Burnout, anxiety and depression in healthcare workers during the early COVID-19 period in Singapore

acceptedVersionPeer reviewe

Discretionary expensing of stock options in U.S Fortune 500 companies

This paper investigates the attributes associated with US Fortune 500 firms which adopt SFAS No. 123 before the mandatory adoption date. Additionally, this paper also examines if there are significant differences in firm characteristics among three groups of firms, namely the early adopters, late adopters, and non-adopters of the standard

TQM implementation in service industries - a case study of an educational institution.

In the course of our study, we have tried to trace the emerging trends of TQM in service industries. We have also formulated a conceptual framework that we hope will serve as a guide in the pursuit of TQM within education institutions. For the purpose of our study, we have also commented on the implementation model of Temasek Polytechnic with reference to our proposed conceptual model, which has included the core elements of TQM–customer focus, continuous improvement and total participation

Atypical Presentation of Traumatic Aortic Injury

Background. Blunt thoracic aorta injury (BAI) is second only to head injury as cause of mortality in blunt trauma. While most patients do not survive till arrival at the hospital, for the remainder, prompt diagnosis and treatment greatly improve outcomes. We report an atypical presentation of BAI, highlighting the diagnostic challenges of this condition in the emergency department. Case Presentation. A previously well 25-year-old male presented 15 hours after injury hemodynamically stable with delirium. There were no signs or symptoms suggestive of BAI. Sonography showed small bilateral pleural effusions. Chest radiograph showed a normal mediastinum. Eventually, CT demonstrated a contained distal aortic arch disruption. The patient underwent percutaneous endovascular thoracic aortic repair and recovered well. Conclusion. This catastrophic lesion may present with few reliable signs and symptoms; hence, a high index of suspicion is crucial for early diagnosis and definitive surgical management. This paper discusses the diagnostic utility of clinical features, injury mechanism, and radiographic modalities. Consideration of mechanism of injury, clinical features, and chest radiograph findings should prompt advanced chest imaging

BRAVE: a point of care adaptive leadership approach to providing patient-centric care in the emergency department

The practice of emergency medicine has reached its cross roads. Emergency physicians (EPs) are managing many more time-dependent conditions, initiating complex treatments in the emergency department (ED), handling ethical and end of life care discussions upfront, and even performing procedures which used to be done only in critical care settings, in the resuscitation room. EPs manage a wide spectrum of patients, 24 h a day, which reflects the community and society they practice in. Besides the medical and "technical" issues to handle, they have to learn how to resolve confounding elements which their patients can present with. These may include social, financial, cultural, ethical, relationship, and even employment matters. EPs cannot overlook these, in order to provide holistic care. More and more emphasis is also now given to the social determinants of health. We, from the emergency medicine fraternity, are proposing a unique "BRAVE model," as a mnemonic to assist in the provision of point of care, adaptive leadership at the bedside in the ED. This represents another useful tool for use in the current climate of the ED, where patients have higher expectations, need more patient-centric resolution and handling of their issues, looming against the background of a more complex society and world.Published versio

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

BackgroundThere is a growing demand globally for emergency department (ED) services. An increase in ED visits has resulted in overcrowding and longer waiting times. The triage process plays a crucial role in assessing and stratifying patients’ risks and ensuring that the critically ill promptly receive appropriate priority and emergency treatment. A substantial amount of research has been conducted on the use of machine learning tools to construct triage and risk prediction models; however, the black box nature of these models has limited their clinical application and interpretation. ObjectiveIn this study, we plan to develop an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED by leveraging large-scale electronic health records (EHRs) and machine learning. MethodsTo achieve this objective, we will conduct a retrospective, single-center study based on a large, longitudinal data set obtained from the EHRs of the largest tertiary hospital in Singapore. Study outcomes include adverse events experienced by patients, such as the need for an intensive care unit and inpatient death. With preidentified candidate variables drawn from expert opinions and relevant literature, we will apply an interpretable machine learning–based AutoScore to develop 3 SERT scores. These 3 scores can be used at different times in the ED, that is, on arrival, during ED stay, and at admission. Furthermore, we will compare our novel SERT scores with established clinical scores and previously described black box machine learning models as baselines. Receiver operating characteristic analysis will be conducted on the testing cohorts for performance evaluation. ResultsThe study is currently being conducted. The extracted data indicate approximately 1.8 million ED visits by over 810,000 unique patients. Modelling results are expected to be published in 2022. ConclusionsThe SERT scoring system proposed in this study will be unique and innovative because of its dynamic nature and modelling transparency. If successfully validated, our proposed solution will establish a standard for data processing and modelling by taking advantage of large-scale EHRs and interpretable machine learning tools. International Registered Report Identifier (IRRID)DERR1-10.2196/3420

Review of the Clinical Evidence and Controversies in Therapeutic Hypothermia for Survivors of Sudden Cardiac Death

Sudden cardiac arrest constitutes a major public health burden in both developed and developing countries. In those successfully resuscitated from cardiac arrest, subsequent mortality is still high (∼75%) and is due to a combination of ischaemia and reperfusion injury. The purpose of this review is to describe the experimental and clinical evidence supporting therapeutic hypothermia in survivors of sudden cardiac arrest. We also discuss controversies and unresolved issues in therapeutic hypothermia, including the optimum target temperature for therapeutic hypothermia, and the role of pre-hospital induction of hypothermia. We conclude with a perspective on therapeutic hypothermia as it applies to the Singapore context

Ambient Air Quality and Emergency Hospital Admissions in Singapore: A Time-Series Analysis

Air pollution exposure may increase the demand for emergency healthcare services, particularly in South-East Asia, where the burden of air-pollution-related health impacts is high. This article aims to investigate the association between air quality and emergency hospital admissions in Singapore. Quasi-Poisson regression was applied with a distributed lag non-linear model (DLNM) to assess the short-term associations between air quality variations and all-cause, emergency admissions from a major hospital in Singapore, between 2009 and 2017. Higher concentrations of SO2, PM2.5, PM10, NO2, and CO were positively associated with an increased risk of (i) all-cause, (ii) cardiovascular-related, and (iii) respiratory-related emergency admissions over 7 days. O3 concentration increases were associated with a non-linear decrease in emergency admissions. Females experienced a higher risk of emergency admissions associated with PM2.5, PM10, and CO exposure, and a lower risk of admissions with NO2 exposure, compared to males. The older adults (≥65 years) experienced a higher risk of emergency admissions associated with SO2 and O3 exposure compared to the non-elderly group. We found significant positive associations between respiratory disease- and cardiovascular disease-related emergency hospital admissions and ambient SO2, PM2.5, PM10, NO2, and CO concentrations. Age and gender were identified as effect modifiers of all-cause admissions

Clinicians are Outperformed by a Deep Learning System When Identifying Optic Disc Lesions

Detection of optic disc abnormalities using ophthalmoscopy is a difficult task for non-ophthalmologists. We recently developed a deep learning system (BONSAI-DLS) to accurately classify optic discs on digital fundus photographs. The aim of this study was to compare the performance of the BONSAI-DLS to that of clinicians with or without ophthalmic training for the classification of optic discs