Securing patient data in the healthcare industry: A blockchain-driven protocol with advanced encryption.

Artificial intelligence and machine learning models are key tools in advancing data-driven healthcare solutions that aim to improve patient care and outcomes. A key step in developing accurate models is to train them on a large quantity and variety of data from several healthcare institutions. However, the standard approach of collecting data into a central database is often undesirable and not feasible in healthcare due to concerns regarding the privacy and security of patient data as well as more general data sharing restrictions. Distributed learning circumvents this need to share sensitive patient data by training models locally. This chapter reviews and discusses the principles and methods of distributed learning and describes its applications in the healthcare domain. Moreover, an overview of the current technical challenges and new advancements in distributed learning is provided, and important considerations in designing a distributed learning framework for healthcare applications discussed.

The integration of blockchain technology in healthcare has emerged as a promising solution for addressing the growing concerns of data privacy and security. This case study looks at how blockchain-enabled information systems (BEIS) can be used to handle patient health data, with a focus on making sure that personal medical data is kept private, correct, and easy to access. As telemedicine services grow and electronic health records (EHRs) become more common, the risk of data leaks and illegal access to patient information has grown. Traditional ways of protecting medical data, like centralized systems and security protocols, have trouble making it possible for healthcare providers to share data in a way that is clear and can't be changed. Blockchain is a strong answer to these problems because it is independent. By using smart contracts and cryptography, BEIS makes sure that records of patient information are safe, clear, and can't be changed. Only allowed parties can access these records in real time, which lowers the risk of data being changed or shared without permission. This case study looks at a blockchain-based patient data management system that was put in place in a regional healthcare network. It looks at how it protected privacy, made data easier to access, and made the system scalable. It also talks about the legal effects of using blockchain in healthcare, like how to get patients' permission and make sure that data security rules like GDPR and HIPAA are followed. The study's results show that blockchain has the potential to change the way healthcare data is managed. It could provide a safe and patient-centered way to keep personal health information safe while also encouraging healthcare systems to work together.

Health is one of the most important aspects of human life. For a healthy society, the healthcare sector must be reliable and efficient as much as possible. Blockchain networks can be used to provide innovative solutions to store and share patient data among hospitals, pharmaceutical companies, diagnostic labs, and physicians. Blockchain applications are capable of building trust and detecting fatal errors in the sharing of data. So blockchain can help in the security, effectiveness, and transparency of sharing medical data in the healthcare industry. Using this technology, medical institutions can get quality and trustworthy knowledge which they can use to improve the analysis of patient data and help in the good diagnosis of health issues. A thorough study and analysis have been done to explore the opportunities that blockchain technology can provide to improve the healthcare industry. The various characteristics, methods, and smooth workflow processes of blockchain technology are discussed in diagrams as a potential means of improving global healthcare. The article concludes by listing and analyzing various significant blockchain applications for the healthcare industry. It can help in implementing the smart contract, nonce concepts, and P2P distributed ledgers which ensure the immutability of the shared data. The methods discussed in this paper improve the security of Electronic Health Records (EHRs). The data of patients are taken directly via IoT devices which are used as medical instruments for patients which increases the reliability of data as there is no human interference in between hence human error is eliminated. The proposed technique implementation results show that the security of data has increased and it is more efficient.

