ABSTRACT
The COVID-19 pandemic has brought in many unique challenges and opportunities for patient care, and one is online healthcare practices. Patient satisfaction with online consultation is primary importance as online healthcare practices are evolving with time. Although previous research has examined how patient satisfaction with online doctor services can be further improved, there has been scant research on the satisfaction with online doctor services concerning Indian patients. Within the framework of service science theories, this study examines satisfaction and sentiments of Indian patients with online doctor services from multiple perspectives. A total of 38019 patient online feedback for 343 doctors was used for understanding patient sentiments. The sentiment analysis classified the reviews of the patients on online doctor consultation services. The finding suggests that healthcare service providers consider a systemic approach that includes core health services along with technical and marketing factors to proactively improve online patient satisfaction.
ABSTRACT
Recently, medical image encryption has attracted many researchers because of security issues in the communication process. The recent COVID-19 has highlighted the fact that medical images are consistently created and disseminated online, leading to a need for protection from unauthorised utilisation. This paper intends to review the various medical image encryption approaches along with their merits and limitations. It includes a survey, a brief introduction, and the most utilised interesting applications of image encryption. Then, the contributions of reviewed approaches are summarised and compared regarding different technical perspectives. Lastly, we highlight the recent challenges along with several directions of potential research that could fill the gaps in these domains for researchers and developers.
ABSTRACT
Automated Patient monitoring is rising to importance in the mobile healthcare services as it makes day-to-day activities risk-free, by continuously monitoring their vital signs. Clinical solutions are being provided to patients in no time, which is made possible due to the latest improvements in the "Internet of Things (IoT), cloud computing, and fog computing”. "Machine learning and Deep learning” are now being extensively used for various applications in healthcare such as extracting relations from vast amounts of patient data, analyzing patterns to predict the propagation of diseases, classify reports and X-rays to detect diseases, to name a few. In this paper, a deep learning-based model is proposed to monitor Covid-affected patients within hospitals. Our model can provide an online link between a patient and medical facility while also collecting patient data. This will enhance the care taken for patients. At the hospital end, we present a deep learning model using ResNet-50 that could classify chest X-rays as Covid positive or No Covid. Through this model we expect to quicken the process of COVID-19 detection while lowering the healthcare expenses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Healthcare is one of the flourishing sectors in each developed and emerging economy. Due to this vast COVID-19 pandemic, the traditional healthcare system cannot provide adequate facilities due to a lack of interactions between doctors and patients. In such conditions, e-healthcare is contributing towards the accelerating growth within the healthcare industry by providing the latest information technology to support information search and communication processes. Besides this, a machine learning algorithm is used to intensify the smartness of the healthcare industry. The five major components of an e-healthcare system are cost-saving, virtual networking, electronic medical record physician-patient relationships and privacy concerns. Our proposed system provides location-based e-prescribing, e-reports, disease prediction, and suggesting treatments and emergency services with a single click, so it is better than another existing system. Copyright © 2023 Inderscience Enterprises Ltd.
ABSTRACT
Recently, the coronavirus pandemic has caused widespread panic around the world. Modern technologies can be used to monitor and control this highly contagious disease. A plausible solution is to equip each patient who is diagnosed with or suspected of having COVID-19 with sensors that can monitor various healthcare and location parameters and report them to the desired facility to control the spread of the disease. However, the simultaneous communication of numerous sensors installed in most of an area's population results in a massive burden on existing Long-Term Evolution (LTE) networks. The existing network becomes oversaturated because it has to manage two more kinds of traffic in addition to regular traffic (text, voice, and video). Healthcare traffic is generated by many sensors deployed over a huge population, and extra traffic is generated by people contacting their family members via video or voice calls. In pandemics, e-healthcare traffic is critical and should not suffer packet loss or latency due to network overload. In this research, we studied the performance of existing networks under various conditions and predicted the severity of network degradation in an emergency scenario. We proposed and evaluated three schemes (doubling bandwidth, combining LTE-A and LTE-M networks, and request queuing) for ensuring the quality of service (QoS) of healthcare sensor (HCS) network traffic without perturbation from routine human-to-human or machine-to-machine communications. We simulated all proposed schemes and compared them with existing network scenarios. Although it is observed from the results that doubling the bandwidth serves the purpose, it is a time-consuming and expensive solution that seems non-practical in case a sudden peak occurs during an emergency. We can conclude by analyzing the simulations that the proposed queuing scheme is best-suitable under all studied scenarios where QoS for HCS traffic is never compromised, which is the ultimate goal of this research.
