Musharraf N. Alruwaill, Saraju P. Mohanty, Elias Kougianos
Individuals with health insurance are protected from financial risk and have access to critical medical treatments. However, traditional healthcare insurance has issues such as complexity, availability, claim processing time, fraudulent claims, and double claims. These problems can lead to issues such as patient funds being held, lengthy claim processing, and fraudulent claims. The claim process is handled manually in order to verify each medical treatment, validate the claim, and ensure that it complies with the coverage policy. Significant advantages of an automated system for healthcare insurance procedures include a reduction in time, increased accuracy, and improved insurance service quality. However, the centralized nature of such systems may present vulnerabilities, such as a single point of failure, loss of transparency, and integrity. The proposed Forti-Ins system integrates blockchain technology, smart contracts, and a distributed file system to facilitate automated claim processing, prevent double claims, increase transparency, reduce administrative costs, and strengthen system robustness, all within a secure framework. Smart contracts facilitate the automated handling of healthcare insurance procedures in a secure manner, while distributed file systems provide cost-effective management of large volumes of files.
The idea of networked personal medical devices is a component of contemporary Smart Health Systems (SHS). These gadgets offer remote observing and the exchange of wellbeing information, which enormously further develop the patient's personal satisfaction while at the same time reducing treatment expenses for both the patient and the medical care suppliers (telemedicine). For individuals' wellbeing, a cutting edge individual wellbeing record framework is fundamental. Here, there are still difficulties with information mix from different EHRs, information interoperability, and guaranteeing that admittance to information is totally under the power of the patient. Some security issues are caused by the Network. To settle these issues, we propose a novel profound learning-based framework that circuits state of the art decentralized innovations like IPFS and blockchain with wellbeing information interoperability principles and advances like FHIR's APIs. In this review, we show that correspondence between private clinical gadgets is as a matter of fact powerless to different cyber attacks. We show how an outer assailant could involve man-in-the-center, replay, bogus information infusion, and refusal of-administration assaults to block delicate wellbeing information stream by capturing the correspondence of the individual clinical gadget. We likewise suggest an Interruption Recognition Framework (IDS), GAN, to additional screen traffic on private clinical gear and spot attacks against them. Our extensive investigation shows that GAN, with an F1-score of 98 % and an accuracy of 98.7 %, can successfully and accurately recognise numerous assaults on personal medical equipment.
Ajay Kumar, Rajiv R. P. Singh, Indranath Chatterjee, Nikita Sharma · 5 authors
Abstract Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges.
Healthcare chatbots are becoming increasingly popular, but with their use comes the issues of excessive personal information collection and privacy leakage. To address this issue, we propose a Healthcare Chatbot-based Privacy Preserving (HCPP) Framework that adopts a data-oriented approach to reduce the excessive disclosure of personal information. HCPP consists of two main components: the Healthcare Chatbot-based Minimized Personal Information (HCMPI) method and the Healthcare Chatbot-based Zero Knowledge Proof (HCZKP) method. HCMPI leverages large language models (LLMs) to minimize the acquisition of unnecessary personal health information without significantly affecting healthcare service. HCZKP further encrypts a part of the minimized information, making the data available but invisible. The experimental evaluation results demonstrate the effectiveness and feasibility of our approach.
Most algorithms deployed in healthcare do not consider gender and sex despite the effect they have on individuals' health differences. Missing these dimensions in healthcare information systems is a point of concern, as neglecting these aspects will inevitably perpetuate existing biases, produce far from optimal results, and may generate diagnosis errors. An often-overlooked community with distinct care values and needs are LGBT+ older adults, which has traditionally been under-surveyed in healthcare and technology design. This paper investigates the implications of missing gender and sex considerations in distributed ledger technologies for LGBT+ older adults. By using the value sensitive design methodology, our contribution shows that many value meanings dear to marginalized communities are not considered in the design of the blockchain, such as LGBT+ older adults' interpretations of trust, privacy, and security. By highlighting the LGBT+ older population values, our contribution alerts us to the potential discriminatory implications of these technologies, which do not consider the gender and sex differences of marginalized, silent populations. Focusing on one community throughout - LGBT+ older adults - we emphasize the need for a holistic, value sensitive design approach for the development of ledger technologies for healthcare, including the values of everyone within the healthcare ecosystem.
The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.
