Healthcare service providers are dependent on accurate information systems. Some crucial challenges are (1) improvement of patient caring services, (2) containing the deployment cost of information systems, and (3) avoiding any disturbances in its business processes at the time of data gathering and processing activities. Technological advancements are a significant driver of efficient healthcare information systems and services. By creating a rich healthcare-related data foundation and integrating technologies like the internet of things (IoT), blockchain technology, artificial intelligence (AI) techniques, and big data analytics, the digital transformation in healthcare is recognized as a pivotal component to tackle these challenges. For example, it can improve diagnostics, prevention, and patient therapy, ultimately empowering caregivers to use an evidence-based method to enhance clinical decision-making. Real-time interactions permit a physician to monitor a patient 'live' instead of interaction regularly (e.g., weekly, monthly). This way, healthcare operation can provide better services. However, the IoT system creates the risk of a sensitive data breach without a highly secure infrastructure. Blockchain technology improves the reliance on a centralized authority to certify information integrity and ownership and mediate transactions and exchange of digital assets. As a result, the mining process in the blockchain is very resource-intensive; hence, miners create coalition groups to cross-check each block of transactions in return for a reward. In addition, it creates enormous competition among miners, and consequently, dishonest mining strategies (e.g., block withholding attack, selfish mining, eclipse attack) need to be controlled. Consequently, it is necessary to regulate the mining process to make miners accountable for any dishonest mining behaviours; game theory can help regulate it. Finally, this chapter presents a survey of game theory used to address the common issues in the blockchain network.
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The world has been going through a challenging time in recent years. On the one hand, the coronavirus pandemic (i.e., COVID-19) is placing enormous strain on the global healthcare sector's workforce, infrastructure, its supply chain and exposing social inequities in healthcare. In contrast, medical and technical advances are driving better healthcare for humanity across the globe. Several foundational shifts are arising from the COVID-19's spread. For example, patients are heavily involved in treatment decision-making processes, the rapid use of virtual consultation, the adoption of interoperable data and data analytics, and tremendous public-private collaborations in the healthcare industry and related service innovation.
The long-standing assumption that healthcare is ‘sick care' for the physical body, including patients' minds and spirits, has changed. Focus shifting from healthcare to health and well-being and providers should integrate this shift into the design of their service offering and delivery channels and locations. In addition, consumers (or patients) will expect care to be available when and how it is most convenient and safe for them. It includes virtual care, at-home prescription delivery, remote monitoring, digital diagnostic and decision support, self-service education applications, and social support.
Consequently, healthcare organizations invest in optimizing or replacing foundational structures, technologies, and business processes automation. Digital transformation helps individual healthcare organizations and the broader health ecosystem improve working methods, widen access to services, and provide a more effective patient and clinician experience. In addition, four critical aspects of computing are playing increasingly pivotal roles (i.e., IoT, blockchain, cloud computing, and artificial intelligence) in this transformation.
Along with technological advancement, healthcare service provision is changing rapidly. The IoT paradigm has revolutionized the healthcare industry. The IoT technology can help collect invaluable patient data, automate workflows, provide insights on disease symptoms and trends, and facilitate remote patient care. Over these years, several advanced IoT applications developed to support patients and medical officials. For example, IoT technology-based healthcare improves existing features by supporting patient management, medical records management, medical emergency management, patient treatment management, and other facilities, thus increasing the quality of healthcare information technology (IT) based applications.
However, many exiting IoT-based healthcare systems leveraged for managing data are centralized and pose potential risks of a single point of failure. Blockchain technology (Nakamoto, 2008) provides more intelligent and flexible handing of transactional data through appropriate convergence with IoT technology in supporting data integration and processing. This chapter describes a review of game theory models used to address common issues in the blockchain network. It includes different security issues (e.g., selfish mining, Denial of Service (DoS) attack, regarding mining management). Reward allocation, the verified transactional information is stored in a chain of blocks (a central data structure), and the chain grows in an append-only manner with all new verified blocks to it. This way, the whole process consists of many operations, for example, verifying transactions, disseminating blocks, and attaching blocks to the blockchain.
Blockchain-based information systems need several consensus nodes to participate in the information exchange network. The rational nodes perform actions or strategies to optimize their utility. In addition, the malicious nodes may launch attacks that damage the blockchain-enabled IoT information-sharing networks. Game theory-based techniques can mitigate consensus protocols such as Byzantine Fault Tolerance (BFT) protocol (Castro & Liskov, 2002). Nevertheless, the consensus protocols require a centralized permission controller and only achieve a tiny group of nodes. Such a consensus protocol is thus not useable to the blockchain network that is a decentralized and large-scale system. Different techniques and remedies (e.g., a Markov Decision Process (MDP) (Altman, 1999)) were deployed to optimize strategies of the blockchain nodes to stop their inappropriate behaviours. However, the minimization methods do not consider the interactions among the nodes.