Provider's satisfaction with telemedicine services for tribals of Western Rajasthan: A qualitative analysis

Background

There are significant barriers to healthcare access in tribal areas, even though for every 834 people, there is one public physician (registered allopathic and AYUSH doctors). More than 86% of hospital visits occur in rural areas. Furthermore, the bulk of them travel long distances to reach hospitals. A telemedicine center was established in the aspirational tribal district of Sirohi, Rajasthan, to provide accessible quality health care.

Objective

To understand providers' attitudes and satisfaction with telemedicine services for tribal populations.

Materials and Methods

This cross-sectional, mixed-method study enrolled consultants from various clinical departments of AIIMS Jodhpur (n = 23) who provide teleconsultations to the tribal population.

Result

The mean score of the satisfactory index was 54.7 ± 22.04. The higher score is 87.4 regarding the ability to use the technology platform during teleconsultation. The lower score was 34.7 for video quality during teleconsultation at STHR. 91.3% found this a beneficial model for the tribal population. Consultants providing teleconsultations expressed that this model is a boon for tribal patients as a screening tool and will save time and money for improved accessibility.

Conclusion

Positive indications of teleconsultation with a provider's utility, acceptability, and satisfaction. Most marginalized people can efficiently access all levels of (primary, secondary, or tertiary) health care from experts through telemedicine, which will broaden outreach in hard-to-reach or inaccessible tribal or rural areas.

The global impact of the COVID-19 pandemic on pediatric spinal care: A multi-centric study

Background

The COVID-19 pandemic has affected healthcare worldwide since December 2019. We aimed to identify the effect of the COVID-19 pandemic on outpatient clinic and surgical volumes and peri-operative complications for pediatric spinal deformities patients.

Methods

In this multi-center retrospective study, outpatient visits (in-person and virtual care) and pediatric spine surgeries volumes in four high-volume pediatric spine centers were compared between March and December 2019 and the same period in 2020. Peri-operative complications were collected and compared in the same periods. Descriptive statistics were calculated, and comparative analyses were performed.

Results

During the 2020 study period, the outpatient visit (in-person and virtual care) volume decreased during local lockdown periods by 71% for new patients (p < 0.001) and 53% for returning patients (p = 0.03). Overall, for 2020, there was a 20% reduction in new patients (p = 0.001) and 21% decrease in returning patients (p < 0.001). During the pandemic, there was also 20% less overall surgical volume of adolescent idiopathic scoliosis (AIS) patients undergoing primary posterior spinal fusion, with a 70% reduction during lockdown times (p < 0.001). Complication rate and profile were similar between periods.

Conclusion

There was a significant decrease in outpatient pediatric spine outpatient visits, particularly new patients, which may increase the proportion of pediatric patients with spinal deformities that present late, meeting surgical indication. This, in combination with the reduction in surgical volume of AIS over the first year of the pandemic, could result in an extended waitlist for surgeries during years to come. Complication rate was similar for both periods, suggesting it is safe to continue elective pediatric spine surgery even in a time of a pandemic.

Level of evidence

level IV.

Analysing the current status, hotspots, and future trends of technology management: Using the WoS and scopus database

This study presents a comprehensive overview of the development process and the latest trends in technology management (TM), laying a robust foundation for further advancements in this domain. To achieve this, we analysed 1944 TM articles from the Web of Science database and 2642 articles from Scopus, spanning the last 20 years. Employing methodologies that involve scientific knowledge graphs and bibliometrics, we analysed diverse aspects such as changes in the annual publication of articles; geographical distribution among countries, institutions, disciplines, and authors; keyword co-occurrence and clusters; and timezone view. Our findings reveal a significant surge in TM's growth in recent years, showcasing its highly promising potential. The USA is the frontrunner in contributing to TM research, followed by China and the UK. TM research is relatively concentrated in the UK, while it appears more dispersed in China. The University of Cambridge had the highest volume of research, and the disciplines of Business, Management, Engineering, and Computer Science occupied the top spots. As TM evolves, a possible challenge could be the emergence of new authoritative authors. Second, TM's vibrant landscape is characterised by hotspots such as innovation, technology strategy, technology acquisition, technology application, technology standards, and sustainable development. Among these, information and medical technologies stand out as the most frequently referenced technologies. Third, the trends in TM are as follows: innovation is subdivided into technological innovation and open innovation, bibliometric analysis and patent analysis have become pivotal methods for knowledge management, the scope of TM has expanded from internal organisational processes to encompass external aspects, and TM is gradually evolving into a mature science, with its focus transitioning from macro to micro and becoming more profound and detailed. Last, Industry 4.0, artificial intelligence, big data, and the IoT represent the latest frontier technologies in the realm of TM.

Physician's hesitancy in treating COVID-19 patients and its associated occupational risk factors in Indonesia: an online cross-sectional survey

This cross-sectional survey aims to investigate physician hesitancy in treating COVID-19 patients in Indonesia, particularly among those who have already contracted the disease, along with associated occupational risk factors. The study involved distributing a questionnaire to physicians across the country, using contact information from the Indonesian Physician Association database. The results show that out of the 383 participants, 25.6% experienced moderate symptoms of COVID-19, and 2.9% required critical care. The study found that 20.3% of physicians demonstrated hesitancy to treat suspected, probable, or confirmed COVID-19 patients. Interestingly, older physicians and those with less experience in treating COVID-19 patients were found to have a higher hesitancy rate, while specialist trainees and those working in public hospitals demonstrated the lowest hesitancy. These findings highlight the significant hesitancy among physicians who have suffered from COVID-19 and underline the need for management and policymakers to take further action to address this issue. Understanding the effects and benefits of physician hesitancy in treating COVID-19 patients is crucial for ensuring the effective delivery of healthcare services during pandemics like COVID-19.

A medical assistive robot for tele-healthcare during the COVID-19 pandemic: development and usability study in an isolation ward

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic is affecting the mental and emotional well-being of patients, family members, and healthcare workers. Patients in the isolation ward may have psychological problems due to long-term hospitalization, the development of the epidemic, and the inability to meet their families. The medical assistive robot (MAR), acting as an intermediary of communication, can be deployed to address mental pressures.

OBJECTIVE

CareDo, a MAR with telepresence and teleoperation functions, is developed in this work for remote healthcare. This study aims to investigate its practical performance in the isolation ward during the pandemic.

METHODS

Two systems were integrated into the CareDo robot. For the telepresence system, web real-time communications solution is used for the multi-user chat system and the convolutional neural network is used for expression recognition. For the teleoperation system, an incremental motion mapping method is used for operating the robot remotely. This study was finally conducted at the First Affiliated Hospital, Zhejiang University for clinical trials.

RESULTS

During the clinical trials in First Affiliated Hospital, Zhejiang University, tasks such as video chatting, emotion detection, and medical supplies delivery are performed through this robot. Seven voice commands are set for performing system wakeup, video chatting, and system exiting. Statistical durations from 1 second to 3 seconds of common commands are set to improve voice command detection. The facial expression was recorded 152 times for a patient in one day for the psychological intervention. The recognition accuracy reaches 95% and 92.8% for happy and neutral expressions respectively.

