Blockchain-Based Healthcare Data Management: Analysis and Evaluation of Security, Scalability, and Compliance for Electronic Health Records (EHRs)

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE Blockchain-Based Healthcare Data Management: Analysis and Evaluation of Security, Scalability, and Compliance for Electronic Health Records (EHRs) 1 Deng Zilong City Graduate School, City University, Malaysia Anqing Vocational & Technical College, China dzlong@aqvtc.edu.cn 2 Mustafa Muwafak Alobaedy City Graduate School, City University, Malaysia mustafa.theab@city.edu.my Abstract—The integration of blockchain technology into healthcare data management systems addresses persistent chal- lenges such as data integrity, privacy, secure sharing ofElectronic Health Records (EHRs), and compliance with regulatory stan- dards. This study identifies the problem of centralized healthcare systems being prone to unauthorized access, data breaches, and limited scalability, hindering efficient data sharing across stakeholders. The primary objectives are to design, implement, and evaluate a blockchain-based healthcare data management framework to enhance data integrity, scalability, and access control. A multi-phase methodology was employed, including simulation testing, encryption evaluation, and benchmarking of blockchain security mechanisms. Synthetic healthcare datasets representing EHRs were used, with a sample size of 1 million transactionsprocessedundernormal,peak,andstressconditions. Techniques such as AES-256 encryption, SHA-3 hashing, and smart contracts were applied for data security and access control. Performance metrics analyzed include transaction latency, throughput, encryption efficiency, data integrity, and system resilience. The results demonstrate significant improvements, with an average transaction latency of 2.1 seconds, throughput of 1,200 TPS, 100% data integrity, and robust compliance with role-based access control mechanisms. Compared to traditional centralized systems, blockchain exhibited superior scalability, reduced error rates, and enhanced transparency in data-sharing processes. This study contributes a novel blockchain-based healthcare data framework optimized for secure, transparent, and regulatory-compliant data management. The findings show blockchain’s transformative potential to revolutionize healthcare data sharing, offering a resilient and tamper-proof platform for EHR systems while addressing scalability and privacyconcerns. Keywords—Blockchain Technology, Healthcare Data Management, Electronic Health Records (EHRs), Data Integrity and Privacy, Scalability and Access Control I. INTRODUCTION The healthcare industry is currently experiencing a major shift towards digitalization, with Electronic Health Records (EHRs) playing a crucial role in improving healthcare delivery. These digital records help streamline the management of patient information, enhance the accuracy of diagnoses, and reduce administrative burdens. However, traditional centralized systems used for managing EHRs face significant vulnerabilities. They are prone to data breaches, unauthorized access, and cyberattacks. Additionally, the lack of interoperability between different healthcare systems often creates barriers to seamless data sharing, limiting the full potential of digital healthcare records. To address these issues, blockchain technology has emerged as a promising solution due to its decentralized, transparent, and tamper-proof nature [1]. By leveraging distributed ledgers and cryptographic hashing, blockchain ensures that healthcare data remains secure, immutable, and accessible only to authorized individuals [2]. Furthermore, smart contracts play a critical role in automating access control and consent management, reducing reliance on manual processes and minimizing human errors [3]. Despite its potential, blockchain adoption in healthcare is not without challenges. Issues such as scalability limitations, inefficiencies in consensus mechanisms, and integration hurdles with existing healthcare IT infrastructure present significant barriers [4]. Additionally, navigating stringent regulatory frameworks, including the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), adds complexity to implementation efforts [5]. These challenges underscore the need for innovative blockchain frameworks that can balance technical efficiency with regulatory compliance, while also ensuring scalability for managing large healthcare datasets [6]. This study focuses on evaluating the performance and effectiveness of the proposed blockchain architecture in comparison to traditional healthcare data management systems. These objectives aim to bridge existing gaps in healthcare data security, accessibility, and efficiency, ultimately contributing to the development of a more resilient and transparent healthcare ecosystem. II. LITERATURE REVIEW The integration of blockchain technology into healthcare data management has garnered significant attention in recent years due to its potential to address persistent challenges related to data integrity, privacy, security, and interoperability. Traditional centralized healthcare systems often suffer from vulnerabilities such as data breaches, unauthorized access, inefficient data-sharing mechanisms, and limited scalability. These challenges have underscored the need for innovative This research was funded by Anhui Province Young and Middle aged Backbone Teachers Overseas Study Visit Project. 