The role of blockchain technology in telehealth and telemedicine

Responsible CVD screening with a blockchain assisted chatbot powered by explainable AI

Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intelligence (AI) and blockchain are emerging approaches that may be integrated into the healthcare sector to help responsible and secure decision-making in dealing with CVD concerns. Secure CVD information is needed while dealing with confidential patient healthcare data, especially with a decentralized blockchain technology (BCT) system that requires strong encryption. However, AI and blockchain-empowered approaches could make people trust the healthcare sector, mainly in diagnosing areas like cardiovascular care. This research proposed an explainable AI (XAI) approach entangled with BCT that enhances healthcare interpretability and responsibility to cardiovascular health medical experts. XAI is significant in addressing cardiovascular prediction issues and offers potential solutions for complex communication and decision-making in cardiovascular care. The proposed approach performs better, with the highest accuracy of 97.12% compared to earlier methods. This achievement shows its ability to tackle complex issues, accessible during healthcare sector communication and decision processes.

Telehealth vs face-to-face consultations in a urological oncology clinic: a randomised controlled trial

OBJECTIVES

To determine the effectiveness of telehealth, among patients reviewed for urological oncological diseases, compared with standard face-to-face (F2F) consultations with regard to patient-reported satisfaction through use of a validated questionnaire.

MATERIALS AND METHODS

We conducted a single-centre randomised controlled trial in 126 patients recruited from the Crown Princess Mary Cancer Centres urological oncology clinics. Patients were randomised to either a telehealth audio-only (telephone) consultation group or a standard F2F consultation group for their next routine appointment. Validated questionnaires, using a 4-point Likert index scale, were completed at the end of the appointments. Questionnaire scores were analysed using the Mann-Whitney U-test. The primary outcome measured was patients' preference for type of consultation on subsequent follow-up. Secondary outcomes involved evaluation of efficiency, quality of care, ease of understanding, overall satisfaction, professionalism, limitations, and convenience.

RESULTS

A total of 63 patients in the F2F group and 53 patients in the telephone group completed and returned the validated questionnaires. Patients' preference for next follow-up was statistically significant, favouring the telephone group (P = 0.012). For the secondary outcomes, patients in the telephone group also reported greater satisfaction with regard to the efficiency and timing of the consultation (P = 0.005). Conversely, patients in the F2F group reported higher rates of satisfaction with regard to 'tests and follow-up' and the clinician's ability to deal with their issues, as compared to the telephone group (P = 0.002). Also, patients in the F2F group reported higher rates of perceived quality of consultation (P = 0.027).

CONCLUSION

Our study demonstrates that patients with urological oncological diseases attending routine follow-up generally prefer telehealth over F2F appointments. However, patients in the F2F group perceived that there was a higher quality of care in their consultation, and that the services provided were superior, in comparison to patients in the telehealth group. These are barriers to the widespread adoption of telehealth in urological oncology follow-up care and are rarely reported in the current literature.

Fintech and healthtech synergism: Pioneering a digital healthcare revolution for accessible, affordable, and effective patient-centered care

The blending of financial technology (fintech) and health technology (healthtech) has the potential to bring about major changes in the healthcare industry. In this review, we have delved into this convergence and explored how it can improve healthcare access, affordability, and patient outcomes. Beginning with a delineation of fintech and healthtech, we analyzed the impact of financial innovations such as mobile payment systems, crowdfunding platforms, and blockchain technology on healthcare. Additionally, we examined the rising trends in healthtech integration, such as telemedicine, wearable devices, and health data analytics, and how these technologies intersect with financial systems to enhance healthcare delivery. By showcasing real-life examples and case studies, the study demonstrated how fintech and healthtech solutions have been successfully implemented in healthcare settings. But why doesn't everyone take them up? Regulatory complexities along with ethical considerations are two of the main barriers toward large-scale adoption of these solutions, besides technological limitations. It is crucial to meet these challenges if financial and healthcare solutions are going to be successfully combined through continued innovation.

MetaRehabVerse: The Great Opportunity to Put the Person's Functioning and Participation at the Center of Healthcare

BACKGROUND AND OBJECTIVE

The metaverse refers to a digital realm accessible via internet connections using virtual reality and augmented reality glasses for promoting a new era of social rehabilitation. It represents the next-generation mobile computing platform expected to see widespread utilization in the future. In the context of rehabilitation, the metaverse is envisioned as a novel approach to enhance the treatment of human functioning exploiting the "synchronized brains" potential exacerbated by social interactions in virtual scenarios.

