Fortifying healthcare data security with precision blockchain access

With increasing demand for collaborative work on healthcare data for research and effective healthcare management, the need for a distributed mechanism for resource sharing is inevitable. Common implementation for integrated healthcare data management usually rely upon a centralized data processing and repository, in which data is extracted as needed. However, such implementation is unscalable and prone to security and privacy issues, specifically when dealing with private and confidential health data. In this paper, we propose a preliminary design of permissioned blockchain application for integrated health data management. Our proposed scheme utilizes distributed ledger mechanism for data storage and verification, which allows participating entities to access and verify healthcare data in a distributed manner without depending on centralized facility. The proposed design reflected a simulated scenario involving medical institutions and military service in healthcare data management in national service recruitment.

New generation of health care IT systems are collecting and storing more and more data of patients. Useful knowledge can be extracted from the data in EMR or PHR to provide medical advises to patients, while through data analysis the result statistics can be used to support the scientific research. However, RDBMSs-based framework is not able to support the requirements of massive health care data storage, management and analysis. To solve the problem, this paper proposes a massive data management and analysis solution based on Hadoop to archive better performance, scalability and fault tolerance. The data management framework is presented. Besides, 2 different data analysis methods based on MapReduce and Hive are proposed. Experiment results of data upload, data query and data analysis show that the performance of the proposed framework is greatly improved, and a brief summary of the performance and the differences between 2 methods of MapReduce and Hive is also discussed.

BackgroundRandomised implementation trials evaluate the effects of implementation strategies on implementation outcomes and may also monitor clinical effectiveness. Routine healthcare data are used in implementation trials for participant identification, intervention delivery, and/or outcome ascertainment. Trial efficiency (scientific, operational, statistical, and economic) is operationalised across trial design, processes, superstructure, infrastructure, and stakeholder engagement (the Trial Efficiency Pentagon). Despite frequent usage, the contribution of routine data to implementation trial efficiency remains underexplored. We aimed to investigate how the use of routine healthcare data affects trial efficiency in two implementation trials.MethodsWe conducted a qualitative comparative case study of two implementation trials, one UK-based and one US-based. Participants were purposively sampled from trial teams involved in the use and management of routine healthcare data. Data were collected through semi-structured interviews, document analysis, and feedback workshops. Framework analysis guided by the Trial Efficiency Pentagon was used to analyse the data, and data flow diagrams were developed to visualise routine data pathways within each trial.ResultsThe two trials (DIGITS and IMP2ART) used routine data to characterise the practice population of eligible patients, support clinical and economic outcome evaluation, facilitate audit and feedback, and assist in intervention delivery. Common facilitators that supported the use of routine data included sufficient IT and hardware capacity, relatively low cost, centralised regulatory approval for multi-site studies, and strong collaboration and partnerships. Common barriers included administrative complexity, redundant bureaucratic processes, and challenges with data sharing requirements. Key differences included the DIGITS trial’s in-house data warehouses within an integrated healthcare system ensured high data quality and enabled preliminary analyses. In contrast, the IMP2ART trial, managing a larger national sample, employed an external research database to integrate data from various EHR systems but faced challenges such as legacy systems, diverse coding practices and site-specific approvals. Data quality can act as either a facilitator or a barrier.ConclusionsRoutine data has an impact on implementation trial efficiency across trial design, processes, superstructure, infrastructure, and stakeholder engagement. To improve trial efficiency in public healthcare systems, researchers must address technological and regulatory barriers to accessing data. In private healthcare systems, data use and access hinges on investing in robust IT infrastructure and ensuring comprehensive organisational commitment.Trial registrationIMP2ART trial registration: ISRCTN15448074; DIGITS trial Clinicaltrials.gov Identifier: NCT05160233.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13063-026-09706-3.