The patient's medical data is very valuable and sensitive. Hackers are always trying to steal or tamper with this patient's data. Cyber-criminals can misuse or sell this patient's data. Protecting medical data, and ensuring security and privacy of data is a statutory and ethical mandate for healthcare services providers. Implementation of blockchain technology enhances the security, confidentiality, and traceability of patient data. Secure sharing of patient data among all stakeholders of healthcare ecosystems ensures quality and high speed in services.<br>Objective: This paper's objective is to review data security applications in the healthcare domain using blockchain technology and present the highlights from selected survey papers. The second objective is to implement data security using a web-based prototype based on blockchain technology. <br>Method: This blockchain technology enables secure online sharing of data using open, distributed, denationalized, and immutable ledgers. Blockchain-based online transactions are taking place between sender and receiver directly. Blockchain-based transactions eliminate the need for third-party intermediate agents. Data. Authors applied blockchain technology for the implementation of oral health big data using a web portal. Results: The taxonomy of concepts applied in blockchain and EHR, Smart Contracts, and Healthcare 4.0 systems in healthcare from the literature are mapped. To identify research gaps, a comparative study of curated survey papers is conducted with specific variables. The results of web portal implementation are discussed.<br>Conclusion: At the out, the authors tried to provide a holistic view of blockchain applications and presented insight into systems design by discussing algorithms, techniques and methods applied. Authors conclude that intelligent systems integrated blockchain systems are going to play a vital role in the healthcare industry and enhance the quality and efficiency of services.

<title>Abstract</title> Brain tumors, complex and potentially devastating, demand precise classification for effective patient prognosis and treatment planning. This paper introduces a novel approach to automate brain tumor classification using deep learning techniques, particularly convolutional neural networks (CNNs). However, conventional centralized methods compromise patient privacy and data security. To address this issue, federated learning (FL), a collaborative paradigm enabling model training across multiple institutions and aggregating models at a central server while preserving the confidentiality of sensitive medical data, is proposed. Moreover, an aggregation function at the central server is modified to identify the effect of aggregation on global model training. In addition to that, Blockchain technology is also integrated with FL architecture to enhance privacy preservation and trust, to ensure the integrity and immutability of patient data. By synergistically integrating modified FL, CNNs, and Blockchain technology, the proposed approach achieves accuracy (98%) and security in brain tumor classification. Through this, it aims to advance the field of medical imaging while prioritizing patient privacy and data security (through Blockchain technology) in brain tumor diagnosis and treatment.

Security and privacy of patient data is a vital requirement during exchange/storage of medical information over communication network. Steganography method hides patient data into a cover signal to prevent unauthenticated accesses during data transfer. This study evaluates the performance of ECG steganography to ensure secured transmission of patient data where an abnormal ECG signal is used as cover signal. The novelty of this work is to hide patient data into two dimensional matrix of an abnormal ECG signal using Discrete Wavelet Transform and Singular Value Decomposition based steganography method. A 2D ECG is constructed according to Tompkins QRS detection algorithm. The missed R peaks are computed using RR interval during 2D conversion. The abnormal ECG signals are obtained from the MIT-BIH arrhythmia database. Metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback-Leibler distance and Bit Error Rate are used to evaluate the performance of the proposed approach.

This review chapter provides an in-depth exploration of various aspects of data security, ranging from foundational principles to advanced techniques. It sheds light on the importance of maintaining confidentiality, integrity, and availability of data and presents a comprehensive overview of technologies and methodologies that can be employed to achieve robust data security. In today's digital age, ensuring the security and protection of data has become paramount. This research chapter explores various topics related to data security, including confidentiality, integrity, and availability. It also delves into the role of blockchain technology, zero-trust architecture, secure coding practices, legal and ethical considerations, homomorphic encryption, secure multi-party computation, hash functions and digital signatures, secure communication protocols, cryptocurrency and blockchain security, as well as cryptanalysis and side-channel attacks.It highlights various techniques and technologies that can be employed to protect data confidentiality, including encryption and access control mechanisms. Blockchain technology has emerged as a groundbreaking solution for enhancing data security. Zero-trust architecture challenges traditional security models by adopting a holistic and dynamic approach to data protection. Secure coding practices and DevSecOps play a vital role in developing secure software applications. The chapter investigates the importance of integrating security measures throughout the software development lifecycle. It emphasizes the adoption of secure coding practices and the implementation of DevSecOps methodologies to build resilient and secure software systems. Legal and ethical considerations are crucial when addressing data security. Homomorphic encryption and secure multi-party computation are advanced cryptographic techniques that allow data to be processed securely while encrypted. The chapter examines the principles and applications of these techniques, highlighting their significance in scenarios where privacy is paramount. Hash functions and digital signatures form the backbone of data authentication and integrity. The chapter discusses the properties and applications of hash functions and digital signatures in verifying the integrity and authenticity of data. Secure communication protocols are essential for protecting data during transmission. The chapter explores various secure communication protocols, such as Transport Layer Security (TLS), and examines their role in ensuring data confidentiality, integrity, and authenticity. Cryptocurrency and blockchain security focus on the secure storage and transfer of digital assets. The research chapter investigates the security considerations specific to cryptocurrencies and explores how block chain technology can mitigate vulnerabilities and ensure the trustworthiness of transactions. Cryptanalysis and side-channel attacks are threats to cryptographic systems. The chapter discusses these attack techniques, highlighting their potential risks and the measures that can be taken to counter them.

