Recent Trends in Remote Healthcare Applications and Futuristic Approach

Remote healthcare is a well-accepted telemedicine service that renders efficient and reliable healthcare to patients suffering from chronic diseases, neurological disorders, diabetes, osteoporosis, sensory organs, and other ailments. Artificial intelligence, wireless communication, sensors, organic polymers, and wearables enable affordable, non-invasive healthcare to patients in all age groups. Telehealth services and telemedicine are beneficial to people residing in remote locations or patients with limited mobility, rehabilitation treatment, and post-operative recovery. Remote healthcare applications and services proved to be significant during the COVID-19 pandemic for both patients and doctors. This study presents a detailed study of the use of artificial intelligence and the internet of things in applications of remote healthcare in many domains of health, along with recent patents. This research also presents network diagrams of documents from the Scopus database using the tool VOSViewer. The paper highlights gap which can be undertaken by future researchers. © 2023 IEEE.

HarDNet and Dual-Code Attention Mechanism based model for medical images segmentation

During the formation of medical images, they are easily disturbed by factors such as acquisition devices and tissue backgrounds, causing problems such as blurred image backgrounds and difficulty in differentiation. In this paper, we combine the HarDNet module and the multi-coding attention mechanism module to optimize the two stages of encoding and decoding to improve the model segmentation performance. In the encoding stage, the HarDNet module extracts medical image feature information to improve the segmentation network operation speed. In the decoding stage, the multi-coding attention module is used to extract both the position feature information and channel feature information of the image to improve the model segmentation effect. Finally, to improve the segmentation accuracy of small targets, the use of Cross Entropy and Dice combination function is proposed as the loss function of this algorithm. The algorithm has experimented on three different types of medical datasets, Kvasir-SEG, ISIC2018, and COVID-19CT. The values of JS were 0.7189, 0.7702, 0.9895, ACC were 0.8964, 0.9491, 0.9965, SENS were 0.7634, 0.8204, 0.9976, PRE were 0.9214, 0.9504, 0.9931. The experimental results showed that the model proposed in this paper achieved excellent segmentation results in all the above evaluation indexes, which can effectively assist doctors to diagnose related diseases quickly and improve the speed of diagnosis and patients’quality of life. Author

Uncovering the interplay between hypertension and the inflammatory response for the patient affected by COVID-19 via mathematical modeling and computer-based analysis

We explore here the systems-based regulatory mechanisms that determine human blood pressure patterns. This in the context of the reported negative association between hypertension and COVID-19 disease. We are particularly interested in the key role that plays angiotensin converting enzyme 2 (ACE2), one of the first identified receptors that enable the entry of the SARS-CoV-2 virus into a cell. Taking into account the two main systems involved in the regulation of blood pressure, that is, the Renin-Angiotensin system and the Kallikrein-Kinin system, we follow a Bottom-Up systems biology modeling approach in order to built the discrete Boolean model of the gene regulatory network that underlies both the typical hypertensive phenotype and the hypotensive/normotensive phenotype. These phenotypes correspond to the dynamic attractors of the regulatory network modeled on the basis of publicly available experimental information. Our model recovers the observed phenotypes and shows the key role played by the inflammatory response in the emergence of hypertension. Source code go to the next url: https://github.com/cxro-cc/red_ras_kks © 2023, EasyChair. All rights reserved.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE

Towards Intermediated Workflows for Hybrid Telemedicine

The growing platformization of health has spurred new avenues for healthcare access and reinvigorated telemedicine as a viable pathway to care. Telemedicine adoption during the COVID-19 pandemic has surfaced barriers to patient-centered care that call for attention. Our work extends current Human-Computer Interaction (HCI) research on telemedicine and the challenges to remote care, and investigates the scope for enhancing remote care seeking and provision through telemedicine workflows involving intermediation. Our study, focused on the urban Indian context, involved providing doctors with videos of remote clinical examinations to aid in telemedicine. We present a qualitative evaluation of this modified telemedicine experience, highlighting how workflows involving intermediation could bridge existing gaps in telemedicine, and how their acceptance among doctors could shift interaction dynamics between doctors and patients. We conclude by discussing the implications of such telemedicine workflows on patient-centered care and the future of care work. © 2023 Owner/Author.

Identify Type of Lung Infection from Lung Patients X-RAY Image LIVERAGING Computer Vision

This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-The-Art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-Trained models;second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease. © 2023 IEEE.