RESULTS

The metaverse emerges as an ideal domain for adapting the principles of the-International Classification of Functioning. Its intrinsic capacity to simulate interactions within virtual environments shared by multi-users, while providing a profound sense of presence and comprehensive perception, should facilitate learning and experiential understanding. Technical and conceptual aspects are currently under definition, including the interplay with artificial intelligence, definition of social metrics performance, and the utilization of blockchain technology for economic purposes.

CONCLUSION

Building upon these foundations, this paper explores potential areas of metaverse applications in rehabilitation and examines how they may facilitate the pillars outlined in the World Health Organization's Rehabilitation 2030 call for action.

Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review

Introduction

Given the increasing number of artificial intelligence and machine learning (AI/ML) tools in healthcare, we aimed to gain an understanding of consumer perspectives on the use of AI/ML tools for healthcare diagnostics.

Methods

We conducted a qualitative systematic review, following established standardized methods, of the existing literature indexed in the following databases up to 4 April 2022: OVID MEDLINE, OVID EMBASE, Scopus and Web of Science.

Results

Fourteen studies were identified as appropriate for inclusion in the meta-synthesis and systematic review. Most studies (n = 12) were conducted in high-income countries, with data extracted from both mixed methods (42.9%) and qualitative (57.1%) studies. The meta-synthesis identified four overarching themes across the included studies: (1) Trust, fear, and uncertainty; (2) Data privacy and ML governance; (3) Impact on healthcare delivery and access; and (4) Consumers want to be engaged.

Conclusion

The current evidence demonstrates consumers' understandings of AI/ML for medical diagnosis are complex. Consumers express a complex combination of both hesitancy and support towards AI/ML in healthcare diagnosis. Importantly, their views of the use of AI/ML in medical diagnosis are influenced by the perceived trustworthiness of their healthcare providers who use these AI/ML tools. Consumers recognize the potential for AI/ML tools to improve diagnostic accuracy, efficiency and access, and express a strong interest to be engaged in the development and implementation process of AI/ML into routine healthcare.

Privacy preserving technology in ophthalmology

PURPOSE OF REVIEW

Patient privacy protection is a critical focus in medical practice. Advances over the past decade in big data have led to the digitization of medical records, making medical data increasingly accessible through frequent data sharing and online communication. Periocular features, iris, and fundus images all contain biometric characteristics of patients, making privacy protection in ophthalmology particularly important. Consequently, privacy-preserving technologies have emerged, and are reviewed in this study.

RECENT FINDINGS

Recent findings indicate that general medical privacy-preserving technologies, such as federated learning and blockchain, have been gradually applied in ophthalmology. However, the exploration of privacy protection techniques of specific ophthalmic examinations, like digital mask, is still limited. Moreover, we have observed advancements in addressing ophthalmic ethical issues related to privacy protection in the era of big data, such as algorithm fairness and explainability.

SUMMARY

Future privacy protection for ophthalmic patients still faces challenges and requires improved strategies. Progress in privacy protection technology for ophthalmology will continue to promote a better healthcare environment and patient experience, as well as more effective data sharing and scientific research.

Designing A Blockchain-Empowered Telehealth Artifact for Decentralized Identity Management and Trustworthy Communication: Interdisciplinary Approach

BACKGROUND

Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks.

OBJECTIVE

The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance.

METHODS

We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype implementation. We used the design science research methodology to implement our overall design. We innovated over existing telehealth platforms with blockchain integration that improves health care delivery services in terms of security, privacy, and efficiency. We adopted a user-centric design approach and started with user requirement collection, followed by system functionality development. Overall system implementation facilitates user requirements, thus promoting user behavior for the adoption of the telehealth platform with decentralized identity management and an access control mechanism.

RESULTS

Our investigation identified key challenges to identity management and trustworthy communication processes in telehealth platforms used in the current health care domain. By adopting distributed ledger technology, we proposed a decentralized telehealth platform to support identity management and a trustworthy communication process. Our design and prototype implementation using a smart contract-driven telehealth platform to provide decentralized identity management and trustworthy communication with token-based access control addressed several security challenges. This was substantiated by testing with 10,000 simulated transactions across 5 peers in the Rahasak blockchain network. The proposed design provides resistance to common attacks while maintaining a linear time overhead, demonstrating improved security and efficiency in telehealth services. We evaluated the performance in terms of transaction throughput, smart contract execution time, and block generation time. To create a block with 10,000 transactions, it takes 8 seconds on average, which is an acceptable overhead for blockchain-based applications.