ABSTRACT
Automated Patient monitoring is rising to importance in the mobile healthcare services as it makes day-to-day activities risk-free, by continuously monitoring their vital signs. Clinical solutions are being provided to patients in no time, which is made possible due to the latest improvements in the “Internet of Things (IoT), cloud computing, and fog computing”. “Machine learning and Deep learning” are now being extensively used for various applications in healthcare such as extracting relations from vast amounts of patient data, analyzing patterns to predict the propagation of diseases, classify reports and X-rays to detect diseases, to name a few. In this paper, a deep learning-based model is proposed to monitor Covid-affected patients within hospitals. Our model can provide an online link between a patient and medical facility while also collecting patient data. This will enhance the care taken for patients. At the hospital end, we present a deep learning model using ResNet-50 that could classify chest X-rays as Covid positive or No Covid. Through this model we expect to quicken the process of COVID-19 detection while lowering the healthcare expenses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Detection of COVID-19 has been a global challenge due to the lack of proper resources across all regions. Recently, research has been conducted for non-invasive testing of COVID-19 using an individual's cough audio as input to deep learning models. However, these methods do not pay sufficient attention to resource and infrastructure constraints for real-life practical deployment and the lack of focus on maintaining user data privacy makes these solutions unsuitable for large-scale use. We propose a resource-efficient CoviFL framework using an AIoMT approach for remote COVID-19 detection while maintaining user data privacy. Federated learning has been used to decentralize the CoviFL CNN model training and test the COVID-19 status of users with an accuracy of 93.01 % on portable AIoMT edge devices. Experiments on real-world datasets suggest that the proposed CoviFL solution is promising for large-scale deployment even in resource and infrastructure-constrained environments making it suitable for remote COVID-19 detection. © 2022 IEEE.
ABSTRACT
This paper introduces SPOT, a Secure and Privacy-preserving prOximity based protocol for e-healthcare systems. It relies on a distributed proxy-based approach to preserve users' privacy and a semi-trusted computing server to ensure data consistency and integrity. The proposed protocol ensures a balance between security, privacy and scalability. As far as we know, in terms of security, SPOT is the first one to prevent malicious users from colluding and generating false positives. In terms of privacy, SPOT supports both anonymity of users being in proximity of infected people and unlinkability of contact information issued by the same user. A concrete construction based on structure-preserving signatures and NIWI proofs is proposed and a detailed security and privacy analysis proves that SPOT is secure under standard assumptions. In terms of scalability, SPOT's procedures and algorithms are implemented to show its efficiency and practical usability with acceptable computation and communication overhead.
ABSTRACT
The recent development of information technology has changed how individuals access services via online public transportation, e-marketplace, e-commerce, as well as ehealthcare. As the COVID-19 pandemic forces the customers to minimize contact with the service providers, they begin to use e-healthcare for their daily needs, medical consultations, online medicine purchases, even COVID-19 test appointments can be arranged using this application. The purpose of this study is to examine the main drivers of customer intention to use mobile health. The study applies the Extended Technology Acceptance Model. The study also adds external variables that are affecting customer's intention to use were added to the model. The data was collected through online surveys, consisting of two hundred respondents of mobile health users. The data was analyzed using structural equation models, the result shows that customer's intention to use e-healthcare application is heavily influenced by perceived usefulness and perceived ease of use, as well as subjective norm, health consciousness, and communication effectiveness. Furthermore, this study also offers tangible recommendations of improvement for current mobile healthcare companies. © 2021 IEEE.
