An efficient decision support system for selecting very light business jet using CRITIC-TOPSIS method

PurposeThis paper aims to assure the selection of the most suitable very light business aircraft which is preferred by the passengers based on effectiveness and aesthetic comfort. The proposed approach to determine the light business jet aircraft would provide long-range, less travel time, cozy seating arrangements, on-board lavatory facility, other aesthetic ambiance (audio systems, light systems and temperature-noise control) and appliances at reasonable flight cost.Design/methodology/approachThe selection of a light business jet is obtained through multi-criteria decision-making based on the speed limit ranges from 0.57 to 0.70 Mach number and the distance traveled up to 3,000 km with the best aesthetic comfort level. To validate the approach, case studies of five aircrafts such as Honda Jet HA 420, Cessna Citation jet M2, Embraer Phenom 100, Eclipse 550 and Cessna Citation Mustang are performed. To obtain the best suitable business jet, criteria importance through intercriteria correlation (CRITIC) and technique for order performance by similarity to ideal solution (TOPSIS) is used to determine the rankings of listed aircraft.FindingsThe study concludes that the Cessna Citation jet M2 is chosen as the best Very Light Jet (VLJ) on the basis of speed, range, weight, cost, aesthetic and comfort. Based on the sensitivity, mean absolute percentage error (MAPE) and symmetric mean absolute percentage error analysis (sMAPE), the most and least sensitive criteria for a business jet came out to be cost and speed, respectively.Originality/valueA real case study for several parameters of five different jets such as Honda Jet HA 420, Cessna Citation jet M2, Embraer Phenom 100, Eclipse 550 and Cessna Citation Mustang are shown in this paper. Based on the case study numerical values are assigned with speed, range, weight, cost, aesthetic and comfort which are applied with CRITIC and TOPSIS to obtain the most suitable business jet among the five mentioned jets which are rarely found in the literature.

COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study.

OBJECTIVES: COVID-19 pandemic has led to unprecedented increase in rates of stress and burn out among healthcare workers (HCWs). Heart rate variability (HRV) has been shown to be reflective of stress and burnout. The present study evaluated the prevalence of burnout and attempted to develop a HRV based predictive machine learning (ML) model to detect burnout among HCWs during COVID-19 pandemic. METHODS: Mini-Z 1.0 survey was collected from 1615 HCWs, of whom 664, 512 and 439 were frontline, second-line and non-COVID HCWs respectively. Burnout was defined as score ≥3 on Mini-Z-burnout-item. A 12-lead digitized ECG recording was performed and ECG features of HRV were obtained using feature extraction. A ML model comprising demographic and HRV features was developed to detect burnout. RESULTS: Burnout rates were higher among second-line workers 20.5% than frontline 14.9% and non-COVID 13.2% workers. In multivariable analyses, features associated with higher likelihood of burnout were feeling stressed (OR = 6.02), feeling dissatisfied with current job (OR = 5.15), working in a chaotic, hectic environment (OR = 2.09) and feeling that COVID has significantly impacted the mental wellbeing (OR = 6.02). HCWs with burnout had a significantly lower HRV parameters like root mean square of successive RR intervals differences (RMSSD) [p < 0.0001] and standard deviation of the time interval between successive RR intervals (SDNN) [p < 0.001]) as compared to normal subjects. Extra tree classifier was the best performing ML model (sensitivity: 84%) CONCLUSION: In this study of HCWs from India, burnout prevalence was lower than reports from developed nations, and was higher among second-line versus frontline workers. Incorporation of HRV based ML model predicted burnout among HCWs with a good accuracy.

Health System Preparedness for COVID-19 and Its Impacts on Frontline Health-Care Workers in Nepal: A Qualitative Study Among Frontline Health-Care Workers and Policy-Makers.

BACKGROUND: Rapidly growing coronavirus disease 2019 (COVID-19) pandemic has brought unprecedented challenges to the health system in Nepal. The main objective of this study was to explore the health system preparedness for COVID-19 and its impacts on frontline health-care workers in Nepal. METHODS: Semi-structured interviews were conducted among 32 health-care workers who were involved in clinical care of COVID-19 patients and four policy-makers who were responsible for COVID-19 control and management at central and provincial level. Interviews were conducted through telephone or Internet-based tools such as Zoom and Skype. All interviews were audio-recorded, transcribed into English, and coded using inductive and deductive approaches. RESULTS: Both health-care workers and policy-makers reported failure to initiate pre-emptive control measures at the early stages of the outbreak as the pivot in pandemic control. Although several measures were rolled out when cases started to appear, the overall health system preparedness was low. The poor governance, and coordination between three tiers of government was compounded by the inadequate personal protective equipment for health-care workers, insufficient isolation beds for patients, and poor engagement of the private sector. Frontline health-care workers experienced various degrees of stigma because of their profession and yet were able to maintain their motivation to continue serving patients. CONCLUSION: Preparedness for COVID-19 was affected by the poor coordination between three tiers of governance. Specifically, the lack of human resources, inadequate logistic chain management and laboratory facilities for testing COVID-19 appeared to have jeopardized the health system preparedness and escalated the pandemic in Nepal. Despite the poor preparedness, and health and safety concerns, health-care workers maintained their motivation. There is an urgent need for an effective coordination mechanism between various tiers of health structure (including private sector) in addition to incentivizing the health-care workers for the current and future pandemics.

Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study.