CONCLUSIONS

Patients and healthcare workers can use this MAR in the isolation ward for tele-healthcare during the COVID-19 pandemic. It can be a useful approach to break the chains of virus transmission, and also an effective way for remote psychological intervention.

CLINICALTRIAL

Utilization of remote e-prescription (Anat) in Saudi Arabia during COVID-19: Factors associated with primary adherence and antibiotic prescription

Background

The COVID-19 pandemic has affected healthcare systems globally. Various health care technologies have been used to mitigate the risk of disease transmission. Telemedicine is one such technology, and remote consulting and prescribing comprise one of its key aspects. In Saudi Arabia, telephone health services have been widely used through the free Medical Consultation Call Center (937). This platform facilitates medical consultations for all citizens, residents, and visitors. After consultations, healthcare providers are able to issue authenticated e-prescriptions using the Anat platform.

Objectives

To explore the utilization of the Anat remote prescription system in Saudi Arabia during the COVID-19 pandemic and to identify the factors associated with antibiotic prescription and primary medication adherence.

Methods

This retrospective analysis included data from the Anat e‑prescription system using a stratified random sample of 25000 prescriptions issued in Saudi Arabia in 2020. Predictive factors related to the patients, practitioners, and prescriptions were identified through bivariate and multivariate logistic regression analyses.

Results

Out of 25,000 e-prescriptions, 8885 were dispensed, resulting in a 35.5% primary medication adherence rate. The significant predictors of primary adherence were children, respiratory diseases, and antibacterial drugs. In addition, antibiotics made up 32.1% of the e-prescriptions. The prescription of antibiotics was significantly associated with male sex, children, genitourinary system diseases, and being treated by radiologists.

Conclusions

Almost two thirds 62.2% of e-prescriptions were undispensed, with antibiotic eprescriptions at 32.1%. Findings emphasize the need to enhance primary medication adherence and antibiotic prescription interventions. These findings could aid decision-makers in improving patient-centered e-prescribing practices.

Intellectual capital and knowledge management for overcoming social and economic barriers in the health-care sector

Purpose

This study aims to explore how knowledge management (KM) influences the intellectual capital (IC) of organizations operating in health care and how IC and knowledge-sharing (KS) can contribute to the achievement of sustainable development in health systems. Notably, this study focuses on telemedicine, investigating how relational capital contributes to KS in the context of remote care services.

Design/methodology/approach

To comply with the paper’s aim, the authors use a qualitative research method based on a polar case study suitable for IC in health-care studies. More precisely, this study analyzes a nonprofit organization that, for over 15 years, has offered a free multispecialist teleconsultation service to answer medical questions from the most disadvantaged places in the world.

Findings

The findings show that the KM significantly contributes to the IC of organizations. Indeed, it improves the data management and transmission system, it increases performance flexibility in times of resource scarcity without compromising business objectives and it can attract new human resources even when not motivated by selfish goals (volunteer physicians).

Research limitations/implications

This research contributes to studies on IC in health care by focusing on the contribution of telemedicine to the creation of IC. In particular, this work emphasizes the ability of telemedicine to develop and share knowledge in disadvantaged areas of the world. Moreover, in the current context, still strongly permeated by the health emergency generated by the pandemic and recently by the war in Eastern Europe, the importance of such assistance and diagnosis grows.

Practical implications

The conclusions the research findings lead may guide policymakers toward a policy supporting telemedicine. It would alleviate general health-care costs and completely revolutionize light health care’s role. Moreover, reducing socioeconomic distances, improving access to care and applying innovative technologies for sharing outcomes foster balanced socioeconomic development and knowledge dissemination.

Originality/value

This research has shown how telemedicine represents a new successful business model even in times of crisis. The organizational model makes it possible to offer cutting-edge specialized care, contain costs, easily reach disadvantaged areas of the planet, strengthen the skills and autonomy of the most backward countries through a process of KS and push the structures operating there to interact with those in advanced countries.

Embedded Sensor Systems in Medical Devices: Requisites and Challenges Ahead

The evolution of technology enables the design of smarter medical devices. Embedded Sensor Systems play an important role, both in monitoring and diagnostic devices for healthcare. The design and development of Embedded Sensor Systems for medical devices are subjected to standards and regulations that will depend on the intended use of the device as well as the used technology. This article summarizes the challenges to be faced when designing Embedded Sensor Systems for the medical sector. With this aim, it presents the innovation context of the sector, the stages of new medical device development, the technological components that make up an Embedded Sensor System and the regulatory framework that applies to it. Finally, this article highlights the need to define new medical product design and development methodologies that help companies to successfully introduce new technologies in medical devices.

Essential health services delivery in South Africa during COVID-19: Community and healthcare worker perspectives

Background

Between May 2020 and February 2022, South Africa's health system bore strain as it battled mitigating the coronavirus pandemic. The country's pandemic response was scrutinized. This period also brought into focus pre-existing shortcomings in the healthcare system and its governing bodies. Contextually, there is a paucity in literature on the experiences of healthcare providers and users. This study aimed to contribute information on COVID-19, with the intention of providing guidance on preparing for future infectious disease outbreaks.

Methods

Cross sectional exploratory qualitative methodology was employed using semi-structured interviews and focus group discussions with community members (CM) and healthcare workers (HCW) from two South African study sites: (a) rural Bushbuckridge (run by Agincourt Health and Socio-Demographic Surveillance Site) and (b), Regions D and F in Johannesburg Metropole.

Results

After interviewing 42 CMs and 43 HCWs, it emerged that mandated process changes while minimizing COVID-19 exposure, necessitated healthcare personnel focusing on critical care treatment at the expense of less acute ones. COVID-19 isolation protocols, extensive absenteeism and HCWs with advanced skills being perceived as more adept to treat COVID-19 patients contributed to HCWs experiencing higher workloads. Fears regarding contracting and transmitting COVID-19, suffering financial losses, and not being able to provide adequate advice to patients were recurrent themes. Dissemination of relevant information among healthcare facility personnel and communities suffered due to breakdowns in communication.

Conclusion

Concessions and novel strategies to avail medication to patients had to be created. Since providence was lacking, government needs to formulate health intervention strategies that embrace health literacy, alternate methods of chronic medication dispensation, improved communication across health care platforms and the use of telehealth, to circumvent the threats of possible further infectious disease outbreaks.

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

User satisfaction and the readiness-to-use e-health applications in the future in Polish society in the early phase of the COVID-19 pandemic: A cross-sectional study

Introduction

The COVID-19 pandemic resulted in the rapid expansion of e-health services in Poland. The main aim of the study was to assess the determinants of user satisfaction and the readiness-to-use e-health applications in Polish society.

Patients and methods

The paper presents the results of the analysis of the data obtained through a computer-assisted web interviewing survey in a representative sample of 1002 adult Internet users in Poland. The survey was based on a questionnaire consisting of 55 items. The determinants of user satisfaction and the readiness-to-use of e-health solutions were assessed with univariable and hierarchical logistic regression models.

Results

E-health services had been used by 60.6% of respondents from the beginning of the epidemic state in Poland to June 2020, when the survey was performed. The hierarchical model showed that user satisfaction with e-health interactions was significantly associated with HL (Health Literacy) and eHL (e-Health Literacy), COVID-19-related conspiracy beliefs score, and using a televisit because of acute symptoms not suggesting COVID-19. Readiness-to-use e-health in the future showed a significant relationship with place of residence, marital status, eHL, the self-assessment of knowledge about preventing COVID-19, the use of televisits for renewing prescriptions, and the level of satisfaction with e-health services.