The Coronavirus Pandemic and the Transformation of Telehealth in the Context of Artificial Intelligence

The pandemic is seriously affecting individuals' wellbeing, occupations, economies, and practices. This pandemic has shaken the world dramatically and framed a moment to think about the future, incorporating our relationship with nature. Since the COVID-19 pandemic started, it's been relied upon to drive remarkable development in telehealth, especially for demonstrative patients, to stay at home and talk with specialists through virtual stations, helping with diminishing the spread of the disease to mass and the clinical staff on the ground zero. The novel coronavirus epidemic has changed our way of living, society, and human services framework. This study proposed the application of artificial intelligence to make its classification. The outcomes of the proposed systems are equated with pre-existing algorithms to highlight the benefits of test time minimization and classification error. Furthermore, this study tries to analyse corona time series data on the level of classification and found that the decision tree algorithm gives the best accuracy of approx. 100% with zero error and zero standard deviation with 7098 milliseconds. © 2022 IEEE.

Intention to Use eSanjeevani-OPD App: An Empirical Investigation Using Health Belief Model

Digital health interventions have become an essential component of every public health system since the COVID-19 pandemic. 'eSanjeevani OPD - Stay at Home OPD' is a telemedicine system that connects doctors and patients launched as part of the Ayushman Bharat project of the Indian government. This study analyses various factors affecting the intention to use eSanjeevani. A theoretical model integrating the health belief model and the theory of reasoned action was framed and empirically tested. Responses were collected using a survey questionnaire(n=248). A partial least square-structural equation modeling was used to analyze the linkages between the constructs. Perceived susceptibility and benefits were found to be the most contributing variables. Attitude has a significant mediating effect on the intention to use eSanjeevani. © 2023 IEEE.

An intelligent platform of services based on multimedia understanding and telehealth for supporting the management of SARS-CoV-2 multi-pathological patients

The combination of pervasive sensing and multimedia understanding with the advances in communications makes it possible to conceive platforms of services for providing telehealth solutions responding to the current needs of society. The recent outbreak has indeed posed several concerns on the management of patients at home, urging to devise complex pathways to address the Severe Acute Respiratory Syndrome (SARS) in combination with the usual diseases of an increasingly elder population. In this paper, we present TiAssisto, a project aiming to design, develop, and validate an innovative and intelligent platform of services, having as its main objective to assist both Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) multi-pathological patients and healthcare professionals. This is achieved by researching and validating new methods to improve their lives and reduce avoidable hospitalisations. TiAssisto features telehealth and telemedicine solutions to enable high-quality standards treatments based on Information and Communication Technologies (ICT), Artificial Intelligence (AI) and Machine Learning (ML). Three hundred patients are involved in our study: one half using our telehealth platform, while the other half participate as a control group for a correct validation. The developed AI models and the Decision Support System assist General Practitioners (GPs) and other healthcare professionals in order to help them in their diagnosis, by providing suggestions and pointing out possible presence or absence of signs that can be related to pathologies. Deep learning techniques are also used to detect the absence or presence of specific signs in lung ultrasound images. © 2022 IEEE.

COVID-19 and Viral Pneumonia Classification Using Radiomic Features and Deep Learning

The new coronavirus has become the greatest challenge of the 21st century. But since the first cases, much is being discovered about the disease and its effects on the body. Medical imaging, such as X-Rays and CT is widely used to visualize and follow up the patient's clinical picture, especially the effects on the lungs. Although useful, the analysis of this type of image requires some expertise from the radiologist. In less developed countries, the amount of radiologists specialized in chest X-Rays is inadequate, which motivates the development of new technologies to assist clinicians to provide reliable diagnoses. Therefore, this paper addresses the development of a computer-based method to assist in COVID-19 detection among viral pneumonia and health patients through X-Rays images. The proposed method is based on extracting radiomic features and analyzing them using Deep Neural Networks. Experiments following K-Fold Cross-Validation achieved an overall accuracy of 94.98%, a sensibility of 94.89% and an AUC of 99.20%. A benchmark with traditional machine learning algorithms and a binary assessment are also provided. From a multiclass perspective, the analysis and differentiation of COVID-19 and other viral pneumonia reached great results and may assist radiologists in better diagnosing the disease worldwide. © 2022 IEEE.