In the context of mental health, safeguarding patient data poses numerous challenges due to the sensitive nature of the field. The psychiatric sector heavily relies on data collection, processing, and sharing, which necessitates a robust and privacy-preserving system. By leveraging the unique features of blockchain, such as decentralization and cryptographic techniques, we establish a secure environment for managing psychiatric data. Our model addresses concerns related to data privacy and unauthorized access by guaranteeing data integrity and confidentiality. The blockchain-based shared case registry model also facilitates seamless collaboration and data sharing among healthcare providers, effectively overcoming interoperability challenges. By securely exchanging patient records, healthcare professionals can ensure continuity of care and gain a comprehensive understanding of a patient’s mental health history. This study investigates the potential of blockchain technology as an enabling solution for effective psychological health data management. Our research proposes a blockchain-based shared case registry model that ensures secure and efficient access to electronic psychiatric records.
Tomas Bueno Momčilović, Matthias Buchinger, Dian Balta
In its 14 years, distributed ledger technology has attracted increasing attention, investments, enthusiasm, and user base. However, ongoing doubts about its usefulness and recent losses of trust in prominent cryptocurrencies have fueled deeply skeptical assessments. Multiple groups attempted to disentangle the technology from the associated hype and controversy by building workflows for rapid prototyping and informed decision-making, but their mostly isolated work leaves users only with fewer unclarities. To bridge the gaps between these contributions, we develop a holistic analytical framework and open-source web tool for making evidence-based decisions. Consisting of three stages - evaluation, elicitation, and design - the framework relies on input from the users' domain knowledge, maps their choices, and provides an output of needed technology bundles. We apply it to an example clinical use case to clarify the directions of our contribution charts for prototyping, hopefully driving the conversation towards ways to enhance further tools and approaches.
Using wearable devices and WEB3.0 to make doctor-patient interaction smarter. The aging of the population and COVID-19 have placed a tremendous workload on the medical field. Traditional monitoring has been centralized, with healthcare professionals providing face-to-face care to all patients. This is where blockchain is utilized, where users themselves have data and monitor each other. Mutual monitoring of medical information on the blockchain creates a mutually supportive relationship where users themselves monitor each other. In this demonstration experiment, the system was introduced to six elderly people. This decentralized system in which users themselves monitor each other is called the Web3 type. The Web type is a technology that is expected to break through the current concentration of data in overseas digital industries and leave digital data resources in Japan. This research is unique in that it combines blockchain technology, which is at the core of this technology, with medical information. A mutual assistance system was constructed to visualize and mutually monitor their exercise habits using wearable terminals. As a result, the exercise habits of all participants improved, and unique information was obtained from the questionnaire survey.
John Robert Bautista, Daniel Toshio Harrell, Ladd Hanson, Eliel Oliveira · 7 authors
Patients' control over how their health information is stored has been an ongoing issue in health informatics. Currently, most patients' health information is stored in centralized but siloed health information systems of healthcare institutions, rarely connected to or interoperable with other institutions outside of their specific health system. This centralized approach to the storage of health information is susceptible to breaches, though it can be mitigated using technology that allows for decentralized access. One promising technology that offers the possibility of decentralization, data protection, and interoperability is blockchain. In 2019, our interdisciplinary team from the University of Texas at Austin's Dell Medical School, School of Information, Department of Electrical and Computer Engineering, and Information Technology Services developed MediLinker-a blockchain-based decentralized health information management platform for patient-centric healthcare. This paper provides an overview of MediLinker and outlines its ongoing and future development and implementation. Overall, this paper contributes insights into the opportunities and challenges in developing and implementing blockchain-based technologies in healthcare.
Lilly Marie Baltruschat, Vikas Jaiman, Visara Urovi
Purpose Blockchain systems have been proposed as a solution for exchanging electronic health records (EHR) because they enable data sharing in decentralised networks. This paper aims to analyse the user acceptability of blockchain technology in enabling EHR exchange and to formulate practical implications for increasing user acceptability. Design/methodology/approach A technology acceptance model [extended Unified Theory of Acceptance and Use of Technology (UTAUT) model] was used as a framework to measure the effects of 13 factors. The authors conducted a survey and analysed data from 214 participants using partial least square path modelling. Findings The acceptance of blockchain for EHR sharing is positively influenced by performance expectancy, social influence and perceived trust. Effort expectancy and facilitating conditions do not influence acceptance. The UTAUT model explains the variance in acceptance at 58.4%. Self-efficacy influences effort expectancy, incentives influence facilitating conditions and security predicts perceived trust. Practical implications Three implications are drawn: (1) Users need to clearly understand system’s purpose, functions, security mechanism and environmental impacts. (2) Users are incentivised to share health data via a blockchain solution if the technology offers personalising options and health information. (3) Health personnel can socially impact patients to use blockchain-based solutions. Originality/value Studies have shown that blockchain technology is a valuable solution for exchanging EHR. The novelty of this work is to identify how and why patients may accept this emerging technology for EHR exchange.