2025 5th International Conference on Advances in Electrical, Electronics and Computing Technology (EECT) | 979-8-3315-4154-5/25/$31.00 ©2025 IEEE | DOI: 10.1109/EECT64505.2025.10966949 Authorized licensed use limited to: MULTIMEDIA UNIVERSITY. Downloaded on December 19,2025 at 22:53:00 UTC from IEEE Xplore. Restrictions apply. solutions capable of managing sensitive healthcare data securely and transparently. A. Blockchain in Healthcare: Context and Applications Blockchain technology is revolutionizing healthcare data management by addressing security, efficiency, and reliability challenges. Traditional centralized systems are vulnerable to unauthorized access, data breaches, and inefficient data-sharing, compromising patient privacy and creating barriers to seamless medical information exchange. Blockchain’s decentralized structure, cryptographic safeguards, and immutable ledger offer a more secure and transparent approach, ensuring that transactions are tamper-proof, auditable, and accessible only to authorized stakeholders [1]. Blockchain enhances interoperability by reducing bottlenecks in fragmented systems, enabling seamless access to patient records while maintaining robust data security and privacy [2]. Its transparency, traceability, and immutability improve accountability and trust among stakeholders, making it an effective solution for managing Electronic Medical Records (EMRs) [3]. Smart contracts—self-executing agreements embedded in the blockchain—automate compliance with data-sharing policies, reducing administrative burdens and human error [7], [5]. Data privacy and security, critical challenges in healthcare, are addressed through blockchain’s encryption algorithms, cryptographic hashing, and smart contracts. These mechanisms ensure adherence to regulations like GDPR and HIPAA, safeguard sensitive patient information, and provide a verifiable record of all data transactions [8]. Blockchain’s ability to detect breaches and enforce access permissions automatically enhances transparency and accountability among providers, insurers, and regulators. The technology also minimizes risks of unauthorized data manipulation, streamlines consent management, and improves regulatory compliance through its immutable ledger. By fostering real-time security and transparency, blockchain holds transformative potential to modernize healthcare data management and protect patient rights [1], [2]. B. Research Gap While blockchain demonstrates significant potential for healthcare data management, challenges related to scalability, interoperability, energy efficiency, and regulatory compliance must be addressed. Advancements in sharding, multi-layered architectures, hybrid models, and consensus mechanisms, alongside greater real-world testing, are essential to realizing blockchain’s full potential in healthcare. Scalability is a critical challenge for blockchain adoption in healthcare data management. While blockchain offers promising solutions for data integrity, privacy, and interoperability, its efficiency in handling high transaction volumes is limited by current consensus mechanisms such as Proof of Work (PoW) and Proof of Stake (PoS). These mechanisms often lead to delays, energy inefficiencies, and computational overhead, constraining practical applications in healthcare [9]. Research highlights various strategies to address these limitations. Abdulaziz et al. emphasize Ethereum-based workflows to optimize resource use, improve transaction throughput, and reduce latency [10]. However, these developments are mostly confined to theoretical models and lack empirical validation in large-scale healthcare systems [11]. The rapid growth of healthcare datasets—driven by advancements in electronic health records (EHRs), telemedicine, and wearable devices—further exacerbates scalability issues. Traditional blockchain architectures struggle to process and store these large volumes of data efficiently [12]. Sharding, a technique that divides networks into smaller groups for parallel processing, is one proposed solution. While effective in improving scalability, sharding introduces vulnerabilities such as communication inefficiencies between shards and potential points of failure [13]. Multi-layered blockchain architectures are another promising approach. For example, Javan et al. propose a model for enhanced EHR sharing and drug supply chain management, which reduces network traffic and improves throughput [14]. However, aligning these frameworks with existing healthcare IT systems remains a challenge, limiting real-world adoption [15]. Innovations in consensus mechanisms also aim to improve scalability. Sadath et al. propose a hierarchical consensus model within Hyperledger Fabric to enhance performance [16]. Despite progress, these models still experience latency during extreme network congestion, indicating a need for further refinement. Interoperability between blockchain and legacy healthcare systems is another significant challenge. Many healthcare IT infrastructures rely on centralized databases, complicating integration without workflow disruption [17]. Middleware solutions bridging these systems are largely theoretical and lack real-world deployment [18]. Additionally, the high energy consumption of blockchain, particularly PoW-based systems, remains unsustainable for large-scale healthcare use [19]. Hybrid models combining off-chain data storage with on-chain verification offer a potential pathway to optimize performance and reduce energy usage [20]. Regulatory compliance also presents barriers. Many blockchain solutions fail to meet standards like HIPAA and GDPR due to issues with data anonymization and consent management [21]. Limited empirical validation and testing in diverse healthcare settings further hinder the development of compliant frameworks [22]. III. METHODOLOGY This study adopts a design science methodology to develop and evaluate a blockchain-based healthcare data management framework. The methodology integrates theoretical modeling