The incorporation of Artificial Intelligence (AI) technology into several industries has significantly impacted the usual workflows and processes in recent years, including the healthcare industry. Human Resource Management (HRM) is essential in healthcare businesses as it is responsible for the recruitment, training, and the retention of skilled staff members who are capable of providing high-quality patient care. This paper investigates different methods in which AI is used in HRM in the healthcare industry on the basis of existing research in the area. It analyzes how AI affects recruitment, talent management, workforce optimization, and employee well-being. This paper also discusses the challenges and future prospects of AI-driven approaches in HRM practices. It explores how these approaches are changing the way healthcare organizations operate and improving patient outcomes. The results provide some valuable contributions to the field of artificial intelligence in the healthcare sector. Initially, the chapter gives a factual foundation for the current presumptions on the implementation and difficulties of artificial intelligence in the healthcare domain. Further, it shows how artificial intelligence provides numerous opportunities to expedite Human Resource operations by offering automated applicant screening, customized learning systems, optimizing the workforce and enhancing employee engagement. Although AI has the capacity to revolutionize HRM practices in the healthcare industry, it also presents some challenges and obstacles. In order to ensure that AI-driven solutions promote fairness, transparency, and equity, it is crucial to address issues such as algorithmic bias, privacy of data and the impact on the human workforce in a deliberate manner. In addition, healthcare firms need to invest funds for implementing rigorous cyber security measures in order to ensure the privacy of patient and employee data from cyber-attacks and potential breaches.

Novel record linkage (RL) methods have the potential to enhance clinical informatics by integrating patient data from multiple sources-including electronic health records, insurance claims, and digital health devices-to inform patient-centered care. Engaging patients and other stakeholders in the use of RL methods in patient-centered outcomes research (PCOR) is a key step in ensuring RL methods are viewed as acceptable, appropriate, and useful. The University of Colorado Record Linkage (CURL) platform empowers the use of RL in PCOR. This study aimed to describe the process of engaging patients and other stakeholders in the design of an RL dissemination package to support the use of RL methods in PCOR. Customer discovery, value proposition design, and user experience methods were used to iteratively develop an RL dissemination package that includes animated explainer videos for patients and an RL research planning workbook for researchers. Patients and other stakeholders (researchers, data managers, and regulatory officials) were engaged in the RL dissemination package design. Patient partners emphasized the importance of conveying how RL methods may benefit patients and the rules researchers must follow to protect the privacy and security of patient data. Other stakeholders described accuracy, flexibility, efficiency, and data security compared with other available RL solutions. Dissemination package communication products reflect the value propositions identified by key stakeholders. As prioritized by patients, the animated explainer videos emphasize the data privacy and security processes and procedures employed when performing research using RL. The RL workbook addresses researchers' and data managers' needs to iteratively design RL projects and provides accompanying resources to alleviate leadership and regulatory officials' concerns about data regulation compliance. Dissemination products to promote adoption and use of CURL include materials to facilitate patient engagement in RL research and investigator step-by-step decision-making materials about the integration of RL methods in PCOR.