BACKGROUND: There is no large contemporary data from India to see the prevalence of burnout in HCWs in covid era. Burnout and mental stress is associated with electrocardiographic changes detectable by artificial intelligence (AI). OBJECTIVE: The present study aims to estimate the prevalence of burnout in HCWs in COVID-19 era using Mini Z-scale and to develop predictive AI model to detect burnout in HCWs in COVID-19 era. METHODS: This is an observational and cross-sectional study to evaluate the presence of burnout in HCWs in academic tertiary care centres of North India in the COVID-19 era. At least 900 participants will be enrolled in this study from four leading premier government-funded/public-private centres of North India. Each study centre will be asked to recruit HCWs by approaching them through various listed ways for participation in the study. Interested participants after initial screening and meeting the eligibility criteria, will be asked to fill the questionnaire (having demographic and work related with Mini Z questionnaire) to assess burnout. The healthcare workers will include physicians at all levels of training, nursing staff and paramedical staff who are involved directly or indirectly in COVID-19 care. The analysis of the raw electrocardiogram (ECG) data and development of algorithm using convolutional neural networks (CNN) will be done by experts. CONCLUSIONS: In Summary, we propose that ECG data generated from the people with burnout can be utilized to develop AI-enabled model to predict the presence of stress and burnout in HCWs in COVID-19 era.

Pattern of acute MI admissions in India during COVID-19 era: A Cardiological Society of India study - Rationale and design.

BACKGROUND: COVID-19 pandemic has affected around 20million patients worldwide and 2.0 million cases from India. The lockdown was employed to delay the pandemic. However, it had an unintentional impact on acute cardiovascular care, especially acute myocardial infarction (AMI). Observational studies have shown a decrease in hospital admissions for AMI in several developed countries during the pandemic period. We aimed to evaluate the impact of COVID-19 on the AMI admissions patterns across India. METHODS: In this multicentric, retrospective, cross-sectional study, we included all AMI cases admitted to participating hospitals during the study period 15th March to 15th June 2020 and compared them using a historical control of all cases of AMI admitted during the corresponding period in the year 2019. Major objective of the study is to analyze the changes inthe number of hospital admissions for AMI in hospitals across India. In addition, we intend to evaluate the impact of COVID-19 on the weekly AMI admission rates, and other performance measures like rates of thrombolysis/primary percutaneous interventions (PCI), window period, door to balloon time, and door to needle time. Other objectives include evaluation of changes in the major complications and mortality rates of AMI and its predictors during COVID-19 pandemic. CONCLUSIONS: This CSI-AMI study will provide scientific evidence about the impact of COVID-19 on AMI care in India. Based on this study, we may be able to suggest appropriate changes to the existing MI guidelines and to educate the public regarding emergency care for AMI during COVID-19 pandemic.

Will the hidden specter of acute coronary syndrome (ACS) and ST-segment elevation myocardial infarction (STEMI) emerge from the avalanche of COVID-19?

There has been a huge impact of the COVID-19 pandemic on global healthcare systems. Advisories across the world have appealed to people to stay at home and observe social distancing to slow down the pandemic. However it is important to realize as to how this is affecting acute cardiovascular care. Recent studies from Europe and USA have reported > 50% reduction in hospital admissions for ACS and declining rates of coronary interventions. The possible reasons for this noticeable reduction in patients with ACS/STEMI during the COVID-19 pandemic are multi-factorial. On one hand, it is due to change in thresholds for referring patients of ACS/STEMI for cardiac catheterization, with fibrinolysis being acceptable for many stable STEMI patients and conservative management being preferred for NSTEMI patients. Theories abound on how "staying at home" strategy may have led to an reduction in acute coronary events due to healthier lifestyle, better compliance and reduced stress. Realistically however, a more disquieting reason would be a "pseudo-reduction" ie. the incidence of ACS/STEMI is actually the same, but these patients are staying away from hospitals due to fear of contracting the infection. Lockdown restrictions have also limited transport options for patients seeking to reach hospitals in time. Healthcare systems need to be prepared for an anticipated downstream deluge of such untreated patients who may present with sequelae like heart failure, reinfarction, arrhythmias, mechanical complications etc. Scientific societies should have proactive campaigns to alleviate patient concerns, and encourage them to seek timely medical attention despite the COVID-19 pandemic.

Cardiological society of India document on safety measure during echo evaluation of cardiovascular disease in the time of COVID-19.

An echocardiographic investigation is one of the key modalities of diagnosis in cardiology. There has been a rising presence of cardiological comorbidities in patients positive for COVID-19. Hence, it is becoming extremely essential to look into the correct safety precautions, healthcare professionals must take while conducting an echo investigation. The decision matrix formulated for conducting an echocardiographic evaluation is based on presence or absence of cardiological comorbidity vis-à-vis positive, suspected or negative for COVID-19. The safety measures have been constructed keeping in mind the current safety precautions by WHO, CDC and MoHFW, India.

Managing ACS during COVID-19 infection: Do not follow the traditional route.

COVID-19 pandemic is creating havoc in the world. It is also spreading in India creating a massive healthcare problem. Few major hospitals were closed down because of the spread among healthcare personnel. Management of several commonly occurring diseases needed modifications to a lesser or greater extent because of this pandemic. Management of acute coronary syndrome (ACS) also requires certain modifications. In this opinion paper an attempt has been made to give an outline of ACS management in this changed scenario.

Digital healthcare: The only solution for better healthcare during COVID-19 pandemic?

The huge impact of the COVID-19 pandemic on global healthcare systems has prompted search for novel tools to stem the tide. Attention has turned to the digital health community to provide possible health solutions in this time of unprecedented medical crisis to mitigate the impact of this pandemic. The paper shall focus on how digital solutions can impact healthcare during this pandemic.