with experimental validation to ensure that the proposed system meets the objectives of data security, privacy, scalability, and interoperability in healthcare systems. This section explains the research design, data collection methods, and evaluation metrics employed throughout the study. Fig.1 shows the research flow. Authorized licensed use limited to: MULTIMEDIA UNIVERSITY. Downloaded on December 19,2025 at 22:53:00 UTC from IEEE Xplore. Restrictions apply. Fig. 1. Research Flow The research employs a multi-phase approach to address challenges in healthcare data management. It begins with identifying key issues like security vulnerabilities, privacy concerns, and scalability bottlenecks. A blockchain-based framework is then designed, integrating encryption algorithms, smart contracts, and role-based access control (RBAC) mechanisms to ensure secure storage, automated permissions, and transparent transactions. The framework is implemented in a simulated healthcare environment to test its performance under varying scenarios. Finally, its efficiency and reliability are evaluated using metrics such as latency, encryption strength, and transaction throughput, providing quantitative insights into its effectiveness. A. Data Collection Data collection played a central role in evaluating the proposed blockchain-based healthcare framework. Table I shows data collection overview. TABLE I DATA COLLECTION OVERVIEW Dataset Type Description Purpose Synthetic EHR Dataset Simulated patient records and blockchain transactions. Validate system under realistic data loads. Security Event Logs Logs of security breaches and unauthorized access attempts. Evaluate access control effectiveness. Performance Logs Transaction throughput, latency, and encryption metrics. Assess system performance and scalability. The study employed synthetic datasets, real-world datasets, and performance logs to ensure robustness and reliability. Synthetic data, generated using tools like Synthea, included simulated Electronic Health Records (EHRs) and blockchain transactions, providing a controlled testing environment. Realworld datasets, including patient security logs and access control records from healthcare databases, added authenticity and addressed practical challenges. Performance logs captured key metrics such as transaction frequency, latency, and encryption performance during simulations. This data-driven approach allowed for objective evaluation of the framework's responsiveness, scalability, and efficiency, ensuring alignment with the study's overarching research goals. IV. A NALYSIS AND DISCUSSION This section presents a comprehensive analysis and discussion of the findings from the study, focusing on the performance, efficiency, and resilience of the proposed blockchain-based healthcare data management framework. The analysis evaluates key performance indicators, including transaction latency, throughput, encryption performance, data integrity, access control compliance, and scalability, comparing them against traditional centralized healthcare data management systems. A. Simulation Test Results and Findings The simulation tests conducted in this study provided valuable insights into the performance, efficiency, and resilience of the proposed blockchain-based healthcare data management framework. Figure 2 shows the transaction latency across conditions. These findings are critical in understanding how the system behaves under different operational scenarios, including normal workloads, peak traffic, and extreme stress conditions. The evaluation focused on four key metrics: transaction latency, throughput, encryption performance, and system stability, each of which revealed important outcomes. Transaction Latency emerged as a crucial performance metric, reflecting the time taken to validate and process transactions within the blockchain system. The results demonstrated an average transaction latency of 2.1 seconds, even when subjected to high transaction volumes. This low latency indicates that the blockchain system can handle transaction processing with remarkable efficiency, minimizing delays that could otherwise disrupt healthcare operations. Furthermore, latency remained consistently within acceptable limits across different simulated scenarios, including during peak traffic periods and concurrent data access requests. These results highlight the blockchain framework's ability to deliver timely responses, a feature that is particularly vital in healthcare settings where instant access to patient records can significantly impact medical decision-making. Fig. 2. Transaction Latency Across Conditions Phase •Key Tools/Methods Problem Identification •Problem analysis, vulnerability assessment Framework Design •Encryption algorithms, smart contracts, RBAC Implementation •Blockchain simulation tools, controlled environments Evaluation •Quantitative metrics: latency, throughput, encryption strength Authorized licensed use limited to: MULTIMEDIA UNIVERSITY. Downloaded on December 19,2025 at 22:53:00 UTC from IEEE Xplore. Restrictions apply. Throughput, measured as the number of transactions the system can process per second, is another critical indicator of performance. Figure 3 shows the throughput across conditions. During normal operating conditions, the blockchain system achieved an impressive throughput of 1,200 transactions per second (TPS). This high processing capacity underscores the system's ability to handle substantial transaction loads efficiently. Under peak stress conditions, where the network was intentionally subjected to heavy concurrent access requests, throughput experienced a slight decline to 900 TPS. However, even at this reduced level, the system exceeded the industry benchmark of 800 