Objective One of the most significant ethical challenges in using artificial intelligence (AI) is the presence of ethical dilemmas. An ethical dilemma, a type of ethical conflict, is defined as any incompatibility between motivations and desires, educational methods, values, and the fulfillment of moral duties and responsibilities. Ethical challenges associated with using AI in nursing care include dilemmas linked to patient privacy and data security, the impact on patient autonomy, informed consent, safety and clarity of guidelines, accountability, and transparency. Additionally, AI may have unintended consequences on nursing care provision. This study examined the ethical dilemmas of using AI in nursing through a systematic review. Materials &amp; Methods This research was conducted independently by two researchers based on the research objectives and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) checklist. A comprehensive search of articles was performed from the beginning of January 2018 to the end of April 2025 across the databases Medline, Scopus, Web of Science, association for computing machinery (ACM) Digital Library, PubMed, as well as the Scientific Information Database (SID) and Magiran search engines. Moreover, the search employed a Medical Subject Headings (MeSH) strategy and the keywords “ethics,” “artificial intelligence,” and “nursing,” in both Persian and English, utilizing Boolean operators. The steps included designing the research question, article search and extraction, selection of relevant studies, tabulation and summarization of the information, and result reporting. Results Out of the 62 articles retrieved, 17 were reviewed and included in the study. The results revealed that, despite numerous benefits, using AI in healthcare is accompanied by many ethical challenges that must be considered. These challenges include safety and clarity of guidelines, informed consent, accountability, patient data privacy, and the effect on patient autonomy. Establishing clear guidelines and regulations is crucial to ensuring AI development and implementation in an ethical and accountable manner. The review showed that bias and discrimination received the most attention, being reported in 14 studies (82%). Following this, nurse autonomy was the second most frequently addressed topic, covered in 13 studies (76%), and the accountability and responsibility principle was also a focus in 12 studies (71%). The algorithmic transparency and interpretability topic was addressed in 10 studies (59%). Security, privacy, and human rights, as well as non-maleficence and beneficence, were each reported in 9 studies (53%). Justice was identified in 8 studies (47%), and finally, regulatory and legal frameworks were the least frequent ethical challenge or dilemma, appearing in 5 studies (29%). Conclusion This review demonstrates that ethical challenges in using AI in nursing care, particularly regarding justice, autonomy, non-maleficence, beneficence, and accountability, are common. Therefore, a comprehensive examination and the creation of effective solutions are crucial to guarantee the ethical and responsible use of AI in healthcare.

Electronic health records (EHRs) were seen as a panacea for managing all medical related data about the patient. The goal was to identify the siloed data fragmented across various healthcare providers and combine it in a uniform way in order to provide the best possible diagnostics and make it easy for the practitioners to gather and study patient's medical data. However, despite decades into development, such record systems are still long ways off in terms of practical implementation. They have been dogged by various shortcomings and above all concerns regarding potential loopholes in the privacy and security of patient data. Given the poor track record of the industry when it comes to preventing illicit access and sharing of data, there is an ever-growing impetus to stanch the consistent violation of the privacy of an individual's personal information by illegitimate parties. In this chapter, we aim to develop an understanding of an EHR system, identify associated shortcomings, and the requirements that it is supposed to fulfil. We also present various proposed implementations that show promise in the development of a safe, secure, and unified EHR system. In the last section, we discuss the potential of blockchains and how they have the goods to deliver a perfect EHR system.

