Moving toward smart biomedical sensing.

The development of novel biomedical sensors as highly promising devices/tools in early diagnosis and therapy monitoring of many diseases and disorders has recently witnessed unprecedented growth; more and faster than ever. Nonetheless, on the eve of Industry 5.0 and by learning from defects of current sensors in smart diagnostics of pandemics, there is still a long way to go to achieve the ideal biomedical sensors capable of meeting the growing needs and expectations for smart biomedical/diagnostic sensing through eHealth systems. Herein, an overview is provided to highlight the importance and necessity of an inevitable transition in the era of digital health/Healthcare 4.0 towards smart biomedical/diagnostic sensing and how to approach it via new digital technologies including Internet of Things (IoT), artificial intelligence, IoT gateways (smartphones, readers), etc. This review will bring together the different types of smartphone/reader-based biomedical sensors, which have been employing for a wide variety of optical/electrical/electrochemical biosensing applications and paving the way for future eHealth diagnostic devices by moving towards smart biomedical sensing. Here, alongside highlighting the characteristics/criteria that should be met by the developed sensors towards smart biomedical sensing, the challenging issues ahead are delineated along with a comprehensive outlook on this extremely necessary field.

Industry 4.0 adoption for healthcare supply chain performance during COVID-19 pandemic in Brazil and India: the mediating role of resilience abilities development

This research investigates the mediation of resilience abilities on the relationship between Industry 4.0 technologies adoption and healthcare supply chain performance during the COVID-19 outbreak in Brazil and India. We surveyed 179 practitioners from organizations at different tiers of the healthcare supply chain (e.g., manufacturers, distributors, and care providers) in July 2021. Multivariate data techniques are used to the collected data to verify the hypotheses anchored on concepts from resource dependence theory. We identify two constructs of Industry 4.0 technologies (named after their predominant roles) and two constructs of resilience abilities (named according to the main abilities encompassed). Our findings indicate that resilience abilities mediate the impact of Industry 4.0 technologies on the performance of the healthcare supply chain since the beginning of the COVID-19 pandemic. However, the role played by adaptive and restorative abilities seems more prominent than the one played by anticipation and monitoring abilities. Further, sensing and communication technologies directly affect the healthcare supply chain's performance. Our study brings together three emerging topics related to the literature on the healthcare supply chain (Industry 4.0 adoption, resilience abilities development, and the disruptions caused by the COVID-19 pandemic). Although digitalization of the healthcare supply chain does improve its performance, our research indicated that its impact could be significantly enhanced when resilience abilities are concurrently developed, particularly in the Indian and Brazilian contexts. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Critical Success Factors for Successful Implementation of Healthcare 4.0: A Literature Review and Future Research Agenda.

The digitization of healthcare services is a major shift in the manner in which healthcare services are offered and managed in the modern era. The COVID-19 pandemic has speeded up the use of digital technologies in the healthcare sector. Healthcare 4.0 (H4.0) is much more than the adoption of digital tools, however; going beyond that, it is the digital transformation of healthcare. The successful implementation of H 4.0 presents a challenge as social and technical factors must be considered. This study, through a systematic literature review, expounds ten critical success factors for the successful implementation of H 4.0. Bibliometric analysis of existing articles is also carried out to understand the development of knowledge in this domain. H 4.0 is rapidly gaining prominence, and a comprehensive review of critical success factors in this area has yet to be conducted. Conducting such a review makes a valuable contribution to the body of knowledge in healthcare operations management. Furthermore, this study will also help healthcare practitioners and policymakers to develop strategies to manage the ten critical success factors while implementing H 4.0.

Pregnancy in the time of COVID-19: towards Fetal monitoring 4.0.

On the outbreak of the global COVID-19 pandemic, high-risk and vulnerable groups in the population were at particular risk of severe disease progression. Pregnant women were one of these groups. The infectious disease endangered not only the physical health of pregnant women, but also their mental well-being. Improving the mental health of pregnant women and reducing their risk of an infectious disease could be achieved by using remote home monitoring solutions. These would allow the health of the mother and fetus to be monitored from the comfort of their home, a reduction in the number of physical visits to the doctor and thereby eliminate the need for the mother to venture into high-risk public places. The most commonly used technique in clinical practice, cardiotocography, suffers from low specificity and requires skilled personnel for the examination. For that and due to the intermittent and active nature of its measurements, it is inappropriate for continuous home monitoring. The pandemic has demonstrated that the future lies in accurate remote monitoring and it is therefore vital to search for an option for fetal monitoring based on state-of-the-art technology that would provide a safe, accurate, and reliable information regarding fetal and maternal health state. In this paper, we thus provide a technical and critical review of the latest literature and on this topic to provide the readers the insights to the applications and future directions in fetal monitoring. We extensively discuss the remaining challenges and obstacles in future research and in developing the fetal monitoring in the new era of Fetal monitoring 4.0, based on the pillars of Healthcare 4.0.