Healthcare is an emerging sector with the integration of emerging technologies aiming to improve Quality of Life of an individual through various medical services. Most of the healthcare services work with sensitive information of patients either collected in real-time using body implanted sensors or through various IoT enabled medical devices during the diagnosis in a centrally controlled model. But, the traditional IoT based medical services suffer from several challenges such as data security, privacy, interoperability, single point of failure, scalability, and data integrity. However, by considering the advantages of Blockchain technology and the disadvantages of IoT systems, the amalgamation of a decentralised, distributed ledger technology with the IoT for various healthcare applications will strengthen the system by resolving the major challenges. Thus, this research article conducts a comprehensive survey on the integration of Blockchain and IoT (BCIoT) for Healthcare services, focusing mainly on existing approaches, possibilities, applications and challenges. First, we present a detailed overview of Blockchain, IoT and the motivation for BCIoT along with the survey on existing healthcare applications. Next we discuss the enabling platforms for BCIoT based healthcare services. For the better understanding, we review the role of BCIoT in Remote patient monitoring, electronic heath record management, Health asset tracing, Covid-19 infected patient contact tracking. Finally challenges and future directions are discussed to improve the Quality of Life of patients through Healthcare applications.
Ahmed M. Alwakeel, Mohammed M. Alwakeel, Mohammed M. Alwakeel, Syed Rameem Zahra · 10 authors
Cities have undergone numerous permanent transformations at times of severe disruption. The Lisbon earthquake of 1755, for example, sparked the development of seismic construction rules. In 1848, when cholera spread through London, the first health law in the United Kingdom was passed. The Chicago fire of 1871 led to stricter building rules, which led to taller skyscrapers that were less likely to catch fire. Along similar lines, the COVID-19 epidemic may have a lasting effect, having pushed the global shift towards greener, more digital, and more inclusive cities. The pandemic highlighted the significance of smart/remote healthcare. Specifically, the elderly delayed seeking medical help for fear of contracting the infection. As a result, remote medical services were seen as a key way to keep healthcare services running smoothly. When it comes to both human and environmental health, cities play a critical role. By concentrating people and resources in a single location, the urban environment generates both health risks and opportunities to improve health. In this manuscript, we have identified the most common mental disorders and their prevalence rates in cities. We have also identified the factors that contribute to the development of mental health issues in urban spaces. Through careful analysis, we have found that multimodal feature fusion is the best method for measuring and analysing multiple signal types in real time. However, when utilizing multimodal signals, the most important issue is how we might combine them; this is an area of burgeoning research interest. To this end, we have highlighted ways to combine multimodal features for detecting and predicting mental issues such as anxiety, mood state recognition, suicidal tendencies, and substance abuse.
S. Geetha, S. Kanakaprabha, A. Devipriya, D. Brindha · 5 authors
Student’s attendance is important factor all the time in educational institution because a single absent is big difference in performance and disciplinary related activities. Making attendance with high accuracy is important factor even though there are various ways to mark student’s attendance in modern era. Face recognition-based attendance tracking system is a popular way nowadays introduced in colleges and schools. There are two problems associated with the automated attendance management system is it requires an administrator to monitor the attendance of students. Generally, it’s very difficult to handle with large number of students. Another problem with the system is record need to be maintained from forgery. Blockchain technology is decentralized management useful for protect sensitive data. The goal of the proposed work is to provide a web-based application with a notification system that allows mentors to keep track of their mentees’ attendance while sitting in their place. The work is divided into two parts: first, automatic face detection and analysis using the CNN model; and second, notification systems and the production of logs to consider. The generated log is maintained in block chain network.
IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fog-enabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%.
Data sharing in the health sector represents a big problem due to privacy and security issues. Health data have tremendous value for organisations and criminals. The European Commission has classified health data as a unique resource owing to their ability to enable both retrospective and prospective research at a low cost. Similarly, the Organisation for Economic Co-operation and Development (OECD) encourages member nations to create and implement health data governance systems that protect individual privacy while allowing data sharing. This paper proposes adopting a blockchain framework to enable the transparent sharing of medical information among health entities in a secure environment. We develop a laboratory-based prototype using a design science research methodology (DSRM). This approach has its roots in the sciences of engineering and artificial intelligence, and its primary goal is to create relevant artefacts that add value to the fields in which they are used. We adopt a patient-centric approach, according to which a patient is the owner of their data and may allow hospitals and health professionals access to their data.