BackgroundThe field of “Big Health,” which encompasses the integration of big data in healthcare, has seen rapid development in recent years. As big data technologies continue to transform healthcare, understanding emerging trends and key advancements within the field is essential.MethodsWe retrieved and filtered articles and reviews related to big data analytics in health research from the Web of Science Core Collection, including SCI Expanded and SSCI, covering the period from 2009 to 2024. Bibliometric and co-citation analyses were conducted using VOSviewer and CiteSpace.ResultsA total of 13,609 papers were analyzed, including 10,702 original research and 2,907 reviews. Co-occurrence word analysis identified six key research areas: (1) the application of big data analytics in health decision-making; (2) challenges in the technological management of health and medical big data; (3) integration of machine learning with health monitoring; (4) privacy and ethical issues in health and medical big data; (5) data integration in precision medicine; and (6) the use of big data in disease management and risk assessment. The co-word burst analysis results indicate that topics such as personalized medicine, decision support, and data protection experienced significant growth between 2015 and 2020. With the advancement of big data technologies, research hotspots have gradually expanded from basic data analysis to more complex application areas, such as the digital transformation of healthcare, digital health strategies, and smart health cities.ConclusionThis study highlights the growing impact of big data analytics in healthcare, emphasizing its role in decision-making, disease management, and precision medicine. As digital transformation in healthcare advances, addressing challenges in data integration, privacy, and machine learning integration will be crucial for maximizing the potential of big data technologies in improving health outcomes.

Lung cancer continues to be one of the most widespread and deadly cancer diagnoses that affects humans worldwide. Early detection of lung cancer leads to decreased mortality rates; however, several challenges hinder the development and deployment of effective predictive models. These challenges consist of mainly the problem of high computational power to evaluate large-scale medical data, privacy and security of medical data, limited data sharing between medical organizations and interpretability to handle the black box problem that AI-based models face. Such limitations have posed severe difficulties to the utilization of conventional approaches in the prediction of lung cancer, thus limiting them most importantly for general use, especially in clinical practice settings in real time. To address these challenges, this research introduced a novel lung cancer prediction model that utilizes an integrated framework combining MapReduce, Private Blockchain, Federated Learning (FL), and Explainable Artificial Intelligence (XAI). It improves lung cancer detection using MapReduce to handle large lung cancer datasets, supporting rapid and scalable learning. Private Blockchain is used for the secure, tamper-proof, and immutable processing of patient information, whereas FL allows healthcare sectors to train models together, without compromising patients’ privacy. Moreover, it also employed XAI to improve the model’s interpretability so clinicians can understand and rely on AI predictions. Together, these methods improve AI’s efficiency and trustworthiness in medical applications. This proposed model provides better and more secure lung cancer predictions, ensuring interpretability and collaboration. With an exceptional accuracy of 98.21% and a miss rate of just 1.79%, it outperforms previously published approaches, establishing a new benchmark for privacy-preserving, explainable, and scalable AI models in healthcare.

The privacy and security of medical data is the most important aspect of Health Information System (HIS) as healthcare is totally reliant on data for treatment and research. The main purpose of HIS is to improve treatment processes by providing the latest information about the patient. The healthcare data consist of medical history, path lab records, medication history, diagnosis and treatment details, genetic and sexual information and profession. The privacy of the patient is becoming a concern for healthcare organizations because of the increasing threats and attacks. Moreover, using blockchain-based infrastructure would empower the patients whilst maintaining the security and privacy of the data and providing quality healthcare services. Healthcare providers are moving towards decision-making processes for the advancement in diagnostics, which require the availability and accessibility of accurate data, which is provided by HIS. The healthcare systems will be technically advanced to provide real-time patient monitoring.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

Healthcare data from clinical institution and pharmaceutical industries are fragmented and are sensitive with protected health information of individual. Data privacy, safety and security are of prime important. It is vital to train machine learning model without compromising privacy. In federated learning the global model is aggregated on aggregation servers as per the parameter of local model instead of local data. There is waiting for straggler client before aggregation in each round .Every client operated at its own speed of training. Asynchronous federated learning aggregates the global model based on available updated model received at the server asynchronously during each round of training. Asynchronous federated learning improves the efficiency, performance, privacy and security. In this paper, based on the Lung cancer dataset, the classification model is built. With NVIDIA flare, synchronous federated learning is carried out with one server and three clients for spliited data of lung cancer of Kaggle. The performance of synchronous federated learning is observed similar to the classical machine learning model. Modeling of asynchronous federated learning is carried out with Petri Net and analyzed for the reachability analysis with workcraft software. Substantial improvement in medical data privacy, security and safety is observed.