CONCLUSIONS

We identified technical limitations in current telehealth platforms. We presented several design innovations using blockchain to prototype a system. We also presented the implementation details of a unique distributed architecture for a trustworthy communication system. We illustrated how this design can overcome privacy, security, and scalability limitations. Moreover, we illustrated how improving these factors sets the stage for improving and standardizing the application and for the wide adoption of blockchain-enabled telehealth platforms.

A cross domain access control model for medical consortium based on DBSCAN and penalty function

BACKGROUND

Graded diagnosis and treatment, referral, and expert consultations between medical institutions all require cross domain access to patient medical information to support doctors' treatment decisions, leading to an increase in cross domain access among various medical institutions within the medical consortium. However, patient medical information is sensitive and private, and it is essential to control doctors' cross domain access to reduce the risk of leakage. Access control is a continuous and long-term process, and it first requires verification of the legitimacy of user identities, while utilizing control policies for selection and management. After verifying user identity and access permissions, it is also necessary to monitor unauthorized operations. Therefore, the content of access control includes authentication, implementation of control policies, and security auditing. Unlike the existing focus on authentication and control strategy implementation in access control, this article focuses on the control based on access log security auditing for doctors who have obtained authorization to access medical resources. This paper designs a blockchain based doctor intelligent cross domain access log recording system, which is used to record, query and analyze the cross domain access behavior of doctors after authorization. Through DBSCAN clustering analysis of doctors' cross domain access logs, we find the abnormal phenomenon of cross domain access, and build a penalty function to dynamically control doctors' cross domain access process, so as to reduce the risk of Data breach. Finally, through comparative analysis and experiments, it is shown that the proposed cross domain access control model for medical consortia based on DBSCAN and penalty function has good control effect on the cross domain access behavior of doctors in various medical institutions of the medical consortia, and has certain feasibility for the cross domain access control of doctors.

Large language models and generative AI in telehealth: a responsible use lens

OBJECTIVE

This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI.

MATERIALS AND METHODS

Following the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions.

RESULTS

Twenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration.

CONCLUSION

The findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI.

How Expectations and Trust in Telemedicine Contribute to Older Adults' Sense of Control: An Empirical Study

As numerous nations transition into digital and aging societies, the digital divide has emerged as a significant impediment to older adults' autonomous engagement in the digital society. Enhancing the well-being of elderly individuals through remote medical technology represents a prevailing and prospective trend. Nevertheless, remote medical technology extends beyond the realm of healthcare, offering promise for narrowing the digital divide through the deployment of digital devices and provision of intergenerational support. Therefore, this study investigates the role of trust and expectations in the use of telemedicine, indicating potential pathways for how these products can improve older adults' daily living abilities. Through the construction of a theoretical model, we collected the relevant data of 661 elderly people who use telemedicine technology in China and analyzed the data with SmartPLS4 to obtain the research results. The study discovered that, among older people using telemedicine technology, (1) healthcare expectations promote the breadth of telemedicine product use; (2) trust in product safety increases the depth of telemedicine product use; (3) trust in the service provider promotes the breadth of telemedicine product use; and (4) when compared to the depth of product use, the breadth of telemedicine product use increases older adults' sense of control over their digital lives. The findings provide new empirical data to support growing beliefs about how expectations and trust can increase a sense of control over one's life. They also provide practical contributions on how to boost older adults' usage of telemedicine products, promote their digital literacy and competency, and enhance their sense of control over their digital lives.

The role of blockchain to secure internet of medical things

This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain's transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain's consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.

The Use of Blockchain Technology in Public Health: Lessons Learned

Blockchain is a new technology utilized to develop creative solutions in different industries, such as health care. Blockchain is a decentralized and distributed encrypted system made up of interconnected blocks containing transaction-related information that can be shared with network participants. A blockchain network is utilized in the healthcare industry to safeguard and share patient information among hospitals, pharmacies, and doctors' diagnostic labs. Blockchain applications can precisely identify serious and potentially harmful mistakes within the medical sector. The objective is to comprehensively explore the potential use, present implementations, challenges, and future possibilities of blockchain in health management systems, and to provide information to researchers, policymakers, and practitioners on how to utilize new technology to enhance data security, efficiency, decentralization of data, authenticity of data, transparency, and verifiability of data compared to conventional databases in health management systems. Key review findings for blockchain technology in public health surveillance might include enhanced data security and accessibility of data, data storage and sharing, ensuring tamper-proof records are accessed, empowering patients, and improving overall healthcare outcomes. Its immutability proves to be important for securing healthcare data. It offers a safeguard for health records and clinical trial outcomes and ensures compliance with regulatory standards. This evaluation focuses on how it has transformed data protection, improved workflows, and safe health information interchange. Despite obstacles, further study and standardization initiatives have the potential to transform health care and guarantee patient care that is resilient and trustworthy. In the present healthcare industry, blockchain technology plays an essential role in healthcare systems. It can lead to computerized processes for collecting and validating data, accurate information collected from multiple sources, and data that are fixed, transparent to misuse, and secure, with a reduced risk of digital crimes. In addition, the study provides a detailed analysis of the potential applications for including the use of blockchain technology in transforming public health surveillance.

