ABSTRACT
After the outbreak of corona virus, all counties are paying special attention to their healthcare infrastructure. During second phase of covid-19, entire world has seen health care crisis. Large number of people died globally. Entire world was affected mentally or physically. There is a great need to strengthen the healthcare infrastructure, to vaccinate the population against covid virus infection and to take proper precaution to avoid spread of the virus, so that the world will not see such deadly days again. This paper discusses how technologies like Internet of Things (IoT), Artificial Intelligence (AI), Drones etc can help in remote monitoring of patients, judicious hospital admission, conscious distribution of lifesaving drugs etc. Investment in technology with not only help in the reduction of spread of the virus but will also help in fighting with all other future pandemics. All the countries must have to invest more on latest technologies in their healthcare to make themselves ready for such future pandemics. When the things will improve, the new normal will be very much different from the life that was before pandemic. IoT, AI and other technologies will become the non-separatable part of our life. © 2023 Bharati Vidyapeeth, New Delhi.
ABSTRACT
This paper examines the impact of the pandemic on India's public health system of the country, especially from the perspective of urban slumdwellers. Drawing on a qualitative study carried out by the Urban Health Resource Centre in selected slums in Indore and Agra, the paper reflects the impact of the pandemic on the provision of essential health services such as maternal and child healthcare, family planning, immunization for children and the detection and treatment of non-COVID ailments such as tuberculosis. The authors argue that the veritable collapse of healthcare to the most vulnerable sections of the population exposed the structural weaknesses of India's healthcare system. To build a more robust public health system in India to tackle future crises of this kind, the authors call for strengthening the health infrastructure in small to medium-sized cities and reinforcing other crucial determinants of well-being such as food security, livelihood opportunities and support and enhanced education opportunities. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
ABSTRACT
Medical care is vital to having a decent existence. Be that as it may, it is undeniably challenging to get an appointment with a specialist for each medical issue and due to the current global pandemic in the form of Coronavirus, the healthcare industry is under immense pressure to meet the ends of patients' needs. Doctors and nurses are working relentlessly to treat and help the patients in the best possible way and still, they face problems in terms of time management, technical resources, healthcare infrastructure, support staff as well as healthcare personnel. To resolve this problem, we have made a chatbot utilizing Artificial Intelligence (AI) that can analyze the illness and give fundamental insights regarding the infection by looking at the data of a patient who was previously counselled at a health specialist This will also assist in lessening the medical services costs. The chatbot is a product application intended to recreate discussions with human clients through intuitive and customized content. It is in many cases portrayed as the most moving and promising articulations of communication among people and machines utilizing Artificial Intelligence and Natural Language Processing (NLP). The chatbot stores the information in the data set to recognize the sentence and pursue an inquiry choice and answer the corresponding inquiry. Through this paper, we aim to create a fully functional chatbot that will help the patients/users to know about the disease by simply entering the symptoms they possess. Additionally, they can also get information about certain medicine by simply typing the name of the medicine. Another additional feature is the ability of the bot to answer general questions regarding healthcare and wellbeing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Medical care is vital to having a decent existence. Be that as it may, it is undeniably challenging to get an appointment with a specialist for each medical issue and due to the current global pandemic in the form of Coronavirus, the healthcare industry is under immense pressure to meet the ends of patients' needs. Doctors and nurses are working relentlessly to treat and help the patients in the best possible way and still, they face problems in terms of time management, technical resources, healthcare infrastructure, support staff as well as healthcare personnel. To resolve this problem, we have made a chatbot utilizing Artificial Intelligence (AI) that can analyze the illness and give fundamental insights regarding the infection by looking at the data of a patient who was previously counselled at a health specialist This will also assist in lessening the medical services costs. The chatbot is a product application intended to recreate discussions with human clients through intuitive and customized content. It is in many cases portrayed as the most moving and promising articulations of communication among people and machines utilizing Artificial Intelligence and Natural Language Processing (NLP). The chatbot stores the information in the data set to recognize the sentence and pursue an inquiry choice and answer the corresponding inquiry. Through this paper, we aim to create a fully functional chatbot that will help the patients/users to know about the disease by simply entering the symptoms they possess. Additionally, they can also get information about certain medicine by simply typing the name of the medicine. Another additional feature is the ability of the bot to answer general questions regarding healthcare and wellbeing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Health and socioeconomic developments are so closely intertwined and it is impossible to achieve one without the other. Persistent and growing socioeconomic gaps result in major discrepancies in the quality of people's health. It calls us to realize that health is an investment in the future. Kerala, one of the southern states in India, is branded as a model and it is unique that the standards achieved are comparable with that of the developed countries. A successful primary healthcare infrastructure tested decades back is the myth behind the epic level of the healthcare system in Kerala. There is an urgent need for greater political will and increased funding for essential health services. This paper at first, explores the healthcare scenario in Kerala – expenditure and outcome over a period of 40 years to make an assessment about the commitment shown by government over the years to build a robust infrastructure. It then examines the situation of Covid-19 in Kerala focusing on the lacunae in the system - which showed strains when cases started increasing. Further, it highlights the importance of healthcare investment for improving healthcare infrastructure and proposes ways to improve the system. In the last part of the paper, we would be discussing the importance of Public-Private Partnership model in improving health infrastructure in the state. © 2023 DMICS.