Conclusions

Satisfaction with e-health services depends mainly on the HL, eHL, and conspiracy beliefs of patients. Readiness-to-use e-health in the future is associated with the level of eHL and sociodemographic characteristics, but previous experience with e-health services seems to be the main predictor.

Co-Creative Vermittlungsplattformen für die Psychische Gesundheitsversorgung

Im deutschen Gesundheitswesen nimmt die Versorgung psychischer Störungen eine immer wichtigere Rolle ein. Nicht erst seit dem Beginn der Corona-Pandemie steigt die Zahl der Menschen mit psychischen Erkrankungen deutlich an. Damit gehen sowohl wirtschaftliche als auch persönliche Herausforderungen einher.

Dieser Artikel leistet einen Beitrag zur digitalen Transformation im Gesundheitswesen, indem eine konzeptionelle Analyse der Vermittlungsproblematik in der psychischen Gesundheitsversorgung vorgenommen wird. Die Studie zeigt, dass digitale Plattformen die Möglichkeit bieten, den bisherigen undurchsichtigen Angebotsmarkt zu strukturieren, Betroffenen sowie deren Angehörigen passende online und offline Versorgungs- und Unterstützungsangebote niederschwellig zugänglich zu machen und die Patient*innenreise sinnvoll zu erweitern. Die Ergebnisse verdeutlichen die Notwendigkeit der Berücksichtigung der Eigenschaften Regionalität, Inklusion und Kollaboration der Plattform sowie die der Umsetzung mit Hilfe co-creativer Methodiken. Zusammenfassend stellt die Studie eine umfassende und kontextualisierte Konzeption einer Vermittlungsplattform dar, und liefert damit sowohl für den wissenschaftlichen Diskurs im Bereich Gesundheits-IT als auch für Stakeholder aus der Praxis relevante Anhaltspunkte für die Gestaltung und Konzeption zukünftiger Plattformen.

Telemedicine management of type 2 diabetes mellitus in obese and overweight young and middle-aged patients during COVID-19 outbreak: A single-center, prospective, randomized control study

Objective

The coronavirus disease-2019 (COVID-19) pandemic severely affected the disease management of patients with chronic illnesses such as type 2 diabetes mellitus (T2DM). This study aimed to assess the effect of telemedicine management of diabetes in obese and overweight young and middle-aged patients with T2DM during the COVID-19 pandemic.

Methods

A single-center randomized control study was conducted in 120 obese or overweight (body mass index [BMI] ≥ 24 kg/m2) young and middle-aged patients (aged 18–55 years) with T2DM. Patients were randomly assigned to the intervention (telemedicine) or control (conventional outpatient clinic appointment) group. After baseline assessment, they were home isolated for 21 days, received diet and exercise guidance, underwent glucose monitoring, and followed up for 6 months. Glucose monitoring and Self-Rating Depression Scale (SDS) scores were evaluated at 22 days and at the end of 3 and 6 months.

Results

Ninety-nine patients completed the 6-month follow-up (intervention group: n = 52; control group: n = 47). On day 22, the fasting blood glucose (FBG) level of the intervention group was lower than that of the control group (p < 0.05), and the control group’s SDS increased significantly compared with the baseline value (p < 0.05). At the end of 3 months, glycated hemoglobin (HbA1c) and FBG levels in the intervention group decreased significantly compared with those in the control group (p < 0.01). At the end of 6 months, the intervention group showed a significant decrease in postprandial blood glucose, triglyceride, and low-density lipoprotein cholesterol levels as well as waist-to-hip ratio compared with the control group (p < 0.05); moreover, the intervention group showed lower SDS scores than the baseline value (p < 0.05). Further, the intervention group showed a significant reduction in BMI compared with the control group at the end of 3 and 6 months (p < 0.01).

Conclusion

Telemedicine is a beneficial strategy for achieving remotely supervised blood glucose regulation, weight loss, and depression relief in patients with T2DM.

Trial registration

ClinicalTrials.gov Identifier: NCT04723550.

Real-time internet of medical things framework for early detection of Covid-19

The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.

Two-stage robust telemedicine assignment problem with uncertain service duration and no-show behaviours

The current pandemic of COVID-19 has caused significant strain on medical center resources, which are the main plac healthcare managers to make an effective assignment plan for the patients and telemedical doctors when providing telemedicine services. Motivated by this, we present the first comprehensive study of a two-stage robust telemedicine assignment problem when three different sources of uncertainty are incorporated, including uncertain service duration, no-show behaviours of both patients and telemedical doctors. From an algorithmic viewpoint, we propose an efficient nested column-and-constraint generation (C&CG) solution scheme that decomposes the model into an outer level problem and an inner level problem. Our results show that we can solve the problems of realistic sizes within a reasonable time (e.g., up to 100 patients, 10 telemedical doctors, and 200 scenarios within two hours). On the empirical side, we demonstrate how the hyper-parameters make a balance between cost management and the coverage level of the served patients in the presence of three different sources of uncertainty. Our comparison with a two-stage stochastic programming model implies that our model is not overly conservative and seems to provide a relatively cheaper modeling alternative that requires much less information support when hedging against three different sources of uncertainty under a worst-case situation.

Infodemiological study on the impact of the COVID-19 pandemic on increased headache incidences at the world level

The analysis of the public interest as reflected by Internet queries has become a highly valuable tool in many fields. The Google Trends platform, providing timely and informative data, has become increasingly popular in health and medical studies. This study explores whether Internet search frequencies for the keyword “headache” have been increasing after the COVID-19 pandemic outbreak, which could signal an increased incidence of the health problem. Weekly search volume data for 5 years spanning February 2017 to February 2022 were sourced from Google Trends. Six statistical and machine-learning methods were implemented on training and testing sets via pre-set automated forecasting algorithms. Holt-Winters has been identified as overperforming in predicting web query trends through several accuracy measures and the DM test for forecasting superiority and has been employed for producing the baseline level in the estimation of excess query level over the first pandemic wave. Findings indicate that the COVID-19 pandemic resulted in an increased global incidence of headache (as proxied by related web queries) in the first 6 months after its outbreak, with an excess occurrence of 4.53% globally. However, the study also concludes that the increasing trend in headache incidence at the world level would have continued in the absence of the pandemic, but it has been accelerated by the pandemic event. Results further show mixed correlations at the country-level between COVID-19 infection rates and population web-search behavior, suggesting that the increased headache incidence is caused by pandemic-related factors (i.e. increased stress and mental health problems), rather than a direct effect of coronavirus infections. Other noteworthy findings entail that in the Philippines, the term "headache" was the most frequently searched term in the period spanning February 2020 to February 2022, indicating that headache occurrences are a significant aspect that defines population health at the country level. High relative interest is also detected in Kenya and South Africa after the pandemic outbreak. Additionally, research findings indicate that the relative interest has decreased in some countries (i.e. US, Canada, and Australia), whereas it has increased in others (i.e. India and Pakistan) after the pandemic outbreak. We conclude that observing Internet search habits can provide timely information for policymakers on collective health trends, as opposed to ex-post statistics, and can furthermore yield valuable information for the pain management drug market key players about aggregate consumer behavior.