Blockchain technology in Cardiovascular Medicine: a glance to the future? Results from a social media survey and future perspectives

The leverage of digital facilities in medicine for disease diagnosis, monitoring, and medical history recording has become increasingly pivotal. However, the advancement of these technologies poses a significant challenge regarding data privacy, given the highly sensitive nature of medical information. In this context, the application of Blockchain technology, a digital system where information is stored in blocks and each block is linked to the one before, has the potential to enhance existing technologies through its exceptional security and transparency. This paradigm is of particular importance in cardiovascular medicine, where the prevalence of chronic conditions leads to the need for secure remote monitoring, secure data storage and secure medical history updating. Indeed, digital support for chronic cardiovascular pathologies is getting more and more crucial. This paper lays its rationale in three primary aims: 1) to scrutinize the existing literature for tangible applications of blockchain technology in the field of cardiology; 2) to report results from a survey aimed at gauging the reception of blockchain technology within the cardiovascular community, conducted on social media; 3) to conceptualize a web application tailored specifically to cardiovascular care based on blockchain technology. We believe that Blockchain technology may contribute to a breakthrough in healthcare digitalization, especially in the field of cardiology; in this context, we hope that the present work may be inspiring for physicians and healthcare stakeholders.

Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care

The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.

The Hyperledger fabric as a Blockchain framework preserves the security of electronic health records

The Hyperledger Fabric (HF) framework is widely studied for securing electronic health records (EHRs) in the healthcare sector. Despite the various cross-domain blockchain technology (BCT) applications, little is known about the role of the HF framework in healthcare. The purpose of the systematic literature review (SLR) is to review the existing literature on the HF framework and its applications in healthcare. This SLR includes literature published between January 2015 and March 2023 in the ACM digital library, IEEE Xplore, SCOPUS, Springer, PubMed, and Google Scholar databases. Following the inclusion and exclusion criteria, a total of 57 articles emerged as eligible for this SLR. The HF framework was found to be useful in securing health records coming from the Internet of Medical Things (IoMT) and many other devices. The main causes behind using the HF framework were identified as privacy and security, integrity, traceability, and availability of health records. Additionally, storage issues with transactional data over the blockchain are reduced by the use of the HF framework. This SLR also highlights potential future research trends to ensure the high-level security of health records.

Governance of a Blockchain-Enabled IoT Ecosystem: A Variable Geometry Approach

The proliferation of Internet of Things (IoT) applications is rapidly expanding, generating increased interest in the incorporation of blockchain technology within the IoT ecosystem. IoT applications enhance the efficiency of our daily lives, and when blockchain is integrated into the IoT ecosystem (commonly referred to as a blockchain-IoT system), it introduces crucial elements, like security, transparency, trust, and privacy, into IoT applications. Notably, potential domains where blockchain can empower IoT applications include smart logistics, smart health, and smart cities. However, a significant obstacle hindering the widespread adoption of blockchain-IoT systems in mainstream applications is the absence of a dedicated governance framework. In the absence of proper regulations and due to the inherently cryptic nature of blockchain technology, it can be exploited for nefarious purposes, such as ransomware, money laundering, fraud, and more. Furthermore, both blockchain and the IoT are relatively new technologies, and the absence of well-defined governance structures can erode confidence in their use. Consequently, to fully harness the potential of integrating blockchain-IoT systems and ensure responsible utilization, governance plays a pivotal role. The implementation of appropriate regulations and standardization is imperative to leverage the innovative features of blockchain-IoT systems and prevent misuse for malicious activities. This research focuses on elucidating the significance of blockchain within governance mechanisms, explores governance tailored to blockchain, and proposes a robust governance framework for the blockchain-enabled IoT ecosystem. Additionally, the practical application of our governance framework is showcased through a case study in the realm of smart logistics. We anticipate that our proposed governance framework will not only facilitate but also promote the integration of blockchain and the IoT in various application domains, fostering a more secure and trustworthy IoT landscape.