Every industry is expanding too quickly and adjusting to this new technology as it develops. Since it has the potential to deliver more precise and economical patient care, healthcare data management has recently attracted a lot of attention. Even today, many hospitals hold their own autonomous record management system, which causes security issues. In centralized record management systems, data privacy, centralized data stewardship, and system vulnerability problems affect traditional client-server-based and cloud-based health data management systems. Blockchain technology has a promising future in the healthcare industry because of its immutability, transparency, privacy, and security properties, which can address certain critical problems with the health management system. A more patient-oriented approach in healthcare systems is required to improve the accuracy and transparency of medical data. In healthcare systems, health records are the most sensitive asset that must be unique and protected across the system. Our objective is to showcase the potential use of blockchain technology in health record management systems in hospitals. In this paper, we demonstrate a health record management system that uses blockchain technology to store the medical records of a patient across multiple hospitals. The proposed system will mainly help in maintaining consistency issues related to data along with improved security in the system.

Blockchain technology is a forefront innovation that drives multi-user data accessibility with patient ownership, traceability, transparency, security, and privacy protection. However, optimizing blockchain latency and its relationship with security remains underexplored in health data management. This study aims to optimize security and latency in healthcare data management through a novel Blockchain-based framework. The objectives include: (1) developing an innovative EMR management architecture that integrates key stakeholders using Blockchain and smart contracts, (2)creating a multi-objective optimization model for Hyperledger Fabric Blockchain in medical health management, and (3)implementing and evaluating advanced optimization techniques using block Size and consensusLevels as key decision variables. This study bridges the gap by proposing a novel methodology that integrates the Hyperledger Fabric framework with smart contracts to optimize security and storage in health data systems. A key contribution of this research is the development of a multi-objective optimization model employing two advanced non-dominated sorting genetic algorithms, NSGA-II and NSGA-III, to configure system parameters such as blockSize and consensusLevels. Evaluation results show that NSGA-II outperforms NSGA-III, achieving 50% higher Hyper-volume and five distinct non-dominated solutions compared to two, demonstrating its superior ability to balance conflicting objectives and provide optimal configurations. The proposed framework tackles critical challenges in health data interoperability, privacy, and security while ensuring robust, patient-centric data management. By enabling multi-user access, enhanced security, and optimized storage, this work advances Blockchain’s role in healthcare and positions the industry for the Digital Health era. Future work will expand objectives and enable real-time deployment.

The rapid development of the Internet of Things technology has led to a boom in the adoption of intelligent healthcare management systems in the healthcare industry. However, it has also highlighted key issues such as security, privacy, and efficient query of medical data. Traditional methods for querying medical data suffer from severe data leakage risks, low query performance, and excessive storage space. This article proposes a comprehensive Secure ENcrypted Search for Health Scheme (SENSH) solution based on consortium blockchain and searchable encryption to address these challenges. SENSH enables efficient authorization management through Bloom filters, ensuring fast querying of large datasets by authorized users while saving storage space. It uses off-chain Advanced Encryption Standard (AES) and on-chain storage management for data protection, significantly reducing the likelihood of data exposure. The system is also enhanced with event triggering and logging mechanisms to support real-time monitoring and data tracing to meet audit compliance requirements. It provides version control and timestamping to accommodate dynamic data updates, employs an obfuscationfactor to prevent tag-based original data content leakage, and supports dynamic updating of tags to accommodate different access requirements. Experimental results show that SENSH excels in authorization management, privacy protection, defense against tampering, and anti-replay and Distributed Denial of Service (DDoS). Compared with existing schemes, SENSH has significant advantages in terms of gas consumption, computation cost, and execution time. It is particularly suited for the protection and efficient query of medical and health data.

Properly managing healthcare data is a complex endeavor that must balance the requirements and interests of many stakeholders. In this paper, we present the findings from a panel discussion with healthcare professionals and academics, who elaborate on the current situation in healthcare data management as well as the future role that blockchain could play in this sector. Based on the findings of this panel, we structure the research field of healthcare data management and provide numerous avenues for future research. The outcome is a framework that highlights the important role of healthcare data and puts them into context. From a patient's perspective, we specifically elaborate on trust and privacy as well as the expected benefits. Additionally, four important data aspects are identified: integrity, security, interoperability, and, finally, sharing and transfer. We also outline the importance of current problems and derive several relevant and timely research questions that build the foundation of a research agenda for blockchain-driven innovation in healthcare data management. In summary, the framework will inform practitioners of blockchain's potential in healthcare and structure the area for researchers, who are called upon to investigate the respective topics in greater detail.