ABSTRACT
Healthcare is the most crucial sector in people’s life. Many applications and systems have been proposed to improve the healthcare area. The outbreak of the novel coronavirus Covid19 turns more focus on healthcare applications. To manage medical data, healthcare professionals in developed countries have adopted several electronic healthcare information systems and technologies in recent years. However, these technologies show serious privacy risks and security issues, especially in the transfer of data and the recording of data transactions. Furthermore, the high cost of these technologies acquisition, as well as the complexity of their management, make their application in underdeveloped nations extremely problematic. This article proposes a solution based on a decentralized Blockchain architecture to reinforce the security of health information systems. This solution is particularly recommended for developing countries which lack high-tech infrastructures and suffer from poor interoperability between existing information systems. Various researches and works that implement blockchainbased solutions in the security of electronic health information systems (eHIS) are discussed in this article. A new approach based on a hyperledger fabric, implementing smart contracts and several other components is proposed. The suggested architecture involves many actors who can interact with medical records such as patients, doctors, pharmacists, laboratories and insurance companies. Data privacy is guaranteed because there is minimal risk of unauthorized access entities, and by design, the smart contract is the sole way to manipulate participant data. Various optimization and measurement experiments were carried on. The results covering various key parameters of system performance such as throughput, latency, CPU usage, memory consumption and network usage are presented © 2022,IAENG International Journal of Computer Science.All Rights Reserved
ABSTRACT
The modernization of advanced healthcare infrastructure in the early 21st century is still failing to cope up as the whole world is struggling to get rid of the deadly disease named COVID-19. The scarcity of clinical resources is one of the most fundamental as well as critical reasons behind this calamity. The entire healthcare system faces severe challenges to re-stabilize the system. Digitization of technology which is primarily driven by the next-generation communication networks has given an exclusive paradigm shift to resolving the issues. 5th generation of mobile communication-(5G) introduces classical techniques which play crucial roles in e-healthcare transformations. Software-Defined Networking(SDN), Network Function Virtualization (NFV), Network Slicing (NS), and the concept of programmable networks introduce URLLC communication and time-critical service delivery in a resource-restricted environment. Leveraging the concept of programmable slicing approach, in this work, we have framed a flexible e-healthcare slicing model for dedicated and optimized resource provisioning. We have introduced a vSDN server that can significantly balance the healthcare slice and classify the complex medical databases into simplified segments for quick data processing, management, and orchestration. Considering the reformation of global healthcare customization, our proposed approach will play a vital role in the field of the e-healthcare domain.
ABSTRACT
In the present scenario of globalization, technology advancement has changed the concept of data sharing drastically giving it an altogether new dimension. Availability of data online is susceptible to misuse if not secured. Hence, content protection has become a major issue and is a challenging task for researcher to attain a balance between availability and protection. Keeping in view this challenge, the paper aims at providing an insight into the emerging blockchain technology and its application in various sectors such as finance, data management, commercial and particularly healthcare. This paper further highlights COVID-19 pandemic disease management using blockchain technology with a brief discussion of the solution to resist coronavirus infection and its major challenges with solutions in implementing inherent key features of the blockchain. © 2022 IEEE.
ABSTRACT
The adverse impacts of using conventional batteries in the Internet of Things (IoT) devices, such as cost-effective maintenance, numerous battery replacements, and environmental hazards, have led to an interest in integrating energy harvesting technology into IoT devices to extend their lifetime and sustainably effectively. However, this requires improvements in different IoT protocol stack layers, especially in the MAC layer, due to its high level of energy consumption. These improvements are essential in critical applications such as IoT medical devices. In this paper, we simulated a dense solar-based energy harvesting Wi-Fi network in an e-Health environment, introducing a new algorithm for energy consumption mitigation while maintaining the required Quality of Service (QoS) for e-Health. In compliance with the upcoming Wi-Fi amendment 802.11be, the Access Point (AP) coordination-based optimization technique is proposed, where an AP can request dynamic resource rescheduling along with its nearby APs, to reduce the network energy consumption through adjustments within the standard MAC protocol. This paper shows that the proposed algorithm, alongside using solar energy harvesting technology, increases the energy efficiency by more than 40% while maintaining the e-Health QoS requirements. We believe this research will open new opportunities in IoT energy harvesting integration, especially in QoS-restricted environments.
ABSTRACT
The eruption of chronic diseases such as COVID-19 has re-emphasized the need for people all over the world to have access to urgent healthcare. The latest pandemic has shown the flaws in the conventional healthcare system, namely that hospitals and clinics alone are incapable of dealing with such a crisis. Smart and integrated wearables are one of the main technologies that favor modern healthcare solutions (Godi, B., Viswanadham, S., Muttipati, A.S., Prakash Samantray, O., Gadiraju student, S.R.: E-Healthcare Monitoring System using IoT with Machine Learning Approaches. In: 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). pp. 1–5. IEEE, Gunupur, India (2020). https://doi.org/10.1109/ICCSEA49143.2020.9132937 ). These wearables can now gather data on an unparalleled scale thanks to advancements in the Internet of Things (IoT). Healthcare is among many fields that have been transformed by IoT, with the Internet of Medical Things (IoMT) systems being introduced as an IoT branch. Patients with chronic diseases can be monitored remotely using IoMT systems. As a result, it can offer prompt diagnostics to patients, potentially saving their lives in the event of an emergency. However, protection in these vital systems is a major issue that has arisen as a result of their widespread use. This paper presents an overview of the technologies that are being used on IoMT as well as some security problems found in the literature. This survey will give an insight to the readers regarding the importance of security in healthcare and the different machine learning methods used to address that issue. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Hospital e-healthcare management is one of the important and challenging application domains of Internet of Things (IoT). During the pandemic period of Covid-19, government advices the people through media, only come to the hospital if any urgency and to take the opportunities of e-service from the hospital to control the infection. Internet plays an important role during this crucial period. The important problems are the network problem and effective way to handle e-healthcare service. Efficient management of e-healthcare possible by using IoT based 5G mobile technology. The latest technology improves the quality of e-healthcare service and efficient management of the application. Healthcare management depends on patient's satisfaction, service quality and customer experience etc. In this paper we proposed a model on Patient Relationship Management (PRM) which improves the quality of e-healthcare facilities by using the new technologies like RFID, IoT and 5G. Comparisons are shown between 3G/4G ICT based system and 5G ICT-RFID-IoT enabled system. The PRM parameters cost, accuracy and satisfaction are taken into consideration and how these parameters significantly perform better in healthcare sector with the advent of newer technologies in is main the focus of the paper. © 2021 IEEE.