Applications and Challenges of Telemedicine: Privacy-Preservation as a Case Study

Today, technology has an important impact on the development of medical services, especially during the outbreak of COVID-19. Telemedicine, known by terms such as telehealth and digital health, refers to the utilization of technology to provide health care services at a distance that leads to improved monitoring, detecting and treatment of disease, and provision of individual care. It has been considered in various fields such as radiology, cardiology, pulmonology, psychiatry, emergency care and surgery. The most important advantages of using telemedicine are saving time for the doctor and the patient, reducing the cost of multiple visits to the doctor, reducing the spread of contagious diseases and caring for patients who cannot see a doctor, such as the elderly. In this paper, we review the research in the field of applying telemedicine, as well as its advantages and disadvantages. Next, we discuss the challenges in the field of using telemedicine which are privacy preserving, data security, cost of infrastructures, lack of physical examination and responsibility for patients' compensation. One of the most important challenges is privacy preserving of patients' information during transmission and process. We categorize and compare the various methods that have been proposed to protect peoples' privacy.

Blockchain Revolutionizing in Emergency Medicine: A Scoping Review of Patient Journey through the ED

BACKGROUND

Blockchain technology has revolutionized the healthcare sector, including emergency medicine, by integrating AI, machine learning, and big data, thereby transforming traditional healthcare practices. The increasing utilization and accumulation of personal health data also raises concerns about security and privacy, particularly within emergency medical settings.

METHOD

Our review focused on articles published in databases such as Web of Science, PubMed, and Medline, discussing the revolutionary impact of blockchain technology within the context of the patient journey through the ED.

RESULTS

A total of 33 publications met our inclusion criteria. The findings emphasize that blockchain technology primarily finds its applications in data sharing and documentation. The pre-hospital and post-discharge applications stand out as distinctive features compared to other disciplines. Among various platforms, Ethereum and Hyperledger Fabric emerge as the most frequently utilized options, while Proof of Work (PoW) and Proof of Authority (PoA) stand out as the most commonly employed consensus algorithms in this emergency care domain. The ED journey map and two scenarios are presented, exemplifying the most distinctive applications of emergency medicine, and illustrating the potential of blockchain. Challenges such as interoperability, scalability, security, access control, and cost could potentially arise in emergency medical contexts, depending on the specific scenarios.

CONCLUSION

Our study examines the ongoing research on blockchain technology, highlighting its current influence and potential future advancements in optimizing emergency medical services. This approach empowers frontline medical professionals to validate their practices and recognize the transformative potential of blockchain in emergency medical care, ultimately benefiting both patients and healthcare providers.

Provenance Data Management in Health Information Systems: A Systematic Literature Review

AIMS

This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results.

METHOD

An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied.

RESULTS

Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs.

CONCLUSION

It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.

Blockchain Technology

To encourage industry discussion of cutting-edge blockchain applications, this chapter was written. This chapter provides an introduction to blockchain technology as well as details on how to secure transactions using technology and how the secure element works. It examines common implementation worries and provides use cases for current commercial or trial projects. The following paragraphs go over the concept of a blockchain, how bitcoin transactions operate, and how to finish a transaction. The network of Marco Polo will be highlighted. In this chapter, the authors show how to use blockchain and cloud technology with Marco Polo to remove corporate pain problems. The chapter goes on to give a full overview of blockchain, including its function, traits, and applications.

Sharing Health Information Using a Blockchain

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.

AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions

Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.

Blockchain for Patient Safety: Use Cases, Opportunities and Open Challenges

Medical errors are recognized as major threats to patient safety worldwide. Lack of streamlined communication and an inability to share and exchange data are among the contributory factors affecting patient safety. To address these challenges, blockchain can be utilized to ensure a secure, transparent and decentralized data exchange among stakeholders. In this study, we discuss six use cases that can benefit from blockchain to gain operational effectiveness and efficiency in the patient safety context. The role of stakeholders, system requirements, opportunities and challenges are discussed in each use case in detail. Connecting stakeholders and data in complex healthcare systems, blockchain has the potential to provide an accountable and collaborative milieu for the delivery of safe care. By reviewing the potential of blockchain in six use cases, we suggest that blockchain provides several benefits, such as an immutable and transparent structure and decentralized architecture, which may help transform health care and enhance patient safety. While blockchain offers remarkable opportunities, it also presents open challenges in the form of trust, privacy, scalability and governance. Future research may benefit from including additional use cases and developing smart contracts to present a more comprehensive view on potential contributions and challenges to explore the feasibility of blockchain-based solutions in the patient safety context.