Influence factors analysis of COVID-19 Prevention behavior of chinese Citizens: a path analysis based on the hypothetical model

BACKGROUND

Under the outbreak of Coronavirus disease 2019 (COVID-19), a structural equation model was established to determine the causality of important factors that affect Chinese citizens' COVID-19 prevention behavior.

METHODS

The survey in Qingdao covered several communities in 10 districts and used the method of cluster random sampling. The research instrument used in this study is a self-compiled Chinese version of the questionnaire. Of the 1215 questionnaires, 1188 were included in our analysis. We use the rank sum test, which is a non-parametric test, to test the influence of citizens'basic sociodemographic variables on prevention behavior, and the rank correlation test to analyze the influencing factors of prevention behavior. IBM AMOS 24.0 was used for path analysis, including estimating regression coefficients and evaluating the statistical fits of the structural model, to further explore the causal relationships between variables.

RESULTS

The result showed that the score in the prevention behavior of all citizens is a median of 5 and a quartile spacing of 0.31. The final structural equation model showed that the external support for fighting the epidemic, the demand level of health information, the cognition of (COVID-19) and the negative emotions after the outbreak had direct effects on the COVID-19 prevention behavior, and that negative emotions and information needs served as mediating variables.

CONCLUSIONS

The study provided a basis for relevant departments to further adopt epidemic prevention and control strategies.

Application of Telemedicine in COVID-19: A Bibliometric Analysis

Background

Telemedicine as a tool that can reduce potential disease spread and fill a gap in healthcare has been increasingly applied during the COVID-19 pandemic. Many studies have summarized telemedicine's technologies or the diseases' applications. However, these studies were reviewed separately. There is a lack of a comprehensive overview of the telemedicine technologies, application areas, and medical service types.

Objective

We aimed to investigate the research direction of telemedicine at COVID-19 and to clarify what kind of telemedicine technology is used in what diseases, and what medical services are provided by telemedicine.

Methods

Publications addressing telemedicine in COVID-19 were retrieved from the PubMed database. To extract bibliographic information and do a bi-clustering analysis, we used Bicomb and gCLUTO. The co-occurrence networks of diseases, technology, and healthcare services were then constructed and shown using R-studio and the Gephi tool.

Results

We retrieved 5,224 research papers on telemedicine at COVID-19 distributed among 1460 journals. Most articles were published in the Journal of Medical Internet Research (166/5,224, 3.18%). The United States published the most articles on telemedicine. The research clusters comprised 6 clusters, which refer to mental health, mhealth, cross-infection control, and self-management of diseases. The network analysis revealed a triple relation with diseases, technologies, and health care services with 303 nodes and 5,664 edges. The entity “delivery of health care” was the node with the highest betweenness centrality at 6,787.79, followed by “remote consultation” (4,395.76) and “infection control” (3,700.50).

Conclusions

The results of this study highlight widely use of telemedicine during COVID-19. Most studies relate to the delivery of health care and mental health services. Technologies were primarily via mobile devices to deliver health care, remote consultation, control infection, and contact tracing. The study assists researchers in comprehending the knowledge structure in this sector, enabling them to discover critical topics and choose the best match for their survey work.

Analytical mapping of information and communication technology in emerging infectious diseases using CiteSpace

The prevalence of severe infectious diseases has become a major global health concern. Currently, the COVID-19 outbreak has spread across the world and has created an unprecedented humanitarian crisis. The proliferation of novel viruses has put traditional health systems under immense pressure and posed several serious issues. Henceforth, early detection, identification, rapid testing, and advanced surveillance systems are required to address public health emergencies. However, Information and Communication Technology (ICT) tackles several issues raised by this pandemic and significantly improves the quality of services in the health care sector. This paper presents an ICT-assisted scientometric analysis of infectious diseases, namely, airborne, food & waterborne, fomite-borne, sexually transmitted illnesses, and vector-borne illnesses. It assesses the international research status of this field in terms of citation structure, prolific journals, and country contributions. It has used the CiteSpace tool to address the visualization needs and in-depth insights of scientific literature to pinpoint core hotspots, research frontiers, emerging research areas, and ICT trends. The research finding reveals that mobile apps, telemedicine, and artificial intelligence technologies have greater scope to reduce the threats of infectious diseases. COVID-19, influenza, HIV, and malaria viruses have been identified as research hotspots whereas COVID-19, contact tracing applications, security and privacy concerns about users' data are the recent challenges in this field that need to address. The United States has produced higher research output in all domains of infectious diseases. Furthermore, it explores the co-occurrence network analysis and intellectual landscape of each domain of infectious diseases. It provides potential research directions and insightful clues to researchers and the academic fraternity for further research.

A framework for information technology-based management against COVID-19 in Iran

Background

The COVID-19 pandemic has become a global concern. Iran is one of the countries affected most by the SARS-CoV-2 outbreak. As a result, the use of information technology (IT) has a variety of applications for pandemic management. The purpose of this study was to develop a conceptual framework for responding to the COVID-19 pandemic via IT management, based on extensive literature review and expert knowledge.

Methods

The conceptual framework is developed in three stages: (1) a literature review to gather practical experience with IT applications for managing the COVID-19 pandemic, (2) a study of Iranian documents and papers that present Iran’s practical experience with COVID-19, and (3) developing a conceptual framework based on the previous steps and validating it through a Delphi approach in two rounds, and by 13 experts.

Results

The proposed conceptual framework demonstrates that during pandemics, 22 different types of technologies were used for various purposes, including virtual education, early warning, rapid screening and diagnosis of infected individuals, and data management. These objectives were classified into six categories, with the following applications highlighted: (1) Prevention (M-health, Internet search queries, telehealth, robotics, Internet of things (IoT), Artificial Intelligence (AI), big data, Virtual Reality (VR), social media); (2) Diagnosis (M-health, drones, telehealth, IoT, Robotics, AI, Decision Support System (DSS), Electronic Health Record (EHR)); (3) Treatment (Telehealth, M-health, AI, Robotic, VR, IoT); (4) Follow-up (Telehealth, M-health, VR), (5) Management & planning (Geographic information system, M-health, IoT, blockchain), and (6) Protection (IoT, AI, Robotic and automatic vehicles, Augmented Reality (AR)). In Iran, the use of IT for prevention has been emphasized through M-health, internet search queries, social media, video conferencing, management and planning objectives using databases, health information systems, dashboards, surveillance systems, and vaccine coverage.

Conclusions

IT capabilities were critical during the COVID-19 outbreak. Practical experience demonstrates that various aspects of information technologies were overlooked. To combat this pandemic, the government and decision-makers of this country should consider strategic planning that incorporates successful experiences against COVID-19 and the most advanced IT capabilities.

