Influence of Covid-19 on Burnout among Critical Care Advanced Practice Providers

INTRODUCTION: Burnout results from physical and mental exhaustion and affects the intentionality to leave the profession. Unfortunately, burnout scenarios have been quite common among Critical Care Advance Practice Providers (APPs). The calls to action to sustain the critical care workforce were in full force before COVID-19 attacked the world and rapidly turned into a pandemic. Already overburdened and depleted critical care workforce was further exacerbated by overwhelming work demands. Our research was aimed at understanding the influence of COVID-19 on the critical care APPs. METHOD(S): We utilized a cross-sectional REDCap survey to understand the self-perceived extent to which the burnout among critical APPs had changed. A single link to survey was emailed to 2775 SCCM physician assistant and nursing section members. There were 578 responses (response rate 20.8%). After excluding 60 non-APPs responses, our sample size was 518. Survey questions asked about the symptoms of emotional exhaustion, depersonalization, perception of reduced personal accomplishments and intention to leave. RESULT(S): We found an increase in burnout and related dimensions. Emotional exhaustion increased from 28.8%- 69.9%, depersonalization increased from 17.2%-37.6%%, and perception of reduced personal accomplishments increased from 18.1%-38% among our study population. We also asked about the intentions to leave critical care and a total of 513 participants engaged with this question. Twenty-two percent (n=114) of our sample expressed an intention to leave critical care. We also asked about the intention to leave the profession altogether and only 21.8% (n=113) participants responded. A total of 43.3% (n=49) respondents reported the intention to leave the profession altogether within the following 2-5 years. Of those who responded, 22.1% (n=25) intended to leave within 1 year and 25.7% (n=29) intended to leave between 5-10 years while 8.9% (n=10) stated that they intended to stay beyond 10 years. CONCLUSION(S): COVID-19 pandemic exacerbated the burnout and its dimensions among critical care APPs. It is crucial to attend to the trends in burnout and intention to leave and mitigation strategies must be employed to sustain the critical care workforce which is key to planning the future of critical care in the US and globally.

Predictive Analysis of the Recovery Rate from Coronavirus (COVID-19)

Estimation of recovery rate of COVID-19 positive persons is significant to measure the severity of the disease for mankind. In this work, prediction of the recovery rate is estimated based on machine learning technology. Standard data set of Kaggle has been used for experimental purpose, and the data sets of COVID cases in Italy, China and India for these countries are considered. Based on that data set and the present scenario, the proposed technique predicts the recovery rate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Real-Time Approach with Deep Learning for Pandemic Management

It has never been so critical to managing pandemic situations created by a virus like COVID-19, which has brought the world almost to a standstill, claiming millions of lives. Learning from all earlier viruses and building a quick tackling mechanism is a need of the hour. There is a greater need for technology to collaborate with healthcare and leverage each of the domains’ expertise. With less time in hand, this collaboration must happen in a short time. There is a need to study the exiting progression in technology and the healthcare landscape to bring them to a common path for practical solutions. In the chapter, an attempt was made to put together some thoughts in both fields to relate them to pandemic management’s frequent subject. Caution is drawn towards some crucial aspects, such as security and transparency, that cannot be compromised in this journey. Artificial intelligence (AI), being at the forefront of the technology supporting lives, provides a greater hope in this direction. Some of the prominent approaches can be looked at from a pandemic management point of view, which can start a more in-depth discussion on AI and healthcare going hand in hand in managing this pandemic situation. Essential areas of pandemic management, such as building on the knowledge gathered over a period, plugging in the real-time data from the society, building efficient data management systems and building transparent and interpretable solutions are the focus areas of exploration in this chapter. © 2022, Springer Nature Switzerland AG.