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

Integrating Lean Six Sigma with blockchain technology for quality management – a scoping review of current trends and future prospects

Purpose

The purpose of this research is to study and analyze the literature that integrates Lean Six Sigma (LSS) approach with blockchain technology in different sectors for improved quality management.

Design/methodology/approach

This study presents a scoping review on the application of integrated LSS and blockchain technology in the manufacturing and healthcare sector. Further, the authors examined existing blockchain-based solutions on a variety of dimensions, including application area, technical approach, methodology, application scenario, various blockchain platforms, purpose, and monitoring parameters. The authors study LSS approaches in detail, as well as the key benefits that blockchain technology can enable. Finally, the authors discuss significant research problems to be addressed in order to develop a highly efficient, resilient, and secure quality management framework using blockchain technology.

Findings

It has been observed that the adoption of blockchain technology for quality management and assurance is influenced by several factors such as transaction execution speed, throughput, latency. Also, prior blockchain-based solutions have neglected to leverage the benefits of LSS methodologies for effective quality management.

Originality/value

This is the first study to explores the influence of blockchain technology on quality management and assurance in manufacturing and healthcare industry. Furthermore, prior research has not examined how integrating the LSS methodology with blockchain technology can aid in the control of product quality management.

Highly private blockchain-based management system for digital COVID-19 certificates

As a result of the declaration of the COVID-19 pandemic, several proposals of blockchain-based solutions for digital COVID-19 certificates have been presented. Considering that health data have high privacy requirements, a health data management system must fulfil several strict privacy and security requirements. On the one hand, confidentiality of the medical data must be assured, being the data owner (the patient) the actor that maintain control over the privacy of their certificates. On the other hand, the entities involved in the generation and validation of certificates must be supervised by a regulatory authority. This set of requirements are generally not achieved together in previous proposals. Moreover, it is required that a digital COVID-19 certificate management protocol provides an easy verification process and also strongly avoid the risk of forgery. In this paper we present the design and implementation of a protocol to manage digital COVID-19 certificates where individual users decide how to share their private data in a hierarchical system. In order to achieve this, we put together two different technologies: the use of a proxy re-encryption (PRE) service in conjunction with a blockchain-based protocol. Additionally, our protocol introduces an authority to control and regulate the centers that can generate digital COVID-19 certificates and offers two kinds of validation of certificates for registered and non-registered verification entities. Therefore, the paper achieves all the requirements, that is, data sovereignty, high privacy, forgery avoidance, regulation of entities, security and easy verification.

Blockchain for Modern Applications: A Survey

Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital cryptocurrencies, such as Bitcoin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.

A rapid review of machine learning approaches for telemedicine in the scope of COVID-19

The COVID-19 pandemic has rapidly spread around the world. The rapid transmission of the virus is a threat that hinders the ability to contain the disease propagation. The pandemic forced widespread conversion of in-person to virtual care delivery through telemedicine. Given this gap, this article aims at providing a literature review of machine learning-based telemedicine applications to mitigate COVID-19. A rapid review of the literature was conducted in six electronic databases published from 2015 through 2020. The process of data extraction was documented using a PRISMA flowchart for inclusion and exclusion of studies. As a result, the literature search identified 1.733 articles, from which 16 articles were included in the review. We developed an updated taxonomy and identified challenges, open questions, and current data types. Our taxonomy and discussion contribute with a significant degree of coverage from subjects related to the use of machine learning to improve telemedicine in response to the COVID-19 pandemic. The evidence identified by this rapid review suggests that machine learning, in combination with telemedicine, can provide a strategy to control outbreaks by providing smart triage of patients and remote monitoring. Also, the use of telemedicine during future outbreaks could be further explored and refined.

A Novel Homomorphic Approach for Preserving Privacy of Patient Data in Telemedicine

Globally, the surge in disease and urgency in maintaining social distancing has reawakened the use of telemedicine/telehealth. Amid the global health crisis, the world adopted the culture of online consultancy. Thus, there is a need to revamp the conventional model of the telemedicine system as per the current challenges and requirements. Security and privacy of data are main aspects to be considered in this era. Data-driven organizations also require compliance with regulatory bodies, such as HIPAA, PHI, and GDPR. These regulatory compliance bodies must ensure user data privacy by implementing necessary security measures. Patients and doctors are now connected to the cloud to access medical records, e.g., voice recordings of clinical sessions. Voice data reside in the cloud and can be compromised. While searching voice data, a patient's critical data can be leaked, exposed to cloud service providers, and spoofed by hackers. Secure, searchable encryption is a requirement for telemedicine systems for secure voice and phoneme searching. This research proposes the secure searching of phonemes from audio recordings using fully homomorphic encryption over the cloud. It utilizes IBM's homomorphic encryption library (HElib) and achieves indistinguishability. Testing and implementation were done on audio datasets of different sizes while varying the security parameters. The analysis includes a thorough security analysis along with leakage profiling. The proposed scheme achieved higher levels of security and privacy, especially when the security parameters increased. However, in use cases where higher levels of security were not desirous, one may rely on a reduction in the security parameters.