Design and Implementation of Intelligent Monitoring System for Head and Neck Surgery Care Based on Internet of Things (IoT)

As a chronic disease, cervical spondylosis is prone to recurrent attacks as we age if we do not pay attention to protection, which can easily lead to symptoms such as osteophytes and herniated discs. In the early stage of cervical spondylosis, it is possible to alleviate the disease and prevent its aggravation by improving poor cervical posture and increasing cervical activities. This article analyzes the current situation and medical prospect of smart wearable devices with the prevention and treatment of cervical spondylosis in white-collar people as the starting point and smart wearable devices as the focus and provides a detailed analysis of the functions, categories, technologies, and applications of smart wearable devices to provide a technical theoretical basis for the construction of the subsequent research system. For the user's health state, some other physiological parameters are sent to data also through mobile Internet, and the user's physiological information is obtained on the computer database in also, which not only provides the monitoring function for the user's health but also provides the information of medical big data elements for medical and health institutions and so on. This article elaborates the requirement analysis of this system, based on which the system architecture design and module division are elaborated. It provides a practical and theoretical basis for further realizing the seamless integration of IoT technology and nursing information management system and improving its depth and breadth in the application of nursing information management system. From the perspective of the way of quantification of nursing practice activities, real-time monitoring, scientific management, and intelligent decision-making, it provides the basis for achieving the quality of nursing services, reducing errors, reducing labor intensity, and improving work efficiency and clinical research.

The effect of online and in-person team-based learning (TBL) on undergraduate endocrinology teaching during COVID-19 pandemic

BACKGROUND

Team-based learning (TBL) combines active and collaborative learning, while incorporating aspects of the flipped classroom approach and problem-based learning. The COVID-19 pandemic presented certain challenges in the delivery of TBL in class. In this study, we investigated the impact of TBL on the academic performance of final year Biomedical Sciences' undergraduate students in the context of an "Endocrine Disorders" study block. We did so by comparing the classical in-person approach and online delivery due to the COVID-19 pandemic.

METHODS

A non-compulsory TBL session was introduced to the curriculum of this block, which followed the traditional 2-h lecture delivery. Comparative analysis was performed for the exam and coursework performance of students who attended the TBL sessions (online and in-person) and those that did not.

RESULTS

Both cohorts of students who attended either in-person (n = 66) or online TBL sessions (n = 109) performed significantly better in their exams (p < 0.05) and a related coursework (p < 0.001 and p < 0.05, respectively) when compared to those that did not attend. For both these cohorts the exam mark distribution was much narrower compared to those that did not attend the TBL sessions where the majority of fails and "no shows" were recorded.

CONCLUSIONS

Online and in-person TBL, can successfully supplement traditional lecture-based teaching and enhance the learning/performance, for complex medical subjects/topics. Our findings demonstrate that it is possible to deliver these sessions online with demonstrable benefit for students suggesting that there is greater flexibility in the use of TBL in higher education.

Blockchain technology and its applications to combat COVID-19 pandemic

Background and aims

The world is currently facing multifaceted problems due to the emergence of the COVID-19 (coronavirus) pandemic. Blockchain technology (BT) plays a vital role in creating a platform for adequately managing the COVID-19 pandemic.

Methods

The latest information on the blockchain and its application in solving challenging problems due to the COVID-19 pandemic are collected and discussed from the available literature searched through PubMed, Scopus, and Google scholar.

Results

In this paper, we propose a blockchain-based platform to combat this pandemic. Furthermore, we have identified and discussed nine significant applications of blockchain in solving the problem arising from the COVID-19 pandemic.

Conclusions

The severity of COVID-19 was so tremendous that the World Health Organization (WHO) had to declare it as a pandemic within a month of its full-scale expansion. The greatest challenge most governments are suffering from is the lack of a precise mechanism to detect the newly infected cases and predict coronavirus infection risk. So, we need a technology-empowered solution to fight during this COVID-19 crisis. The various features of blockchain technology, such as decentralization, transparency, and immutability, can help control this pandemic by early detection of outbreaks, fast-tracking drug delivery, and protecting user privacy during treatment.

Healthcare provider-to-patient perspectives on the uptake of teleconsultation services in the Nigerian healthcare system during the COVID-19 pandemic era

The urgency to curtail the devastating effects of the ongoing COVID-19 Pandemic has led to the implementation of several measures to limit its spread, including movement restrictions and social distancing. As most developing countries rely solely on hospital visitations for their medical needs, this impediment to assessing healthcare services compounded by low uptake of telehealth services could result in dire consequences. This is a cross-sectional study among Healthcare providers (HCP) and Healthcare consumers (HCC) in Nigeria. We administered a pre-validated self-administered online questionnaire comprising questions to assess the knowledge, use, perceptions, and benefits of telemedicine among study participants. Descriptive statistics were used to examine participants' perceptions on telemedicine use and to summarize participants' characteristics. A total of 158 healthcare providers and 1381 healthcare consumers completed the online survey. Ninety percent of HCP reported that they used some form of telemedicine to deliver health care, and 63% of HCC had received healthcare through telemedicine. A significant proportion of HCP (62%) and HCC (69%) agreed that telemedicine would improve healthcare consultation experience and satisfaction. However, fewer (21%) HCP agreed that they liked that there would be no physical contact with patients using telemedicine. In contrast, 52% of HCC agreed that they liked that there would be no physical contact with healthcare providers while using telemedicine. The majority of the participants believed that benefits of telemedicine would include: being a safe way for healthcare delivery during pandemics (HCP = 62%, HCC = 83%), affordability (HCP = 62%, HCC = 82%), and time-saving (HCP = 54%, HCC = 82%,). Teleconsultation services have been shown to aptly complement face-to-face hospital visits in ensuring effective triaging in hospitals and providing adequate healthcare delivery to patients regardless of geographical and physical barriers. These results support telemedicine use for the provision of healthcare services during the COVID-19 pandemic.

Development and utility of a close contact information management system for the COVID-19 pandemic

BACKGROUND

Since the outbreak started in 2019, COVID-19 pandemic has a significant global impact. Due to the highly infective nature of SARS-CoV-2, the COVID-19 close contacts are at significant risk of contracting COVID-19. China's experience in successfully controlling COVID-19 emphasized the importance of managing close contacts because this strategy helps to limit potential infection sources, prevent the unconscious spread of COVID-19 and thus control this pandemic. As a result, to understand and consider the management of close contacts may be beneficial to other countries. However, managing close contacts is challenging owing to the huge number of close contacts and a lack of appropriate management tools and literature references.

METHODS

A new system called the COVID-19 Close Contact Information Management System was developed. Here we introduced the design, use, improvement and achievements of this system.

RESULTS

This system was designed from the standpoint of the Centers for Disease Control and Prevention in charge of managing close contacts. Two main functions and eight modules/themes were ultimately formed after two development stages. The system introduces what information need to be collected in the close contact management. Since the system allows information flow across cities, the geographical distance and administrative regional boundaries are no longer obstacles for managing close contacts, which promotes the management of each close contact. Moreover, when this system is used in conjunction with other data tools, it provides data assistance for understanding the COVID-19 characteristics and formulating targeted COVID-19 control policies. To date, the system has been widely used in Guangdong Province for over 1 year and has recorded tens of thousands of pieces of data. There is sufficient practical experience to suggest that the system is capable of meeting the professional work requirements for close contact management.

CONCLUSIONS

This system provides a new way to manage close contacts and restrict the spread of COVID-19 by combining information technology with disease prevention and control strategies in the realm of public health. We hope that this system will serve as an example and guide for those anticipating similar work in other countries in response to current and future public health incidents.