TPS, maintaining consistent performance without major slowdowns. These results emphasize the blockchain framework's scalability and reliability in managing high transaction volumes, ensuring smooth operations even during times of increased demand. Fig. 3. Throughput Across Conditions The blockchain system demonstrated robust encryption performance and system stability, making it well-suited for healthcare data management. Utilizing AES-256 encryption and SHA-3 hashing, the system ensured data security and integrity while maintaining efficiency. Encryption and decryption speeds of 1.5 MB/s and 1.7 MB/s, alongside a hashing speed of 200 MB/s, highlighted the framework's ability to protect sensitive data without compromising responsiveness. This dual-layer cryptographic approach is critical for processing large volumes of healthcare data swiftly and securely. System stability was thoroughly assessed under various stress scenarios. The blockchain framework maintained consistent performance with no transaction loss or data corruption, even under extreme conditions such as high concurrent access requests and peak transaction loads. Its resilience and reliability make it a strong candidate for deployment in large-scale healthcare environments where uninterrupted service is essential. Key findings emphasized the system’s efficiency and scalability, handling 1,200 transactions per second (TPS) under normal conditions and 900 TPS during stress scenarios as shown in Table II. This scalability ensures the framework can accommodate growing demands as healthcare networks expand. Additionally, the system's low latency of 2.1 seconds allows real-time access to electronic health records (EHRs), which is crucial in emergencies for timely medical decision-making. B. Key Findings and Results The study compared blockchain-based healthcare data management systems to traditional centralized systems, focusing on latency, throughput, data integrity, and access control enforcement. Blockchain systems showed slightly higher latency under normal conditions (2.1s vs. 1.8s) but maintained better stability under peak loads, with latency increasing only to 3.5s compared to 4.2s for centralized systems. Blockchain throughput also exceeded traditional systems, handling 1,200 transactions per second (TPS) during normal operations and 900 TPS under stress, compared to 1,100 TPS and 800 TPS, respectively, for centralized systems. Blockchain's immutable ledger achieved 100% data integrity, outperforming centralized systems, which reported a 0.5% error rate due to crashes and verification failures. Automated access control through smart contracts ensured 100% compliance with role-based policies, surpassing the 85% compliance of centralized systems, which relied on manual enforcement. These findings underscore blockchain’s advantages in resilience, scalability, and transparency. Stress testing revealed the blockchain system's ability to handle up to 10,000 concurrent transactions while maintaining low latency (4.8s under peak conditions) and an error rate below 0.1%. Despite heavy workloads, data integrity and transaction accuracy remained stable. The system also demonstrated self-recovery capabilities, dynamically reallocating resources to stabilize performance during overloads. Practical implications include improved reliability for large-scale healthcare networks, ensuring uninterrupted data access during crises and high-traffic periods. Adaptive algorithms optimized resource distribution, reducing bottlenecks and maintaining performance stability. Blockchain's resilience, scalability, and compliance with privacy regulations such as HIPAA and GDPR position it as a robust solution for secure healthcare data management. In conclusion, blockchain outperforms traditional systems in key areas, offering a scalable, reliable, and secure framework suitable for high-pressure healthcare environments. Its ability to process large transaction volumes efficiently and maintain system integrity under stress makes it a promising technology for modern healthcare needs. The validation results provide a comprehensive comparison between the blockchainbased healthcare data management framework and traditional centralized systems, highlighting significant performance differences across critical metrics. Table II presents an abstract of the analysis results. Authorized licensed use limited to: MULTIMEDIA UNIVERSITY. Downloaded on December 19,2025 at 22:53:00 UTC from IEEE Xplore. Restrictions apply. TABLE II SUMMARY OF ANALYSIS RESULTS Metric Blockchain Framework Centralized System Significance Latency (avg) 2.1 seconds 1.8 seconds Fast response for realtime access Latency (peak) 3.5 seconds 4.2 seconds Stable performance under load Throughput (avg) 1,200 TPS 950 TPS Handles large transaction volumes Throughput (peak) 900 TPS 800 TPS Scalable under peak load Encryption Speed 1.5 MB/s (AES-256) 1.2 MB/s Secure data encryption Hashing Speed 200 MB/s (SHA-3) 180 MB/s Data integrity assurance Data Integrity 100% 99.5% Guaranteed tamperproof records Access Control Compliance 100% 85% Automated policy enforcement System Errors <0.1% 0.5% High resilience to failures Concurrent Transactions (max) 10,000 8,000 Robust scalability The blockchain-based healthcare data management framework aligns with existing literature while showcasing notable advancements. It achieved an average latency of 2.1 seconds and a peak latency of 3.5 seconds, outperforming centralized systems under stress, where latency peaked at 4.2 seconds due to bottlenecks [23]. Blockchain’s throughput (1,200 TPS normal, 900 TPS peak) exceeded centralized systems (950 TPS normal, 800 TPS peak), demonstrating superior scalability and consistent performance with optimized consensus