Blockchain technology is one of the many disruptive technologies of the Fourth Industrial Revolution that will irrevocably change the way we live and work. These technologies are well embedded in the areas of global finance, health care and defence, to name a few. This review focuses on the relevance of blockchain technology to health care. Blockchain technology will be the unifying platform for sharing patient data currently inaccessible due to the siloed architecture of legacy software systems, and as a result potentially be the basis for precision or individualized patient treatment. It will also strengthen digital security of sensitive patient data that is presently a lucrative target for cyber criminals. In the current COVID-19 environment, clinicians will rely more on telehealth to reduce person-to-person contact. This service can be delivered by the clinical team with confidence in the veracity of the patient data made accessible through the blockchain platform. Smart contracts written on the blockchain platform will reduce the possibility of international humanitarian aid to low- and middle-income countries being misspent. The pharmaceutical supply chain industry is adopting blockchain technology to ensure supply chain provenance. Similarly, the health insurance industry recognizes how the blockchain ecosystem can improve services to its members and expedite reimbursements to clinicians.

Blockchain is a new technology under the interest of many researchers based on the benefits it provides for various systems in different industries. One of the main applications of blockchain is the healthcare sector where security, privacy and integrity of patient data is essential. In this paper, we discuss the problems faced by healthcare systems and how blockchain technology can be used to support these systems and improve the patient journey. A new Petri Net-based blockchain framework is proposed. The framework focuses on two main layers: process layer and data layer along with a mapping of the blockchain technology into the model. The proposed framework is validated using mathematical modeling and Petri Nets.KeywordsBlockchainPetri NetHealthcareSmart contractsWorkflow management

The use of information technologies has become an integral part of the modern world, and the internet has played a vital role in facilitating access to various services, from ordering food to obtaining government services. However, with the increased reliance on the internet, the issue of information protection has become more pressing than ever. In the wake of the COVID-19 pandemic, remote work and online learning have become the norm, which has further emphasized the need for robust information protection mechanisms. One of the primary ways of ensuring information security is through the use of cryptographic algorithms. These algorithms have been in use for several decades and have provided an effective means of protecting sensitive information. However, with the exponential growth of computing power, the demands on these algorithms have increased, and some of them have become outdated and are no longer secure enough. As a result, there is a pressing need for the development of new cryptographic algorithms that use more complex mathematical principles to provide a higher level of security. One such class of algorithms that has gained popularity in recent years is elliptic curve cryptography. These algorithms use the principles of elliptic curves to provide greater security while using relatively fewer system resources. Elliptic curve cryptography has several advantages over other cryptographic algorithms. Firstly, it provides a higher level of security with shorter key lengths, making it ideal for use in devices with limited processing power and memory. Additionally, it offers greater resistance to attacks by quantum computers, which are expected to become more prevalent in the future. Moreover, elliptic curve cryptography has found applications in several areas, including secure communication protocols, digital signatures, and key exchange algorithms. These algorithms are part of the TLS protocol, which is used by the HTTPS protocol to ensure secure transmission of information over the Internet. The continuous development and integration of cryptographic algorithms into various information systems will be crucial for protecting against cyber threats in the future.

Blockchains provide a unique approach to storing healthcare data, conducting healthcare transactions, and proving the trustworthiness of healthcare data in the context of a decentralized and open healthcare network ecosystem. Blockchain technology for healthcare businesses, whether for internal operations or collaborative research, is causing continuing debate regarding the potential risks to user data security and privacy. Despite considerable interest and attention from the corporate, government, and academic sectors, blockchain technology deployment in the healthcare business is still in its early stages. Blockchain technology has the potential to greatly boost patient data security in the healthcare industry. We not only understand the problems and expectations associated with security and privacy, but we also provide effective ways and procedures for overcoming these concerns via the use of technology. The first stage in using blockchains in the healthcare business is determining what features and standards are required to ensure the secure and effective transfer of electronic health information. Then we look at the technologies that may be utilized to offer the essential security and privacy aspects for each of the three potential blockchain uses in the healthcare business. In terms of the sharing of electronic medical records, blockchain technology has three potential uses blockchain technology to validate a patient's identity is standard. The previous work highlighted numerous potential uses of blockchain technology in healthcare. Many various types of information will be exchanged, including ideas, risks, requirements, development tools, system designs, and deployment strategies. Based on the findings of our poll, we were able to make some plausible assumptions regarding the problem.