Telemedicine for healthcare: Capabilities, features, barriers, and applications

Regular hospital visits can be expensive, particularly in rural areas, due to travel costs. In the era of the Covid-19 Pandemic, where physical interaction becomes risky, people prefer telemedicine. Fortunately, medical visits can be reduced when telemedicine services are used through video conferencing or other virtual technologies. Thus, telemedicine saves both the patient's and the health care provider time and the cost of the treatment. Furthermore, due to its fast and advantageous characteristics, it can streamline the workflow of hospitals and clinics. This disruptive technology would make it easier to monitor discharged patients and manage their recovery. As a result, it is sufficient to state that telemedicine can create a win-win situation. This paper aims to explore the significant capabilities, features with treatment workflow, and barriers to the adoption of telemedicine in Healthcare. The paper identifies seventeen significant applications of telemedicine in Healthcare. Telemedicine is described as a medical practitioner to diagnose and treat patients in a remote area. Using health apps for scheduled follow-up visits makes doctors and patients more effective and improves the probability of follow-up, reducing missing appointments and optimising patient outcomes. Patients should have an accurate medical history and show the doctor any prominent rashes, bruises, or other signs that need attention through the excellent quality audio-video system. Further, practitioners need file management and a payment gateway system. Telemedicine technologies allow patients and doctors both to review the treatment process. However, this technology supplements physical consultation and is in no way a substitute for a physical consultation. Today this technology is a safe choice for patients who cannot go to the doctor or sit at home, especially during a pandemic.

Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients

The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.

Optimizing and evaluating PCR-based pooled screening during COVID-19 pandemics

Population screening played a substantial role in safely reopening the economy and avoiding new outbreaks of COVID-19. PCR-based pooled screening makes it possible to test the population with limited resources by pooling multiple individual samples. Our study compared different population-wide screening methods as transmission-mitigating interventions, including pooled PCR, individual PCR, and antigen screening. Incorporating testing-isolation process and individual-level viral load trajectories into an epidemic model, we further studied the impacts of testing-isolation on test sensitivities. Results show that the testing-isolation process could maintain a stable test sensitivity during the outbreak by removing most infected individuals, especially during the epidemic decline. Moreover, we compared the efficiency, accuracy, and cost of different screening methods during the pandemic. Our results show that PCR-based pooled screening is cost-effective in reversing the pandemic at low prevalence. When the prevalence is high, PCR-based pooled screening may not stop the outbreak. In contrast, antigen screening with sufficient frequency could reverse the epidemic, despite the high cost and the large numbers of false positives in the screening process.

A Blockchain and Artificial Intelligence-Based, Patient-Centric Healthcare System for Combating the COVID-19 Pandemic: Opportunities and Applications

The world is facing multiple healthcare challenges because of the emergence of the COVID-19 (coronavirus) pandemic. The pandemic has exposed the limitations of handling public healthcare emergencies using existing digital healthcare technologies. Thus, the COVID-19 situation has forced research institutes and countries to rethink healthcare delivery solutions to ensure continuity of services while people stay at home and practice social distancing. Recently, several researchers have focused on disruptive technologies, such as blockchain and artificial intelligence (AI), to improve the digital healthcare workflow during COVID-19. Blockchain could combat pandemics by enabling decentralized healthcare data sharing, protecting users' privacy, providing data empowerment, and ensuring reliable data management during outbreak tracking. In addition, AI provides intelligent computer-aided solutions by analyzing a patient's medical images and symptoms caused by coronavirus for efficient treatments, future outbreak prediction, and drug manufacturing. Integrating both blockchain and AI could transform the existing healthcare ecosystem by democratizing and optimizing clinical workflows. In this article, we begin with an overview of digital healthcare services and problems that have arisen during the COVID-19 pandemic. Next, we conceptually propose a decentralized, patient-centric healthcare framework based on blockchain and AI to mitigate COVID-19 challenges. Then, we explore the significant applications of integrated blockchain and AI technologies to augment existing public healthcare strategies for tackling COVID-19. Finally, we highlight the challenges and implications for future research within a patient-centric paradigm.

The reliability and validity of the Turkish version of the Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ) and Telemedicine Patient Questionnaire (TPQ) in individuals with multiple sclerosis

Objective

The study was aimed to translate and adapt the Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ) and Telemedicine Patient Questionnaire (TPQ) into Turkish, thereafter to analyze the psychometric properties of both questionnaires.

Methods

A total of 149 multiple sclerosis (MS) patients were recruited in the study. For 4 years, all patients were supervised by a department clinician using telemedicine. Cronbach’s alpha coefficient was used to assess internal consistency. By evaluating the scores of 41 retested patients 1 week later, the test-retest reliability was determined using the intraclass correlation coefficient (ICC). Pearson’s correlation coefficient was used to assess the construct validity (r).

Results

A total of 149 patients (103 women, 46 men) with a mean age of 40.9±10.9 years were included in the study. MS duration of the patients was 9.15±6.24 years. Internal consistency of all items and the total score of the TSUQ were excellent (>0.80; ranged 0.971–0.974). On the other hand, the internal consistency of all items and total score of the TPQ was excellent, either (>0.80; ranged 0.878–0.890). The ICC of the TSUQ’s and TPQ’s total score was excellent (>0.80). The correlation between TSUQ and TPQ was strong (r=0.734, p<0.01). In addition, there was moderate relationship between the TSUQ and the Beck Depression Scale (BDS) (r=−0.363, p<0.01). On the other hand, there was low correlation between TPQ and BDS (r=−0.217, p<0.05).

Conclusion

The Turkish version of the TSUQ and the TPQ is valid and reliable in individuals with MS.

The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees

Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

Advancements in Biosensor Technologies for Medical Field and COVID-19 Pandemic

World health organization (WHO) has declared the COVID-19 outbreak as a public health emergency of international concern and then as a pandemic on 30th of January and 11th of March 2020, respectively. After such concern, the world scientific communities have rushed to search for solutions to bring down the disease’s spread, fast-paced vaccine development, and associated medical research using modern technologies. Biosensor technologies play a crucial role in diagnosing various medical diseases, including COVID-19. The present paper describes the major advancement of biosensor-based technological solutions for medical diagnosis, including COVID-19. This review-based work covers the biosensors and their working principles in the context of medical applications. The paper also discusses different biosensors and their applications to tackle medical issues, including this ongoing pandemic.

Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic

A neural network is one of the current trends in deep learning, which is increasingly gaining attention owing to its contribution in transforming the different facets of human life. It also paves a way to approach the current crisis caused by the coronavirus disease (COVID-19) from all scientific directions. Convolutional neural network (CNN), a type of neural network, is extensively applied in the medical field, and is particularly useful in the current COVID-19 pandemic. In this article, we present the application of CNNs for the diagnosis and prognosis of COVID-19 using X-ray and computed tomography (CT) images of COVID-19 patients. The CNN models discussed in this review were mainly developed for the detection, classification, and segmentation of COVID-19 images. The base models used for detection and classification were AlexNet, Visual Geometry Group Network with 16 layers, residual network, DensNet, GoogLeNet, MobileNet, Inception, and extreme Inception. U-Net and voxel-based broad learning network were used for segmentation. Even with limited datasets, these methods proved to be beneficial for efficiently identifying the occurrence of COVID-19. To further validate these observations, we conducted an experimental study using a simple CNN framework for the binary classification of COVID-19 CT images. We achieved an accuracy of 93% with an F1-score of 0.93. Thus, with the availability of improved medical image datasets, it is evident that CNNs are very useful for the efficient diagnosis and prognosis of COVID-19.