Convolutional Neural Networks for Detection of COVID-19 from Chest X-Rays

The coronavirus (COVID-19) pandemic was rapid in its outbreak, and the contagion of the virus led to an extensive loss of life globally. This study aims to propose an efficient and reliable means to differentiate between chest x-rays indicating COVID-19 and other lung conditions. The proposed methodology involved combining deep learning techniques such as data augmentation, CLAHE image normalization, and transfer learning with eight pre-trained networks. The highest performing networks for binary, 3-class (normal vs. COVID-19 vs. viral pneumonia) and 4-class classifications (normal vs. COVID-19 vs. lung opacity vs. viral pneumonia) were MobileNetV2, InceptionResNetV2, and MobileNetV2, achieving accuracies of 97.5%, 96.69%, and 92.39%, respectively. These results outperformed many state-of-the-art methods conducted to address the challenges relating to the detection of COVID-19 from chest x-rays. The method proposed can serve as a basis for a computer-aided diagnosis (CAD) system to ensure that patients receive timely and necessary care for their respective illnesses. Copyright © 2022, IGI Global.

A Framework for a Mobile Informative Push Notification and Web Application for the Office of Senior Citizens Affairs City of Manila

An elderly squire indicated the importance of medication intake in everyday life. Some individuals forget to take medicine on time. Also, monitor using the scheduled medicine to help people with an illness. In addition, technology has increased for utilization in healthcare. According to Harvard Medical School by Stephanie Watson about the technology advancement is flourishing in the Philippines, and telehealth stays with innovation to improve digital health platforms. In addition, healthcare services are facing challenges with Covid-19. On the other hand, telehealth gives good service, and about 91% of consumers use digital healthcare services. Also, A reminder system should be simple, familiar, flexible, and recognizable.Technology can positively impact the lives of older people, including their physical and mental health and daily activities. Technology can help people become active. It also increases awareness and motivation to increase physical activity. © 2022 IEEE.

Towards an SDN/NFV based Network Infrastructure for Hospital Information Systems and Healthcare Services

Today's hospitals have become extremely dependent on technology to providing valuable patient services. From collecting as well as administering data of patients to delivering advanced therapies. The network infrastructure of the hospital is charged to provide various solution that are mission critical to address the increasing demands of healthcare services. Software Defined Networking and Network Functions Virtualization will improve infrastructure agility, making it easier to design, deliver and use networking services in a dynamic and scalable environment. Combining SDN and NFV provides significant benefits throughout the infrastructure network. Additionally, external parties are able to use infrastructure services in order to establish new service offerings. The infrastructure services are opened for many parties like developers of applications and service vendors. In this article, we first highlight some of the issues in the hospital information system (HIS) and healthcare services which network infrastructure will eventually confront. Then, concepts of SDN as well as NFV are presented. In addition, description of the benefits that SDN and NFV have for HIS and healthcare services. Finally, a new multi-layer architecture based on SDN and NFV is proposed and how this architecture can deal with the existing challenges of HIS and healthcare services. © 2022 IEEE.

Think About It: Promoting Physical Activity with a Mobile App with a Theory-based Approach

Currently, people’s highly busy lifestyles and sedentary behavior contribute negatively to multiple health factors. During the COVID-19 pandemic, the different sanitary measures, such as limited mobility and the closing of gyms and sports centers, have contributed to limited physical activity. In this context, there are several apps to enhance physical activity across all mobile stores with an emphasis on mobile sensing. However, the use of a formal theory incorporated into the app development and interventions is less evident. A theory-based approach contributes to understanding the reasons and situations in which an intervention strategy can have an impact. The present work considers the Elaboration Likelihood Model (ELM), which addresses persuasion and attitude change. Can we develop a persuasive app that promotes physical activity based on contemporary attitudes and behavioral change theories? We developed a mobile application for Android OS. Then, 63 participants tested it, and were encouraged to think of ideas or arguments in favor of doing physical activity in a high elaboration task. A mediation analysis was done, with results showing that attitudes partially mediate the association between thought and physical activity. Participants’thoughts were seen to be positively correlated with their attitudes;and, in turn, participants’attitudes were correlated with their behavioral intention (to do physical activity). This suggests that a theory-based approach for the active production of biased beliefs is effective when designing an app that encourages positive attitudes toward physical activity. Author

Speech Recognition and Neural Networks based Talking Health Care Bot (THCB): Medibot

The COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (THCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. Therefore, the THCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. The study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. The program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. This study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%. © 2023 IEEE.

