tertiary hospitals. Data were gathered between March-August 2020, which included a 2-month lockdown period. HCWs completed monthly web-based self-reported assessments of stress (Perceived Stress Scale-4), anxiety (Generalized Anxiety Disorder-7), and job burnout (Physician Work Life Scale). RESULTS: The majority of the sample consisted of female HCWs (81%) and nurses (60%). Using random-intercept logistic regression models, elevated perceived stress, anxiety and job burnout were reported by 33%, 13%, and 24% of the overall sample at baseline respectively. The proportion of HCWs reporting stress and job burnout increased by approximately 1·0% and 1·2% respectively per month. Anxiety did not significantly increase. Working long hours was associated with higher odds, while teamwork and feeling appreciated at work were associated with lower odds, of stress, anxiety, and job burnout. CONCLUSIONS: Perceived stress and job burnout showed a mild increase over six months, even after exiting the lockdown. Teamwork and feeling appreciated at work were protective and are targets for developing organizational interventions to mitigate expected poor outcomes among frontline HCWs.

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.

Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Impact of COVID-19 on Out-of-Hospital Cardiac Arrest in Singapore.

This study aimed to evaluate the impact of the Coronavirus Disease 2019 (COVID-19) pandemic on out-of-hospital cardiac arrest (OHCA) in Singapore. We used data from the Singapore Civil Defence Force to compare the incidence, characteristics and outcomes of all Emergency Medical Services (EMS)-attended adult OHCA during the pandemic (January-May 2020) and pre-pandemic (January-May 2018 and 2019) periods. Pre-hospital return of spontaneous circulation (ROSC) was the primary outcome. Binary logistic regression was used to calculate the adjusted odds ratios (aOR) for the characteristics of OHCA. Of the 3893 OHCA patients (median age 72 years, 63.7% males), 1400 occurred during the pandemic period and 2493 during the pre-pandemic period. Compared with the pre-pandemic period, OHCAs during the pandemic period more likely occurred at home (aOR: 1.48; 95% CI: 1.24-1.75) and were witnessed (aOR: 1.71; 95% CI: 1.49-1.97). They received less bystander CPR (aOR: 0.70; 95% CI: 0.61-0.81) despite 65% of witnessed arrests by a family member, and waited longer for EMS (OR ≥ 10 min: 1.71, 95% CI 1.46-2.00). Pre-hospital ROSC was less likely during the pandemic period (aOR: 0.67; 95% CI: 0.53-0.84). The pandemic saw increased OHCA incidence and worse outcomes in Singapore, likely indirect effects of COVID-19.

The effectiveness of public health interventions against COVID-19: Lessons from the Singapore experience.

BACKGROUND: In dealing with community spread of COVID-19, two active interventions have been attempted or advocated-containment, and mitigation. Given the extensive impact of COVID-19 globally, there is international interest to learn from best practices that have been shown to work in controlling community spread to inform future outbreaks. This study explores the trajectory of COVID-19 infection in Singapore had the government intervention not focused on containment, but rather on mitigation. In addition, we estimate the actual COVID-19 infection cases in Singapore, given that confirmed cases are publicly available. METHODS AND FINDINGS: We developed a COVID-19 infection model, which is a modified SIR model that differentiate between detected (diagnosed) and undetected (undiagnosed) individuals and segments total population into seven health states: susceptible (S), infected asymptomatic undiagnosed (A), infected asymptomatic diagnosed (I), infected symptomatic undiagnosed (U), infected symptomatic diagnosed (E), recovered (R), and dead (D). To account for the infection stages of the asymptomatic and symptomatic infected individuals, the asymptomatic infected individuals were further disaggregated into three infection stages: (a) latent (b) infectious and (c) non-infectious; while the symptomatic infected were disaggregated into two stages: (a) infectious and (b) non-infectious. The simulation result shows that by the end of the current epidemic cycle without considering the possibility of a second wave, under the containment intervention implemented in Singapore, the confirmed number of Singaporeans infected with COVID-19 (diagnosed asymptomatic and symptomatic cases) is projected to be 52,053 (with 95% confidence range of 49,370-54,735) representing 0.87% (0.83%-0.92%) of the total population; while the actual number of Singaporeans infected with COVID-19 (diagnosed and undiagnosed asymptomatic and symptomatic infected cases) is projected to be 86,041 (81,097-90,986), which is 1.65 times the confirmed cases and represents 1.45% (1.36%-1.53%) of the total population. A peak in infected cases is projected to have occurred on around day 125 (27/05/2020) for the confirmed infected cases and around day 115 (17/05/2020) for the actual infected cases. The number of deaths is estimated to be 37 (34-39) among those infected with COVID-19 by the end of the epidemic cycle; consequently, the perceived case fatality rate is projected to be 0.07%, while the actual case fatality rate is estimated to be 0.043%. Importantly, our simulation model results suggest that there about 65% more COVID-19 infection cases in Singapore that have not been captured in the official reported numbers which could be uncovered via a serological study. Compared to the containment intervention, a mitigation intervention would have resulted in early peak infection, and increase both the cumulative confirmed and actual infection cases and deaths. CONCLUSION: Early public health measures in the context of targeted, aggressive containment including swift and effective contact tracing and quarantine, was likely responsible for suppressing the number of COVID-19 infections in Singapore.