ABSTRACT
The recent years have whiteness the substandard situations of the modern healthcare system due to a fatal pandemic called COVID19. The rapid advancements of modern technology have disseminated the superficial benefits of medical infrastructure, but significant improvements are still extremely necessary over the massive e-healthcare system (mHS). Considering the fact of limited resources and unlimited demands, a highly stable end-to-end optimization model is required. Healthcare also struggles with real-time communication. The next-generation communication networks (e.g 5G and beyond) proficiently influence the network resource distribution for URLLC. In this work, we have envisioned a novel on-demand e-Healthcare dynamic network slice architecture that uses the ML algorithms at the edge server for real-time classification and access of the offloaded data from the central controller (vSDN-Control layer to Data plane layer). The comparative analysis over the datasets of patients consisting of special index parameters shows that our proposed model allows the end-user more efficient data accessibility over the conventional approaches. We have studied the model over the multi-classification ML models (kNN, DT and RF) and we have found an average improvement of 10% to 15% of average data offloading time efficiency from the local machines from the edge servers. This approach can be further extended as the QoS improvement of the healthcare data traffic over the dynamic network slice instances. We have kept the model simple but standard in nature. © 2022 IEEE.
ABSTRACT
In the current difficult time of COVID-19 pandemic, social distancing is a common practice adopted by healthy and infected people all over the world. Because of this, electronic healthcare and Telemedicine are coming up a big way. One of the most vital challenges in E-healthcare or telemedicine is the authentication and secure transmission of medical images received by an expert(doctor) at a far-off location from the sender(patient). To address the critical authentication issue, this paper proposes a blind pixel-based self-embedding fragile watermarking approach that is effective enough to localize the tampered region with more than 90% accuracy. The self-embedding approach plays a significant role in fragile watermarking for detecting the tampered region at the receiver side. Most of the self-embedding-based fragile watermarking approaches available in the literature are block-based which has a high False Positive Rate(FPR). In the proposed approach, every pixel of the image dynamically generates eight self-mutating offsprings and gets associated with them tightly at bit level. After a systematic set of procedures on that offsprings with a triple layer of security, a single authentication bit is selected using 16 × 1 multiplexer for each pixel intensity of the image. That’s why the proposed approach has a high imperceptibility level (more than 60 dB PSNR). Experimental results confirm the efficacy of the proposed approach in terms of imperceptibility and tamper detection rate.
ABSTRACT
With the incorporation of Internet of Things (IoT) in healthcare systems immense new possibilities have emerged in the modern healthcare services. In recent times where people around the globe are suffering from the Covid-19 pandemic, providing remote healthcare services maintaining necessary social distancing through e-Healthcare has become an urgent priority. e-Healthcare services like patient's real-time health monitoring, exchange of electronic health reports, tele-consultation, remote surgery etc. require reliable and timely delivery of data and responses. To provide for such critical requirements it is essential to prioritize the forwarding of different types of data varies based on their throughput requirements and the delay sensitivities. In this paper, we study the network Quality of Service (QoS) control mechanism for e-Health services using the Software-Defined Networking (SDN) approach in a Fog based Healthcare Environment. We distinguish the data generated from various types of devices into priority classes looking at their priorities, throughput and privacy requirements in the application. Hence, we propose, OpenHealthQ, an OpenFlow based traffic shaping model using OpenFlow Queues to handle the healthcare data. OpenHealthQ provides a secure, on-demand and low-cost access to Healthcare 4.0's most demanding computing infrastructure in a distributed cloud architecture with SDN based fog nodes at the network's edge. Experimental studies show that OpenHealthQ is capable of reducing response time of the end host and significant increase in network throughput compared to the Best-Effort (BE) approach.