Purpose-Electronic health records (EHRs) have replaced paper medical records due to their convenience, safety, and ability to lessen data duplication. Poor interoperability and unsolved privacy concerns are still difficulties with EHRs, though. Blockchain, a distributed ledger protocol made up of encrypted blocks of data grouped in chains, could be used to address the interoperability and confidentiality issues plaguing EHRs. In this paper, we explain what electronic health records (EHRs) are and how blockchain technology works, and we offer a blockchain-based future that will improve EHR interoperability and privacy. Methodology-There was a total of 424 health care workers from hospitals in the Andhra Pradeshincluded in the study's quantitative analysis, which included descriptive statistics, a t test, and an analysis of variance. Findings-Efficient data management, equitable access, and reliable systems are all topics the study examines. To this aim, we propose the continued need for research in health informatics, data sciences, and ethics in order to implement blockchain-based EHRs. Social implications-Concerns around blockchain's carbon footprint, inequitable access to healthcare, and patient mistrust all need to be addressed in any blockchain-based EHR plan. Originality-With the advent of blockchain technology, the groundwork is being laid for a completely new approach to healthcare. This study contributes to the literature on the use of blockchain technology in future healthcare because there are currently too few case studies examining the intersection of blockchain and contemporary medical practice.
The Metaverse is an online universe that combines virtual reality and augmented reality, linked together via a network.It has generated novel experiences that are fully engaging and transpire in real time, facilitating interpersonal communication and dialogue. Virtual environments with 3D space and avatars can boost patient-facing platforms, operational utilisation, digital education, diagnostics, and treatment choices in medicine and ophthalmology. Globally, there is an increasing prevalence of chronic diseases, with an estimated 25 percent of individuals presently contending with multiple chronic health issues. The management of chronic diseases is currently being rethought in light of the development of technology known together as "Smart Healthcare." A prime example is state-of-the-art wearable technology that incentivizes people to embrace healthier lifestyles through the monitoring of physiological indicators and metabolic processes. With better data organisation and analysis, chronic disease patients may benefit from improved health, privacy, and quality of life. Through the examination of physiological data acquired from wearable devices on a patient, Artificial Intelligence (AI) has the capability to generate informed recommendations pertaining to the diagnosis and treatment of illness. These recommendations can be provided by AI. The adoption of blockchain technology (BC) has the potential to significantly advance healthcare in a variety of ways, including decentralised data sharing, user privacy, user empowerment, and dependability in data administration. The potential impact of Wearable Technologies (WT), Artificial Intelligence (AI), and Blockchain Technology (BC) on Chronic Disease Management (CDM) could be a transition in emphasis from the hospital to the patient. This article provides a patient-centered technical framework for controlling chronic diseases using artificial intelligence, blockchain, and wearable technologies. Our proposed architecture depends on Metaverse environment. In order to participate in the Metaverse, both patients and physicians need to sign up on the Blockchain network. After entering, customers will be accompanied by avatars throughout the experience. A comprehensive record of all information gathered during doctor-patient consultations, including text, videos, images, audio, and clinical data, will be compiled, uploaded to the blockchain, and stored in perpetuity. Explainable Artificial Intelligence (XAI) algorithms examine these particulars in order to diagnose and forecast the progression of diseases. We conclude with a discussion of the constraints of this novel paradigm and recommendations for future research.
RICARDO CARREÑO AGUILERA, ADAN ACOSTA BANDA, Miguel Patiño-Ortiz, Julián Patiño-Ortiz
This paper proposes an innovative method to take advantage of Blockchain Convolutional Neural Networks (BCNNs) in Emotion Recognition (ER). Based on Artificial Intelligence, this proposal uses audio-visual emotion patterns to determine psychiatric profiles to attend to the most urgent as a priority. BCNN architectures were used to identify emergency patterns. The results indicate that the proposed method is adequate for classifying and identifying audio-visual patterns using Deep Learning (DL) with Boltzmann’s restricted machines. It is concluded that it is sufficient to consider the audio-visible critical features from the patient’s face and voice for the proposed model to recognize a psychiatric services emergency for immediate action: the emergency with no control and the Emergency under control. User personal dynamic profiles are stored in the blockchain ecosystem since they are deemed sensitive data. System security is provided by blockchain and authentication uses non-fungible tokens (NFT) technology.
Faced with the impact of the coronavirus disease (COVID-19) pandemic, governments must protect the well-being of the population. Aside from considerations, such as keeping the virus from spreading and treating patients, the government should also be concerned about the mental health of its citizens during the epidemic. This study aimed to help users who develop depression due to COVID-19 on social media, reduce the cost of counselling, and reduce the need for users to visit the hospital for counselling. This study investigated the opportunities for blockchain technology to provide psychological help to social media users suffering from depression caused by the pandemic. Blockchain-based technology has been used to develop a new model that enables a user autonomy system to allow users to control their own data fully. The model utilizes a delegated proof of stake consensus blockchain to manage depression data to enable low cost and information security while discussing aspects related to trust, privacy, interoperability, and integration with other information communication technologies. The blockchain framework has been proven to provide secure and reliable information management that meets the user requirements for information autonomy. The frame-work can be combined with various other information technologies to extend its functionality further.