Cardiopulmonary resuscitation (CPR) training strategies in the times of COVID-19: a systematic literature review comparing different training methodologies.

BACKGROUND: Traditional, instructor led, in-person training of CPR skills has become more challenging due to COVID-19 pandemic. We compared the learning outcomes of standard in-person CPR training (ST) with alternative methods of training such as hybrid or online-only training (AT) on CPR performance, quality, and knowledge among laypersons with no previous CPR training. METHODS: We searched PubMed and Google Scholar for relevant articles from January 1995 to May 2020. Covidence was used to review articles by two independent researchers. Effective Public Health Practice Project (EPHPP) Quality Assessment Tool was used to assess quality of the manuscripts. RESULTS: Of the 978 articles screened, twenty met the final inclusion criteria. All included studies had an experimental design and moderate to strong global quality rating. The trainees in ST group performed better on calling 911, time to initiate chest compressions, hand placement and chest compression depth. Trainees in AT group performed better in assessing scene safety, calling for help, response time including initiating first rescue breathing, adequate ventilation volume, compression rates, shorter hands-off time, confidence, willingness to perform CPR, ability to follow CPR algorithm, and equivalent or better knowledge retention than standard teaching methodology. CONCLUSION: AT methods of CPR training provide an effective alternative to the standard in-person CPR for large scale public training.

A conceptual framework for Emergency department design in a pandemic.

BACKGROUND: The current COVID-19 pandemic is highlighting gaps around the world in the design and workflow of Emergency Departments (ED). These gaps have an impact on both patient care and staff safety and represent a risk to public health. There is a need for a conceptual framework to guide ED design and workflow to address these challenges. Such a framework is important as the ED environment will always remain vulnerable to infectious diseases outbreaks in the future. AIMS: This paper aims to address issues and principles around ED design and workflow amidst the COVID-19 pandemic. We propose a conceptual framework and checklist for EDs to be prepared for future outbreaks as well. METHODS: A scoping literature review was conducted, of the experiences of EDs in managing outbreaks such as SARS, H1N1 and COVID-19. The combined experiences of the authors and the experiences from the literature were grouped under common themes to develop the conceptual framework. RESULTS: Four key principles were derived- (1) situational awareness, surveillance and perimeter defence, (2) ED staff protection, (3) surge capacity management and (4) ED recovery. The findings were integrated in a proposed conceptual framework to guide ED design in response to an infectious disease outbreak. There are various elements which need to be considered at ED input, throughput and output. These elements can be categorised into (1) system (workflow, protocols and communication), (2) staff (human resources), (3) space (infrastructure), and (4) supply (logistics) and are placed in a checklist for pragmatic use. CONCLUSION: The ED needs to be in a constant state of preparedness. A framework can be useful to guide ED design and workflow to achieve this. As all ED systems are different with varying capabilities, our framework may help EDs across the world prepare for infectious disease outbreaks.

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.