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A massive number of patients infected with SARS-CoV2 and Delta variant of COVID-19 have generated acute respiratory distress syndrome (ARDS) which needs intensive care, which includes mechanical ventilation. But due to the huge no of patients, the workload and stress on healthcare infrastructure and related personnel have grown exponentially. This has resulted in huge demand for innovation in the field of automated health care which can help reduce the stress on the current healthcare infrastructure. This work gives a solution for the issue of pressure prediction in mechanical ventilation. The algorithm suggested by the researchers tries to predict the pressure in the respiratory circuit for various lung conditions. Prediction of pressure in the lungs is a type of sequence prediction problem. Long short-term memory (LSTM) is the most efficient solution to the sequence prediction problem. Due to its ability to selectively remember patterns over the long term, LSTM has an edge over normal RNN. RNN is good for short-term patterns but for sequence prediction problems, LSTM is preferred. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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More than two years since the first SARS-CoV-2 infection were reported, the COVID-19 pandemic remains an acute global emergency. The COVID-19 pandemic has deeply affected the lives of people across the world. Its health, economic, political, educational, and societal consequences have disproportionately affected the most vulnerable. Apart from being a global health concern, COVID-19 is having major consequences on the world economy. The pandemic has challenged local, national, regional and global capacities to prepare and respond. Health systems globally have employed three common approaches to rapidly scale up health system infrastructure, namely by constructing new treatment facilities, converting public venues and reconfiguring existing medical facilities to provide care for patients with COVID-19. Considerable efforts were being made behind the scenes to develop new strategies to ensure adequate public healthcare infrastructure and workplace capacities. Hospitals have repurposed and reallocated internal space and redeployed resources to manage COVID-19 patients. Countries discharged many patients from hospitals to their homes and postponed non-critical treatment and elective procedures. Almost all hospitals adopted a strategy of hospital approach to COVID-19 with the different primary and secondary goals. In this article we present a strategy of Mikaelyan University Hospital located in Yerevan, Armenia in managing patients with COVID-19. Preparing for patients' admission, developing of internal and external hospital communications, reconstruction, redistribution of human resources was carried out in parallel with trainings of health care workers, patients' education, etc. Mikaelyan University Hospital laboratory was reorganized to implement the new approaches and goals in managing of unprecedented number of patients and to secure quality control. The number of intensive care unit beds has been increased, also all possible efforts have been made to obtain all the required equipment and maintenance. Overall, the strategy can be considered successful as it was based on the multidisciplinary and multisectoral approach including academic sector, clinicians, leadership, patients, decision makers, nurses, radiologists, psychologists, intensivists, etc. Copyright © 2022, Yerevan State Medical University. All rights reserved.
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Motivated by the varying effectiveness of government intervention policies to contain the COVID-19 pandemic, and the potential positive relationship between ethnolinguistic diversity and social distance, this paper aims to provide empirical evidence on the relationship between ethnolinguistic diversity and the spread of COVID-19. In particular, using global data from 113 developed and developing countries during the early stages of the pandemic (from 31 December 2019 to 8 July 2020), we have found a significant negative effect of ethnolinguistic diversity on the spread of the virus. The result is robust to alternative measures of ethnolinguistic diversity and estimator that addresses endogeneity. Moreover, we also show that the impact of ethnolinguistic diversity on the spread of COVID-19 differs in economies characterized by different levels of democracy, policy stringency on addressing COVID-19 and health expenditure.