Technological Resources for Fighting COVID-19 Pandemic Health Issues

The COVID-19 pandemic has had a global effect with several people dying daily due to the dreaded disease. Therefore, each individual has a duty to support the efforts of their countries either financially, socially, technically, or by any other means to contribute to the fight against the COVID-19 pandemic. During this crisis, engineers can come up with innovations to fight the pandemic. One of the reasons for the death of a patient suffering from COVID-19 was the lack of resources required for patient care. The doctors who are taking care of COVID patients could get infected due to lack or the deficiency of available safety kits. Some of the resources required to fight COVID-19 are personal protective equipment (PPE) (e.g. gloves, gowns, face masks and shields for respiratory and eye protection respectively), mechanical ventilators and body vital monitoring devices. Engineers can contribute to the fight against COVID-19, by developing compact size ventilators, 3D printed face shield, masks, door handles, hand sanitizer, etc. The available medicines to fight the disease are still under development and trials with limited options at present has led to deaths among patients mostly those who are elderly or having any co-morbid condition. The deficiency of medicine availability can be resolved up to some extent by applying tools of supply chain management.

Tissue Engineering and its Significance in Healthcare During COVID-19 Pandemic: Potential Applications and Perspectives

In the present times of the COVID-19 pandemic, there is a great need for new therapeutic and diagnostic strategies to prevent infectious diseases worldwide. Tissue engineering covers the phenomenon of the evolution of tissue, its behavior and growth factors that are better supported in the medical environment. This area of tissue engineering can support the treatment of infected patients of COVID-19 and can help fight the current crisis and viral outbreaks in general. This study aims to identify the significant advancement of tissue engineering for taking up the challenges posed by COVID-19. Major challenges faced during the COVID-19 pandemic situation in the current scenario are discussed. The significant advancements of tissue engineering in the medical field are listed in chronological order. The positive impacts of tissue engineering during the COVID 19 crisis are discussed and finally its useful applications during the ongoing COVID-19 pandemic situation are identified and briefed. This branch of science’s primary importance is to provide biological alternatives that can perform full or partial functions of the damaged, malfunctioned and failing organs or tissues in humans. It is helpful for the supply of convalescent plasma to patients especially during COVID-19. A donor is selected strictly based on a validated case of COVID-19 contagion. The donor must confirm a negative follow-up molecular examination, free from manifestations; usual good health and other pre-donation screening procedures are to be followed.

COVID-19 Pandemic and Debates on the Design of Operating Theatre Ventilation Systems in Healthcare Facilities

COVID-19 pandemic had a significant impact on providing Trauma and Orthopedic surgery around the world. The orthopedic community had to reconfigure emergency and urgent trauma services safely but also support strategies to prevent person-to-person coronavirus transmission. Various organizations including British Orthopedic Association (BOA), American Academy of Orthopedic Surgeons (AAOS) and Public Health England (PHE) have provided guidelines for conducting safe essential surgery in operating theatres. One of the areas that have not been debated enough is the type of ventilation systems that should be used in operating theatres during this global pandemic. We review the current evidence in the literature particularly in the challenges faced by health care professionals in current COVID-19 pandemic in deciding and implementing an effective operating theatre ventilation system protecting both our patients and operating room personnel.

Bioengineering Technology in Context of COVID-19 Pandemic: Potential Roles and Applications

Bioengineering (BE) technology has significant influence on the healthcare environment. This has grown steadily particularly since the medical practice has become more technology based. We have tried to assess the impact of bioengineering in tackling the COVID-19 pandemic. The use of bioengineering principles in healthcare has been evaluated. The practical implications of these technologies in fighting the current global health pandemic have been presented. There has been a shared drive worldwide to harness the advancements of bioengineering to combat COVID-19. These efforts have ranged from small groups of volunteers to large scale research and mass production. Together the engineering and medical fields have worked to address areas of critical need including the production and delivery of personal protective equipment, ventilators as well as the creation of a viable vaccine. The fight against COVID-19 has helped highlight the work and contributions of so many professionals in the bioengineering fields who are working tirelessly to help our health services cope. Their innovation and ingenuity are paving the way to successfully beat this virus. We must continue to support these fields as we evolve our health systems to deal with the challenges of healthcare in the future.

Nanomedicine Technology and COVID-19 Outbreak: Applications and Challenges

The coronavirus (COVID-19) pandemic is one of the biggest challenges in the field of healthcare. Nanomedicine is a developing area that has the potential to treat various diseases and control infections. Now, its applications are open for the treatment of COVID-19. We have studied relevant papers through Scopus, Google Scholar, Science Direct and ResearchGate on nanomedicine in context of COVID-19. This paper provides detailed information about nanomedicine in the context of healthcare. It further identifies the primary challenges faced in the current situation. This study provides details about the advancements in the area of nanomedicine in healthcare for fighting the COVID-19 pandemic. Finally, we have identified and discussed various significant applications of nanomedicine in solving challenges thrown by the COVID-19 pandemic. Researchers can work on developing applications of nanoparticles with the size of the novel Coronavirus. Nanomedicine is helpful to repair the cells of an infected patient the help of repair proteins. It also plays a vital role in testing medicine and helps many clinical trials get approval from healthcare agencies. In the future, nanomedicine will be helpful for fighting against this pandemic and creating advancements in healthcare.

Exploring the Impact of COVID-19 Pandemic on Medical Supply Chain Disruption

The COVID-19 outbreak is a worldwide disaster that has disrupted several aspects of human life and its community living. This pandemic has also affected the existing supply chain and the way it was being managed. Therefore, this study’s primary objective is to identify and discuss the significant impact of COVID-19 on the current supply chain, with specific reference to the medical supply chain. This article then recommends the potential solution measures that can reduce the impact of COVID-19 on the existing supply chain during and after the COVID-19 pandemic. In order to do so, we have identified and discussed various significant impacts of COVID-19 on the supply chain. Identifying the impacts helps the policy planner formulate the policies to mitigate these impacts and recover the supply chain at their functional level. Further, this study also suggests possible solution measures that can be adopted to reduce the disruption of the supply chain. These solution measures help management develop the action plan for early recovery from the COVID-19 disruption.

Symbiotic Relationship Between Machine Learning and Industry 4.0: A Review

Industry 4.0 though launched less than a decade ago, has revolutionized the way technologies are being used. It has found its application in almost every field of manufacturing, cybersecurity, health, banking, and other services. Industry 4.0 is heavily dependent on interconnectivity and data. Machine learning (ML) acts as a foundation for building industry 4.0 applications. In this paper, we have provided a broad view of how ML is necessary to accomplish the benefits of industry 4.0. The paper includes ML usage in companies and the limitations of ML, which need to be mitigated. There are also some instances of the failure of ML algorithms and their repercussions. Though industry 4.0 requires a lot more inputs and capital than normal processes, the long-run benefits outweigh the initial costs. ML is gaining popularity, and extensive research is happening to exploit its potential and develop full smart applications.