Technologies for vaccinating COVID-19, its variants and future pandemics: a short survey

Technologies play a vital role in handling the healthcare sector. In the healthcare sector, there are numerous stakeholders. For example, doctors, hospitals, medical equipment suppliers, health insurance providers, medicine producers, medicine suppliers, and other medicine-based services. It has been recently observed in various studies that COVID-19 vaccine preservation and distribution require special requirements. The existing infrastructure and distribution system do not fulfill the requirements of COVID-19 vaccine distribution. Thus, various temporary solutions are adopted in different scenarios to handle it efficiently. This work surveyed the recent developments in COVID-19 vaccine distribution, challenges, and optimization approaches. Here, those solutions are discussed that give importance to technologies for handling COVID-19 vaccine preservation and distribution system. Further, those architectures are emphasized that consider minimizing the healthcare costs and optimize the system performance for efficiently handling different subsystems. © 2022 Elsevier Inc. All rights reserved.

Health condition prediction and covid risk detection using healthcare 4.0 techniques

Healthcare 4.0 is one of the emerging concepts that has grabbed the interest among researchers as well as the medical sector. Using the Internet of Things (IoT) and sophisticated communication technologies, it is now possible to monitor the patient from a remote area. In this paper, we design a remote health monitoring system using IoT and Machine Learning (ML) to determine the health condition of a patient. Supervised ML algorithms along with a time-series model such as Seasonal Autoregressive Integrated Moving Average (SARIMA) model are applied on the gathered data from IoT medical sensors to predict the health status of a patient. We consider a use-case of covid and compared it with our sensor data by applying the unsupervised ML algorithm, Long Short Term Memory (LSTM) along with a stochastic model, namely Markov Model to detect the risk of getting covid for a particular patient. LSTM with Markov model provides better results for detection with root mean squared error (RMSE) of 0.18 as against the RMSE of 0.45 obtained with only LSTM. We further design an optimization algorithm using “fuzzy logic” that attains optimum results in detecting the risk of getting covid.

Healthcare 4.0 significance and benefits affirmed by the COVID-19 pandemic

When a pandemic happens, our first line of defense is the healthcare system, which must be effective, agile, and efficient in handling the pressure, and Healthcare 4.0 is a good way to achieve this. However, we are nowhere near a real implementation of Healthcare 4.0. When COVID-19 hit, it stretched resources beyond the limits and drove healthcare organizations to move quickly to automate procedures, introduce remote and mobile healthcare services, and enhance the value chain. This affirmed the importance of a well-designed, highly efficient, and smart healthcare infrastructure to better handle future healthcare crises. A Healthcare 4.0 infrastructure will create a framework allowing healthcare systems to expand their capabilities quickly. In this chapter, we investigate what went on during the pandemic and highlight the areas where Healthcare 4.0 could have made a difference. We also discuss how we need to move forward for better adoption and a more effective Healthcare 4.0 infrastructure. © 2022 Elsevier Inc. All rights reserved.

Enabling Healthcare 4.0 applications development through a middleware platform

Smart healthcare is a promising direction for healthcare services and can be facilitated by adopting the Healthcare 4.0 vision, which utilizes new technologies to provide value-added healthcare services to patients. Developing and integrating Healthcare 4.0 applications is challenging in terms of design complexity, architectural choices, support services, reliability, security, and privacy. This chapter discusses these challenges and identifies the requirements for enabling the Healthcare 4.0 vision. It also proposes H4Ware, a service-oriented middleware, for Healthcare 4.0. H4Ware helps utilize new and emerging technologies to relax some of these challenges and provides a coherent collection of support and advanced services, enabling simpler development, integration, and deployment of healthcare applications. An example application illustrating how these services can be integrated is discussed in addition to introducing the core services in H4Ware and a prototype implementation. H4Ware will enable the fast development of healthcare applications that will help handle diseases such as COVID-19. © 2022 Elsevier Inc. All rights reserved.

An AI-based disease detection and prevention scheme for COVID-19.

The proliferating outbreak of COVID-19 raises global health concerns and has brought many countries to a standstill. Several restrain strategies are imposed to suppress and flatten the mortality curve, such as lockdowns, quarantines, etc. Artificial Intelligence (AI) techniques could be a promising solution to leverage these restraint strategies. However, real-time decision-making necessitates a cloud-oriented AI solution to control the pandemic. Though many cloud-oriented solutions exist, they have not been fully exploited for real-time data accessibility and high prediction accuracy. Motivated by these facts, this paper proposes a cloud-oriented AI-based scheme referred to as D-espy (i.e., Disease-espy) for disease detection and prevention. The proposed D-espy scheme performs a comparative analysis between Autoregressive Integrated Moving Average (ARIMA), Vanilla Long Short Term Memory (LSTM), and Stacked LSTM techniques, which signify the dominance of Stacked LSTM in terms of prediction accuracy. Then, a Medical Resource Distribution (MRD) mechanism is proposed for the optimal distribution of medical resources. Next, a three-phase analysis of the COVID-19 spread is presented, which can benefit the governing bodies in deciding lockdown relaxation. Results show the efficacy of the D-espy scheme concerning 96.2% of prediction accuracy compared to the existing approaches.

Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review.

Despite the increasing utilization of lean practices and digital technologies (DTs) related to Industry 4.0, the impact of such dual interventions on healthcare services remains unclear. This study aims to assess the effects of those interventions and provide a comprehensive understanding of their dynamics in healthcare settings. The methodology comprised a systematic review following the PRISMA guidelines, searching for lean interventions supported by DTs. Previous studies reporting outcomes related to patient health, patient flow, quality of care, and efficiency were included. Results show that most of the improvement interventions relied on lean methodology followed by lean combined with Six Sigma. The main supporting technologies were simulation and automation, while emergency departments and laboratories were the main settings. Most interventions focus on patient flow outcomes, reporting positive effects on outcomes related to access to service and utilization of services, including reductions in turnaround time, length of stay, waiting time, and turnover time. Notably, we found scarce outcomes regarding patient health, staff wellbeing, resource use, and savings. This paper, the first to investigate the dual intervention of DTs with lean or lean-Six Sigma in healthcare, summarizes the technical and organizational challenges associated with similar interventions, encourages further research, and promotes practical applications.

Digital technologies: An exploratory study of their role in the resilience of healthcare services.

Descriptions of resilient performance in healthcare services usually emphasize the role of skills and knowledge of caregivers. At the same time, the human factors discipline often frames digital technologies as sources of brittleness. This paper presents an exploratory investigation of the upside of ten digital technologies derived from Healthcare 4.0 (H4.0) in terms of their perceived contribution to six healthcare services and the four abilities of resilient healthcare: monitor, anticipate, respond, and learn. This contribution was assessed through a multinational survey conducted with 109 experts. Emergency rooms (ERs) and intensive care units (ICUs) stood out as the most benefited by H4.0 technologies. That is consistent with the high complexity of those services, which demand resilient performance. Four H4.0 technologies were top ranked regarding their impacts on the resilience of those services. They are further explored in follow-up interviews with ER and ICU professionals from hospitals in emerging and developed economies to collect examples of applications in their routines.

Role of Artificial Intelligence of Things (AIoT) in Covid-19 Pandemic: A Brief Survey

Digital twins, Internet of Things (IoT) and Artificial Intelligence (AI), plays a proactive role in numerous ways during a pandemic such as COVID-19 by allowing us to make informed decisions using real-time data. According to World Health Organization (WHO), COVID-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that predominantly spreads through body fluids, leading to a mild-to-severe respiratory illness. Considering the global health crisis due to COVID-19 and novelty of the SARS-CoV-2 virus due diligence is required in vaccine preparation and human trials. At the early stages of the pandemic, due to lack of complete knowledge on the virus, there are two main objectives: (1) treat patients as effectively as possible and (2) control the spread of the disease. IoT devices in healthcare empower the healthcare industry in identifying potential carriers of COVID-19 and quarantine. Even though IoT plays a major role in healthcare 4.0, decision making capabilities are limited due to the type of the algorithms and decision making paradigms used. Using AI, we will be able to identify critical medical conditions earlier and take necessary steps. Artificial Intelligence of Things (AIoT) implementation has the potential to greatly reduce the mortality rate allowing us in early identification of high-risk patients, monitoring the spread of the disease, methods to limit the spread, predict mortality risk by analyzing patient's health history, remote or in-home treatments to reduce hospital occupancy, and other techniques to significantly control the spread and treat the patients effectively.

Analytical study of the effect of agnihotra on AQI and its psycho-social impacts: a perspective amidst second wave of pandemic challenges in National Capital Region of Indian subcontinents

This research paper is a step towards the study to see how Vedic Homa Therapy is an effective natural approach for treatment of any pollution, heavy PM 2.5 and PM 10 particles and the use of mango wood, cow dung and bargad wood in the cure of ailment, depression, pollution control by just focusing on its lyrics, sound, diction when done continuously. By performing Yagya, two energies are produced. Heat energy from fire of Yagya and the sound energy from vibration of the Vedic mantras;both the energies are combined to give self-healing results on any disease and its ionisation produces a vital role in curbing polluting particles. The study has done comparative analysis on emission of gaseous particles after Yagya post-second wave of COVID-19 and also through ML algorithms and statistical analysis;it demonstrates the auto correlation and high correlation on different parameters responsible for pollution measurement and for AQI.