protocols [24]. The framework ensured 100% data integrity, supported by AES-256 encryption (1.5 MB/s) and SHA-3 hashing (200 MB/s), aligning with findings that emphasize blockchain’s immutability through cryptographic mechanisms [25]. Traditional systems, while achieving 99.5% data integrity, were more prone to errors and data loss. Blockchain balances encryption efficiency with responsiveness, countering concerns about computational overhead [26]. Access control compliance reached 100% with blockchain’s smart contracts, automating enforcement of rolebased policies and reducing manual errors, compared to 85% compliance in centralized systems [27]. This supports prior studies highlighting smart contracts’ effectiveness in improving compliance with standards like HIPAA and GDPR [28]. Under stress, the blockchain system handled up to 10,000 concurrent transactions with an error rate below 0.1%, compared to 8,000 transactions and 0.5% errors in centralized systems. It also demonstrated self-repairing capabilities, autonomously recovering from overloads, reinforcing its resilience and scalability [29][30]. While earlier research raised concerns about scalability, this study shows blockchain can achieve robust performance with protocol optimizations [31]. The findings validate blockchain’s potential as a transformative solution for healthcare, addressing latency, security, and compliance challenges [32]. The framework reduces administrative overhead and costs through smart contract automation, enhancing operational efficiency [33]. However, energy consumption and environmental impact remain unexplored areas for future research [34][35]. In summary, blockchain offers a scalable, secure, and efficient framework for healthcare data management, addressing longstanding challenges and aligning with evolving technological demands. Future research should explore integration with emerging technologies like AI and IoT to further enhance its capabilities. V. CONCLUSION This study presents a significant advancement in the field of healthcare data management by proposing a novel blockchain-based framework that effectively overcomes the inherent limitations of traditional centralized systems. These conventional systems often struggle with issues such as data security vulnerabilities, inefficient access control mechanisms, and poor scalability, which hinder effective and secure data sharing in the healthcare domain. The blockchain framework developed in this research addresses these challenges through a comprehensive design that integrates AES-256 encryption, SHA-3 hashing algorithms, and smart contracts for automated role-based access control. The performance of the proposed system was rigorously evaluated through simulation testing using synthetic Electronic Health Record (EHR) datasets, reflecting real-world healthcare workloads. The results demonstrated exceptional performance, with the system achieving 100% data integrity, an average transaction latency of only 2.1 seconds, and a throughput of up to 1,200 transactions per second. Furthermore, the framework showed strong compliance with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR), which are critical for the legal and ethical management of patient data. These findings suggest that the blockchain model is not only technically sound but also practically viable for deployment in healthcare institutions. In addition to these accomplishments, the study contributes to the field by offering empirical evidence that supports blockchain’s ability to handle high transaction volumes while maintaining system integrity and responsiveness. It validates the scalability, resilience, and transparency of blockchain systems, positioning them as superior alternatives to traditional architectures in healthcare data environments. However, the research is not without limitations. One major constraint is that the evaluation was carried out in a controlled, simulated environment using synthetic and partially real-world data. As a result, the framework's performance may vary when applied in diverse and complex real-world healthcare settings. Moreover, the study did not examine the energy consumption and environmental sustainability of the blockchain system, which are critical considerations given the high computational demands of some blockchain consensus mechanisms. Another limitation is that while integration challenges with legacy healthcare IT systems were acknowledged, they were not Authorized licensed use limited to: MULTIMEDIA UNIVERSITY. Downloaded on December 19,2025 at 22:53:00 UTC from IEEE Xplore. Restrictions apply. empirically tested, leaving questions about practical interoperability unanswered. To build upon the findings of this study, future research should prioritize pilot testing in real-world healthcare institutions. Such implementations would help validate the system’s integration capabilities and its adaptability across different healthcare infrastructures. Additionally, further investigation into energy-efficient consensus mechanisms is necessary to ensure that blockchain adoption in healthcare does not come at the cost of environmental sustainability. Researchers are also encouraged to explore synergies between blockchain and emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT), which could amplify the framework’s capabilities in terms of predictive analytics, automation, and smart monitoring. In conclusion, the study successfully demonstrates that blockchain technology holds transformative potential for healthcare data management. By offering a scalable, secure, and regulation-compliant solution, it lays the groundwork for a more efficient and trustworthy digital health ecosystem. 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