Subject(s)
COVID-19 , Communicable Diseases , Government , Humans , Pandemics , SARS-CoV-2ABSTRACT
As a result of the Covid-19 outbreak, a trustworthy health care system for remote surveillance was required, particularly in care facilitieas for the elderly. Many studies have been done in this subject, however they still have security, latency, extended time of execution and response delay. An intelligent Healthcare infrastructure termed Remote Health Monitoring (RHM) is introduced in this study to overcome these constraints. The framework uses high-level fog layer services including locally storage, native real-time data processing with combined mining of information in handling certain cloud and sensor network loads and transformed in a decision taker entity. This systems uses a body and camera sensors to diagnose, increasing accuracy and efficiency while protecting privacy. The suggested framework was tested using the iFogSim toolbox. It may minimise latency, energy usage, network connectivity and total reaction time. This work will assist develop a high performing, secure, and dependable intelligent Medical infrastructure. © 2022 IEEE.
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The COVID-19 pandemic took a toll on the world’s healthcare infrastructure as well as its social, economic, and psychological well-being. In particular, Italy’s unexpectedly high COVID-19 case and death rate from March to June, 2020, captured headlines due to its speed and virulence. Many governments are currently implementing measures to help contain and slow down the spread of COVID-19. The Social Response to Covid-19 Smart Dashboard was built by researchers at the Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age at San Diego State University and Politecnico di Milano. This dashboard provides an aggregated view of what people in 10 Italian metropolitan cities (Milan, Venice, Turin, Bologna, Florence, Rome, Naples, Bari, Palermo, and Cagliari) tweet during the pandemic by monitoring social media behaviors in the north, center, south, and islands. Moreover, the dashboard is a geo-targeted search tool for Twitter messages to monitor the diffusion of information and social behavior changes which provides an automatic procedure to help researchers to: associate tweets based on geography differences, filter noises such as removing redundant retweets and using machine learning methods to improve precisions, analyze social media data from a spatiotemporal perspective, and visualize social media data in various aspects such as weekly trends, top urls, top retweets, top mentions, and top hashtags. The Social Response to Covid-19 SMART Dashboard provides a useful tool for policy makers, city planners, research organizations, and health officials to monitor real-time societal perceptions using social media. © 2022, The Author(s).
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The COVID-19 pandemic has wreaked havoc on the worldwide economy. We employ semantic analysis to compare and assess the healthcare infrastructure of different Indian states with varying population and GDP levels. The goal is to (1) determine the relative lag in medical resources by state, (2) examine the states' responses to the COVID-19 economic crisis, and (3) recommend potential investments shortly based on the COVID-19 pandemic's findings. Our approach benefits from semantically analyzing tweets at the height of the most horrific second wave, which allows us to catch the tremors and quick shifts induced by wide-scale deaths. To approximate the infrastructure metrics, we leverage the social attitudes from Twitter data. The findings reveal that the lower expenditure on medical infrastructure is the primary challenge for the majority of the states in the country. Our research shows how data from state and city-specific Twitter posts may be utilized to comprehend local issues and opinions around healthcare leading to more directed and widely agreeable social media content-based rules. © 2022 IEEE.
ABSTRACT
With the strain on healthcare infrastructure and healthcare workers due to the ongoing COVID'19 pandemic, the need for novel ways to simplify the interaction between patients and physicians has increased. The aim is mainly to reduce face-to-face interactions and free more time and resources for those who urgently need it. This paper presents a Telemedicine system which can be used as an alternative method to a doctor's visit. The proposed system is considered as an interface that remotely connects the patient and the doctor. The system regularly measures and uploads readings from the patient to a database which the doctor can remotely review to decide on the state of the patient. The whole system is based on the internet of medical things (IoMT). In the proposed system, sensors collect and send data over the internet using the WIFI module connected to a node MCU controller. The developed prototype uses two sensors, one is used to measure both the percentage of oxygen in the blood and heart rate while the other sensor measures the temperature of the human body. The aim of this work is to help patients, by offering them this solution which can help them get the care they require from the comfort of their home and at the same time help doctors remotely give them the suitable treatment while reducing the need for unnecessary face-to-face interactions. © 2022 IEEE.