Investigating the literature reveals the fact that the key management protocols play a vital role in protecting the security and privacy of medical data in telecare medical information systems. Recently, Tseng et al . have proposed an interesting elliptic curve cryptosystem (ECC) based self-certified key management scheme that can yield secure channel for the communications of secure sensors (cluster members) and access point (cluster head). After careful consideration, we found that their scheme suffers from the cluster head impersonation, replay, and key replicating attacks. In addition, in the secure session phase, there are some errata and a point multiplication that is undefined in the ECC and hence is mathematically incorrect. Finally, yet importantly, their presented scheme has been adopted for a similar application by other authors with the same problem. Therefore, in this paper, we first try to elaborate the existing errata and security threats. Second, we propose a modified version, which is free from the challenges of their scheme. Eventually, we propose a novel anonymous ECC-based self-certified two-factor key management scheme that not only provides the desired security features, but also has much better efficiency than several recently-published schemes, such as the presented one by Tseng et al . Our formal security verification and proof besides the efficiency analysis support our claim.

Venous thromboembolism (VTE) is a common vascular disorder requiring extended anticoagulation therapy postdischarge to reduce recurrence risk. Home rehabilitation management systems that use electronic health records from hospital care provide opportunities for continuous patient monitoring. However, transferring medical data from clinical to home settings raises significant concerns about privacy and security. Conventional methods such as manual data entry, optical character recognition, and dedicated data transmission lines face notable technical and operational challenges. This study aims to develop a QR code-based security transmission algorithm using Avro and byte pair encoding (BPE). The algorithm supports the secure creation and transfer of out-of-hospital health records by enabling patients to scan QR codes via a dedicated mobile app, ensuring data security and user privacy. Between January and October 2024, 300 hospitalized patients with VTE were recruited at the Sixth Medical Center of the Chinese PLA General Hospital. Post discharge, participants used a home rehabilitation app tailored for VTE management. The QR code-based security transmission algorithm was developed to securely transfer in-hospital electronic health records to the out-of-hospital app. It uses BPE, Avro, and Gzip for optimized data compression and uses ChaCha20 and BLAKE3 for encryption and authentication. Specifically, BPE tokenizes medical text, while Avro serializes JSON (JavaScript Object Notation) objects, contributing to data encryption. A proprietary tokenizer was trained, and compression efficiency was evaluated using a "Performance Benchmark Dataset." Comparative analyses were conducted to assess the compression efficiency of JSON serialization methods (Avro and ASN.1 [Abstract Syntax Notation One]), and tokenization algorithms (BPE and unigram). The dataset consisted of JSON files from 300 patients, averaging 240.1 fields per file (range 89-623) and 7095 bytes in size (range 2748-17,425 bytes). Using the BPE + Avro + Gzip algorithm, the average file size was reduced to 1048 bytes, achieving a compression ratio of 6.67. This was 1.82 times more efficient than traditional Gzip compression (average file size: 1907 bytes; compression ratio: 3.66; P<.001). For Chinese medical text tokenization, BPE outperformed unigram with a compression ratio of 4.68 versus 4.55 (P<.001). Avro and ASN.1 demonstrated comparable compression ratios of 2.57 and 2.59, respectively, when used alone (P=.30). However, Avro combined with BPE and Gzip significantly outperformed ASN.1, achieving compression ratios of 6.67 versus 5.21 (P<.001). Additionally, 84.7% (254/300) of patients needed to scan only 1 QR code, requiring an average of 3.1 seconds. The QR code-based security transmission algorithm using Avro and BPE efficiently compresses and transmits data in an encrypted manner and authenticates the identity of the scanning users, ensuring the privacy and security of medical data. Delivered as a software development kit, the algorithm offers straightforward implementation and usability, supporting its broad adoption across various applications.