A Review of Blockchain Technology Applications in Ambient Assisted Living

The adoption of remote assisted care was accelerated by the COVID-19 pandemic. This type of system acquires data from various sensors, runs analytics to understand people’s activities, behavior, and living problems, and disseminates information with healthcare stakeholders to support timely follow-up and intervention. Blockchain technology may offer good technical solutions for tackling Internet of Things monitoring, data management, interventions, and privacy concerns in ambient assisted living applications. Even though the integration of blockchain technology with assisted care is still at the beginning, it has the potential to change the health and care processes through a secure transfer of patient data, better integration of care services, or by increasing coordination and awareness across the continuum of care. The motivation of this paper is to systematically review and organize these elements according to the main problems addressed. To the best of our knowledge, there are no studies conducted that address the solutions for integrating blockchain technology with ambient assisted living systems. To conduct the review, we have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology with clear criteria for including and excluding papers, allowing the reader to effortlessly gain insights into the current state-of-the-art research in the field. The results highlight the advantages and open issues that would require increased attention from the research community in the coming years. As for directions for further research, we have identified data sharing and integration of care paths with blockchain, storage, and transactional costs, personalization of data disclosure paths, interoperability with legacy care systems, legal issues, and digital rights management.

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

PURPOSE OF REVIEW

The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each.

RECENT FINDINGS

Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks.

SUMMARY

AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

Blockchain for deep learning: review and open challenges

Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today's deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly trustworthy deep learning frameworks.

Enhancing the security of E-Health services in Bangladesh using blockchain technology

The telemedicine service concept was mainly established to benefit the underprivileged people from rural areas of a country. However, due to the low literacy and awareness rates among rural population of Bangladesh, the service is not much effective. This paper represents a study on the awareness of the rural population of telemedicine service in Bangladesh and few key findings indicate how the awareness could be increased. The research also suggests that utilizing blockchain technology can enhance the data security and privacy. The research reveals some of the findings which can raise the awareness and popularity of telemedicine service among rural population. We have proposed implementation of blockchain technology which can vastly improve the security issue.

Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning

A medical center in the smart cities of the future needs data security and confidentiality to treat patients accurately. One mechanism for sending medical data is to send information to other medical centers without preserving confidentiality. This method is not impressive because in treating people, the privacy of medical information is a principle. In the proposed framework, the opinion of experts from other medical centers for the treatment of patients is received and consider the best therapy. The proposed method has two layers. In the first layer, data transmission uses blockchain. In the second layer, blocks related to patients' records analyze by machine learning methods. Patient records place in a block of the blockchain. Block of patient sends to other medical centers. Each treatment center can recommend the proposed type of treatment and blockchain attachment and send it to all nodes and treatment centers. Each medical center receiving data of the patients, then predicts the treatment using data mining methods. Sending medical data between medical centers with blockchain and maintaining confidentiality is one of the innovations of this article. The proposed method is a binary version of the HHO algorithm for feature selection. Another innovation of this research is the use of majority voting learning in diagnosing the type of disease in medical centers. Implementation of the proposed system shows that the blockchain preserves data confidentiality of about 100%. The reliability and reliability of the proposed framework are much higher than the centralized method. The result shows that the accuracy, sensitivity, and precision of the proposed method for diagnosing heart disease are 92.75%, 92.15%, and 95.69%, respectively. The proposed method has a lower error in diagnosing heart disease from ANN, SVM, DT, RF, AdaBoost, and BN.

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.