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With the fourth industrial revolution, there is a digitization wave going on for the transformation of existing systems into modern digital systems. This has opened the window for many opportunities, but at the same time, there is a multitude of cyber-security threats that need to be addressed. This paper considers one such threat posed by phishing and ransomware attacks to the healthcare infrastructures. Phishing has also been the most prevalent attack mechanism on the healthcare infrastructures during the ongoing COVID-19 pandemic. The paper proposes two intervention strategies as a step towards catering to the challenges posed by phishing and ransomware attacks in the context of healthcare infrastructures. © 2022, IFIP International Federation for Information Processing.
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The COVID-19 outbreak highlighted the smart healthcare infrastructure requirement to speed up vaccination and treatment. Present vaccination supply chain models are fragmented in nature, and they are suitable for a pandemic like COVID-19. Most of these vaccination supply chain models are cloud-centric and depend on humans. Due to this, the transparency in the supply chain and vaccination process is questionable. Moreover, we con't trace where the vaccination programs are facing issues in real-time. Furthermore, traditional supply chain models are vulnerable to a single point of failure and lack people-centric service capabilities. This paper has proposed a novel supply chain model for COVID-19 using robust technologies such as Blockchain and the Internet of Things. Besides, it automates the entire vaccination supplication chain, and it records management without compromising data integrity. We have evaluated our proposed model using Ethereum based decentralized application (DApp) to showcase its real-time capabilities. The DApp contains two divisions to deal with internal (intra) and worldwide (inter) use cases. From the system analysis, it is clear that it provides digital records integrity, availability, and system scalability by eliminating a single point of failure. Finally, the proposed system eliminates human interference in digital record management, which is prone to errors and alternation. © 2021 IEEE.All rights reserved.
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The fast expansion of the COVID-19 epidemic has revealed the shortcomings of current healthcare institutions in dealing with public emergency situations. One of the big reasons of Covid-19 spread is the lack of standard track and trace mechanisms in healthcare infrastructures. Furthermore, throughout the epidemic, the transmission of disinformation has accelerated, and existing platforms lacking capability of verifying the veracity of information, resulting to social unrest and illogical conduct. Therefore, building a track and trace system is critical to ensuring that data collected by the government and the public entities is accurate and dependable. It is obvious that implementing state-of-the-art predictive models like Artificial Neural Network and Blockchain-based traceable mechanisms can help to prevent the spreads of the new variants. In this paper, we proposed a Blockchain based traceable model to track and trace the infected cases so to help an effective planning to prevent the spread. © 2021 IEEE.
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Pakistan is currently facing two outbreaks, dengue and COVID-19; both have strained its healthcare system resulting in multiple concerns including the co-diagnosis of two. Due to poor healthcare capacity, low vaccination rate, increasing COVID-19 variants, socioeconomic disparities, and misinformation, it is inevitable that implications will prove to be damaging to both healthcare workers and civilians. Among these challenges, it is important to note the need for stronger epidemiological surveillance for both COVID-19 and dengue and the implementation of public health measures without endangering sources of livelihood. To sustain this, cooperation between WHO and Pakistan's government must continue through smart lockdowns, dengue awareness campaigns, and double laboratory procedures.
ABSTRACT
With the outbreak of COVID-19, the urgency of wide-scale healthcare infrastructure development has been felt globally for human survival. To accommodate a large infected population, copious wards are to be built within the prevalent constraints of land, power and material availability. This study designs a two-bed modular healthcare ward which is shrunk in size to minimize the requirement of space and other construction commodities such as materials, labour and power. Additionally, HVAC energy usage is accounted for conservation. The health safety and thermal comfort of occupants are regulated by monitoring indoor environment attributes while pushing towards a resource-efficient structure. Two popular envelope thermal retrofits viz. phase change material and thermal insulation are tested to conceive gains in terms of improved energy performance of the ward. Various ward designs contest with their energy performance and occupant's health safety and comfort characteristics in a multicriteria decision making process for delivering the most favourable solution. Subsequently, the most suitable solution is offered by a design involving thermal insulation retrofit with 8 ACH fresh air supply rate and 26°C inlet air temperature. The proposed design can support developing nations to contrive quick response to pandemic outbreaks with reduced construction (cost, time) and energy loads. © 2021 Elsevier Ltd