Assessing the Benefits of a Teleassessment Solution Using a FVM Perspective

The recent COVID-19 pandemic has served to highlight the benefits of digital health in general and telehealth in particular. One area of telehealth that is particularly important is that of teleassessment. Currently, we are witnessing an exponential growth in total knee and total hip replacements (TKR) (THR) due to an aging population coupled with longer life expectancy which is leading to a high likelihood of an unsustainable burden for healthcare delivery in Australia. To address this imminent challenge, the following proffers a tele-assessment solution, ARIADNE (Assist foR hIp AnD kNEe), that can provide high quality care, with access for all and support for high value outcomes. A fit viability assessment is provided to demonstrate benefits of the proffered solution. © 2022 IEEE Computer Society. All rights reserved.

Telehealth Utilization among Low Income Population during COVID-19: An Analysis of COVID-19 Research Database

The COVID-19 Research Database is a public data platform. This platform is a result of private and public partnerships across industries to facilitate data sharing and promote public health research. We analyzed its linked database and examined claims of 2,850,831 unique persons to investigate the influence of demographic, socio-economic, and behavioral factors on telehealth utilization in the low-income population. Our results suggest that patients who had higher education, income, and full-time employment were more likely to use telehealth. Patients who had unhealthy behaviors such as smoking were less likely to use telehealth. Our findings suggest that interventions to bolster education, employment, and healthy behaviors should be considered to promote the use of telehealth services. © 2023 IEEE Computer Society. All rights reserved.

Investigation of Healthcare Security Using Blockchain Technology: A review

Telemedicine and telehealth care system show the revolutionary and modern way to deal with the coronavirus 2019 pandemic. However, such systems are facing increased security risks. As a result, healthcare providers and academic institutions must be well-informed, safe, and prepared to respond to any cyber-attack. The aim of this paper is to conduct a review of healthcare information systems together with how security can be provided for such systems. The paper main focus is on the adoption of blockchain technology to support the security of the healthcare system. This adoption has been investigated and assessed to show its benefits compared with other conventional technologies. Finally, a recommendation was pointed out for the security of healthcare with the usage of blockchain technology. © 2022 IEEE.

Blockchain Meets Federated Learning in Healthcare: A Systematic Review with Challenges and Opportunities

Recently, innovations in the Internet-of-Medical- Things (IoMT), information and communication technologies, and Machine Learning (ML) have enabled smart healthcare. Pooling medical data into a centralised storage system to train a robust ML model, on the other hand, poses privacy, ownership, and regulatory challenges. Federated Learning (FL) overcomes the prior problems with a centralised aggregator server and a shared global model. However, there are two technical challenges: FL members need to be motivated to contribute their time and effort, and the centralised FL server may not accurately aggregate the global model. Therefore, combining the blockchain and FL can overcome these issues and provide high-level security and privacy for smart healthcare in a decentralised fashion. This study integrates two emerging technologies, blockchain and FL, for healthcare. We describe how blockchain-based FL plays a fundamental role in improving competent healthcare, where edge nodes manage the blockchain to avoid a single point of failure, while IoMT devices employ FL to use dispersed clinical data fully. We discuss the benefits and limitations of combining both technologies based on a content analysis approach. We emphasise three main research streams based on a systematic analysis of blockchain-empowered (i) IoMT, (ii) Electronic Health Records (EHR) and Electronic Medical Records (EMR) management, and (iii) digital healthcare systems (internal consortium/secure alerting). In addition, we present a novel conceptual framework of blockchain-enabled FL for the digital healthcare environment. Finally, we highlight the challenges and future directions of combining blockchain and FL for healthcare applications. IEEE

The Virtual Doctor Is In: The Effect of Telehealth Visits on Patient Experience

COVID-19 has accelerated the adoption of telehealth. With this shift comes a need for empirically based research regarding the effect of telehealth on patient experience. The present study employed an online survey (N = 996) examining whether a patient's perceptions of a telehealth visit predicts (a) the likelihood that they will schedule a future telehealth visit, and (b) their recall of clinical information. Participants viewed a video of a real clinician delivering information on a COVID-19 antibody test, and responded to demographic, socioemotional, and cognitive items. We found that for every 1-point increase in an individual's satisfaction with their interaction with the doctor, they were.73 times more likely to revisit the doctor (p < .01). These results provide insight for researchers and medical professionals regarding patient perceptions of virtual encounters and suggest best practices to consider as we further integrate telehealth. © 2022 IEEE Computer Society. All rights reserved.