Virtual Reality (VR) Applications in Cardiology: A Review

Virtual reality (VR) has applications in cardiology to create enhancement, thereby improving the quality of associated planning, treatment and surgery. The need is to study different applications of this technology in the field of cardiology. We have studied research papers on VR and its applications in cardiology through a detailed bibliometric analysis. The study identified five significant steps for proper implementation of this technology in cardiology. Some challenges are to be undertaken by using this technology, and they can provide some benefits; thus, authors contemplate extensive research and development. This study also identifies 10 major VR technology applications in cardiology and provided a brief description. This innovative technology helps a heart surgeon to perform complex heart surgery effectively. Thus, VR applications have the potential for improving decision-making, which helps save human life. VR plays a significant role in the development of a surgical procedure. This technology undertakes 3D heart model information in full colour, which helps to analyze the overall heart vane, blockage and blood flow. With the help of this digital technology, a surgeon can improve the accuracy of heart surgery, and he can simulate the surgery. A surgeon can undertake surgery in a virtual environment on a virtual patient. The unique purpose of this technology is to practice pre-operatively on the specific circumstance. A cardiologist can also check the proper status of inner and outer heart wall layer. Thus, by using this 3D information, the surgeon can now interact with heart data/information without any physical touch. This technology opens a new opportunity to improve the heart surgery and development in cardiovascular treatment to improve patient outcome.

IoT-based GPS assisted surveillance system with inter-WBAN geographic routing for pandemic situations

BACKGROUND

Worldwide pandemic situations drive countries into high healthcare costs and dangerous conditions. Hospital occupancy rates and medical expenses increase dramatically. Real-time remote health monitoring and surveillance systems with IoT assisted eHealth equipment play important roles in such pandemic situations. To prevent the spread of a pandemic is as crucial as treating the infected patients. The COVID-19 pandemic is the ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

METHODS

We propose a surveillance system especially for coronavirus pandemic with IoT applications and an inter-WBAN geographic routing algorithm. In this study, coronavirus symptoms such as respiration rate, body temperature, blood pressure, oxygen saturation, heart rate can be monitored and the social distance with 'mask-wearing status' of persons can be displayed with proposed IoT software (Node-RED, InfluxDB, and Grafana).

RESULTS

The geographic routing algorithm is compared with AODV in outdoor areas according to delivery ratio, delay for priority node, packet loss ratio and bit error rate. The results obtained showed that the geographic routing algorithm is more successful for the proposed architecture.

CONCLUSION

The results show that the use of WBAN technology, geographic routing algorithm, and IoT applications helps to achieve a realistic and meaningful surveillance system with better statistical data.

Significant Applications of Big Data in Industry 4.0

Industry 4.0 is being implemented with the help of advanced technologies. Big data, Artificial Intelligence (AI), Robotics, Internet of Things (IoT), Cloud computing, and 3D printing are the major technologies used to adopt Industry 4.0 successfully. Here, the study’s need is to discuss the major potential of big data for Industry 4.0. These technologies’ primary purpose is to collect the right data to solve the relevant issue during manufacturing and other required services. This technology plays a significant role in creating advancements in this fourth industrial revolution. Conclusively, big data applications are useful for in-process management and productivity improvement in the automation sector. Complex systems of drivers and intelligent sensors can be easily optimized based on information collected using this technology. Big data is the key to gain a competitive leap by reconnoitring the fundamental issues like deviations during the process, quality discriminations, and energy efficiency squander in a manufacturing process. The study discusses the significant applications of big data in Industry 4.0. For a proper surveillance system, industries need to have an immensely technical or personalized way, making big data a valuable source for predicting analysis and operation management based on market insight statistics or information. In upcoming days, big data will provide further advancement in Industry 4.0 and is supposed to play an efficient role in its successful adoption.

Cloud Computing in Solving Problems of COVID-19 Pandemic

Cloud computing facilitates collaboration, communication, and essential online services during the COVID-19 crisis. The current situation of the COVID-19 pandemic has compelled people to work from their homes, but they have to communicate, collaborate online. Thus, we see an essential role of cloud computing in taking up this challenge of working from home and delivering efficiently. A brief review of Cloud Computing service in the context of COVID-19 pandemic is done using recent papers’ by searching keywords such as “Cloud Computing” and “COVID-19” from PubMed’s database SCOPUS and Google Scholar. During the lockdown situation, cloud computing technology helps provide commendable service in the healthcare domain. It provides an advanced infrastructure for facilitating digital transformation. A brief discussion has been made on how cloud computing components are vital for overcoming the ongoing situation. This paper also studies the remote working of cloud computing for the COVID-19 pandemic and finally identified significant cloud computing applications for the COVID-19 pandemic. All countries focus on reducing this virus’s spread, so this technology helps minimize the spread of this virus by providing online services. It provides an innovative environment that enhances the creativity and productivity of healthcare workers. This technology is efficient in detecting, tracking, and monitoring newly infected patients. In the future, this technology will insight and provide control over this infection to save millions of lives worldwide. This technology is also quite helpful to forecast the future impact of the SARS-Co-2 virus.

Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections

Remote symptom tracking is critical for the prevention of Covid-19 spread. The qualified medical staff working in the call centers of primary health care units have to take critical decisions often based on vague information about the patient condition. The congestion and the medical protocols that are constantly changing often lead to incorrect decisions. The proposed platform allows the remote assessment of symptoms and can be useful for patients, health institutes and researchers. It consists of mobile desktop applications and medical sensors connected to cloud infrastructure. The unique features offered by the proposed solution are: (a) dynamic adaptation of Medical Protocols (MP) is supported (for the definition of alert rules, sensor sampling strategy and questionnaire structure) covering different medical cases (pre- or post-hospitalization, vulnerable population, etc.), (b) anonymous medical data can be statistically processed in the context of the research about an infection such as Covid-19, (c) reliable diagnosis is supported since several factors are taken into consideration, (d) the platform can be used to drastically reduce the congestion in various healthcare units. For the demonstration of (b), new classification methods based on similarity metrics have been tested for cough sound classification with an accuracy in the order of 90%.

S. SK. Nanotechnology: an emerging approach to combat COVID-19

The recent outbreak of coronavirus disease (COVID-19) has challenged the survival of human existence in the last 1 year. Frontline healthcare professionals were struggling in combating the pandemic situation and were continuously supported with literature, skill set, research activities, and technologies developed by various scientists/researchers all over the world. To handle the continuously mutating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requires amalgamation of conventional technology with emerging approaches. Nanotechnology is science, engineering, and technology dealing at the nanoscale level. It has made possible the development of nanomaterials, nano-biosensors, nanodrugs, and vaccines for diagnosis, therapy, and prevention of COVID-19. This review has elaborately highlighted the role of nanotechnology in developing various detection kits such as nanoparticle-assisted diagnostics, antibody assay, lateral flow immunoassay, nanomaterial biosensors, etc., in detection of SARS-CoV-2. Similarly, various advancements supervene through nanoparticle-based therapeutic drugs for inhibiting viral infection by blocking virus attachment/cell entry, multiplication/replication, and direct inactivation of the virus. Furthermore, information on vaccine development and the role of nanocarriers/nanoparticles were highlighted with a brief outlining of nanomaterial usage in sterilization and preventive mechanisms engineered to combat COVID-19 pandemic.