Blockchain for genomics and healthcare: a literature review, current status, classification and open issues

The tremendous boost in the next generation sequencing technologies and in the "omics" technologies resulted in the generation of hundreds of gigabytes of data per day. Nowadays, via integrating -omics data with other data types, such as imaging and electronic health record (EHR) data, panomics studies attempt to identify novel and potentially actionable biomarkers for personalized medicine applications. In this respect, for the accurate analysis of -omics data and EHR, there is a need to establish secure and robust pipelines that take the ethical aspects into consideration, regulate privacy and ownership issues, and data sharing. These days, blockchain technology has picked up significant attention in diverse fields, including genomics, since it offers a new solution for these problems from a different perspective. Blockchain is an immutable transaction ledger, which offers secure and distributed system without a central authority. Within the system, each transaction can be expressed with cryptographically signed blocks, and the verification of transactions is performed by the users of the network. In this review, firstly, we aim to highlight the challenges of EHR and genomic data sharing. Secondly, we attempt to answer "Why" or "Why not" the blockchain technology is suitable for genomics and healthcare applications in detail. Thirdly, we elucidate the general blockchain structure based on the Ethereum, which is a more suitable technology for the genomic data sharing platforms. Fourthly, we review current blockchain-based EHR and genomic data sharing platforms, evaluate the advantages and disadvantages of these applications, and classify these applications using different metrics. Finally, we conclude by discussing the open issues and introducing our suggestion on the topic. In summary, to facilitate the diagnosis, monitoring and therapy of diseases with the effective analysis of -omics data with other available data types, through this review, we put forward the possible implications of the blockchain technology to life sciences and healthcare.

Blockchain Technology Projects to Provide Telemedical Services: Systematic Review

BACKGROUND

One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment.

OBJECTIVE

This paper aims to review the feasibility of blockchain technology for telemedicine.

METHODS

The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex).

RESULTS

Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%).

CONCLUSIONS

These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.

Blockchain for COVID-19: a comprehensive review

The rampant and sudden outbreak of the SARS-CoV-2 coronavirus also called COVID-19 and its uncontrollable spread have led to a global crisis. COVID-19 is a highly contagious disease and the only way to fight with it is to follow social distancing and Non-Pharmaceutical Interventions (NPIs). Moreover, this virus is increasing exponentially day-by-day and a huge amount of data from this disease is also generated at the fast pace. So, there is a need to store, manage, and analyze this huge amount of data efficiently to get meaningful insights from it, which further helps medical professionals to tackle this global pandemic situation. Moreover, this data is to be passed through an open channel, i.e., the Internet, which opens the doors for the intruders to perform some malicious activities. Blockchain (BC) emerges as a technology that can manage the data in an efficient, transparent manner and also preserve the privacy of all the stakeholders. It can also aid in transaction authorization and verification in the supply chain or payments. Motivated by these facts, in this paper, we present a comprehensive review on the adoption of BC to tackle COVID-19 situations. We also present a case study on BC-based digital vaccine passports and analyzed its complexity. Finally, we analyzed the research challenges and future directions in this emerging area.

Practical considerations of developing and conducting a successful telehealth practice in response to COVID-19

The coronavirus disease 2019 (COVID‐19) pandemic posed significant challenges to clinicians practicing in outpatient settings focused on chronic disease treatment and management. Many interprofessional teams transitioned to telehealth to continue to provide care while minimizing in‐person interaction to reduce risk of transmission. Given that telehealth will likely remain as a care option even as the pandemic subsides, this review summarizes the applied recommendations on telehealth in interprofessional patient care, provides practical insights for successfully transitioning care from an academic medical center bariatric surgery program, and highlights future opportunities for research.

Integration of 5G and Block-Chain Technologies in Smart Telemedicine Using IoT

The Internet of Health Thing (IoHT) has various applications in healthcare. Modern IoHTintegrates health-related things like sensors and remotely observed medical devices for the assessment and managment of a patient's record to provide smarter and efficient health diagnostics to the patient. In this paper, we proposed an IoT with a cloud-based clinical decision support system for prediction and observation of disease with its severity level with the integration of 5G services and block-chain technologies. A block-chain is a system for storing and sharing information that is secure because of its transparency. Block-chain has many applications in healthcare and can improve mobile health applications, monitoring devices, sharing and storing of the electronic media records, clinical trial data, and insurance information storage. The proposed framework will collect the data of patients through medical devices that will be attached to the patient, and these data will be stored in a cloud server with relevant medical records. Deployment of Block-chain and 5G technology allows for sending patient data securely at a fast transmission rate with efficient response time. Furthermore, a Neural Network (NN) classifier is used for the prediction of diseases and their severity level. The proposed model is validated by employing different classifiers. The performance of different classifiers is measured by comparing the values to select the classifier that is the best for the dataset. The NN classifier attains an accuracy of 98.98. Furthermore, the NN is trained for the dataset so that it can predict the result of the dataset class that is not labeled. The trained Neural Network predicts and intelligently shows the results with more accuracy than other classifiers.