Case Report Forms (CRFs) are essential tools in clinical trials, serving as the primary mechanism for systematic and standardized patient data collection. This chapter discusses the critical role of CRFs in maintaining data integrity, supporting regulatory compliance, and ensuring the accuracy and consistency of clinical trial results. The chapter provides an in-depth exploration of the key principles involved in designing CRFs, including user-centric design, data standardization, and error management. It contrasts the features of well-designed and poorly designed CRFs, highlighting the significant impact that effective CRF design has on clinical trial outcomes. Additionally, the chapter addresses the evolution of CRFs, particularly the transition from paper-based systems to electronic Case Report Forms (eCRFs), and their benefits, including real-time data validation, enhanced accessibility, and compliance with regulatory guidelines. The impact of technological advances such as artificial intelligence, blockchain, and decentralized trials on the future of CRF processes is also discussed. Finally, a sample CRF is presented, providing a practical example of a well-designed form for clinical data collection in a controlled study.
Since announcing the implementation of a single electronic health record for all South Africans, the government has not yet informed healthcare facilities of how this would be accomplished. The siloed South African healthcare system would have to be redesigned to accommodate a single electronic health record. A systematic literature review conducted across three databases returned 9 790 results. By applying ten filters, 22 documents were eventually retrieved for analysis. The analysis showed that existing research focuses on healthcare architectures from a theoretical perspective. Therefore, the literature review revealed a practically based research deficiency and a lack of theoretical studies merged with practical cases. Seeking to enhance the understanding of designing a single electronic health record, the documents were analysed using a qualitative inductive content analysis technique, revealing that a single electronic health record cannot be formulated using a fully centralised architecture as this is not practical. A fully decentralised architecture, such as blockchain, is equally infeasible because this requires significant changes to the existing systems and infrastructure and would require re-skilling system builders. Since the South African healthcare architecture is already decentralised, hybrid architecture incorporating edge computing with clusters of systems and information that connect using middleware should be considered.
Newborn misidentification poses serious patient safety and accountability problems, but errors can be traced through the use of a blockchain to create an audit trail. However, a blockchain storing raw or even hashed biometric templates for individual identities is not acceptable for privacy reasons. This work redefines our prior work (1) to form a privacy-preserving audit protocol that isolates the processes of capturing a biometric and matching it against a database of known identities to an external Service Provider and the processing of the blockchain to a permissioned Ledger that contains only pseudonymous audit commitments related to keyed entries on the Ledger. This work describes an implementation of this protocol in Solidity 0.8.30 and provides metrics for the gas use and latency of the smart contract for 100 iterations of 100 total Enrollment and Verification Workflows each. Twenty Adversarial Functional Tests are also described that attempt to place the system into an invalid state, as well as four additional tests that assess the effect of batched submission to the smart contract of multiple keyed audit commitments. The smart contract processing throughput is also determined for a batch of submissions, finding a maximum local throughput of 60.2 tx/s. A further 50,000 randomized reference-model transitions of the systemâs internal reference-model were then made (involving a total of 57,345,087 invariant checks, all of which passed), as well as a measurement of the time taken to generate an HMAC-SHA-256-sized commitment for 10,000 iterations (local median time = 0.002 ms). The results of this work provide a solid foundation for the blockchain component of BIBIS, but it is not intended to provide any insights into the accuracy of neonatal biometric matching, the presentation attack resistance of the system, or even the usability of BIBIS by clinical end-users. The results also do not comment on the finality of QBFT-based commits to a blockchain.
Introduction Electronic health records (EHRs) are central to healthcare analytics, but their granularity increases re-identification risk when shared. Conventional privacy-preserving methods including k -anonymity, l -diversity, and differential privacy often protect confidentiality at the expense of analytical utility by weakening clinically meaningful correlations. Methods We propose SENTINEL-Chain, a blockchain-integrated privacy-preserving framework for secure EHR publishing. The privacy layer combines six mechanisms: Adaptive Correlation-Aware Perturbation (ACAP), Hierarchical Multi-Granularity Generalization (HMGG), Semantic-Aware Anatomization (SAA), Probabilistic Suppression with Utility Bounds (PSUB), Geo-Temporal Indistinguishability (GTI), and Ensemble Privacy Composition (EPC). The blockchain layer adds Merkle Hash Tree verification, PBFT-based validation, zero-knowledge proof compliance checking, and smart contract-based access control. Evaluation used a synthetic dataset (10,000 records) and two real clinical benchmarks (Wisconsin Breast Cancer, N = 569; Diabetes, N = 442). Results SENTINEL-Chain attains a privacy score of 79.9% and utility of 98.2%, producing a combined score of 178.1% that exceeds all 16 baselines by 4%-95%. Correlation fidelity reaches 99.9% for claim amounts, 99.6% for length of stay, 99.7% for age, and 99.1% for severity indices. The framework shows 100% resistance to record linkage attacks, with membership inference attacker advantage below the random guessing baseline. The blockchain layer processes 9,988 transactions in 101 blocks with complete integrity verification. Formal Renyi DP composition yields Îľ = 7.08 ( δ = 10 â5 ), and throughput reaches approximately 3,600 records/second up to one million records. Discussion SENTINEL-Chain addresses five identified gaps in healthcare data publishing: correlation destruction, the privacy-blockchain disconnect, single-technique brittleness, verification without disclosure, and limited attack resistance evaluation. Smart contract gas estimation on Ethereum indicates a per-record registration cost of 61,895 gas units; Layer-2 deployment would reduce costs by 10-100x.
Vanessa Sophia Cunha, Paul J. Diefenbach, Emil Polyak
This thesis explores the design and development of CLS Nexus, an AI-assisted clinical decision-support platform built for Child Life Specialists (CLS) in pediatric healthcare settings. The project addresses a documented gap in the field: despite a substantive evidence base for psychosocial intervention in pediatric care, no purpose-built digital framework exists to support specialists in organizing, discovering, and personalizing therapeutic activities at an institutional level. CLS Nexus is a WordPress-based proof-of-concept built with an endpoint-agnostic AI integration layer, using the Anthropic API with Claude Sonnet as the demonstration model, with the architecture designed to support institutional deployment without changes to the application layer. A particular focus was placed on positioning AI as a tool that extends specialist judgment rather than replacing it. The methodology employs a design-based research approach progressing through three iterative platform concepts, each of which produced design knowledge that informed the next, culminating in a fully functional proof-of-concept system. The platform encompasses two integrated AI systems: System 1, an automated content tagging pipeline that analyzes uploaded clinical materials across twenty-seven dimensions using a purpose-built pediatric psychology-informed taxonomy; and System 2, a structured patient intake advisor that scores candidate interventions against individual patient profiles using a zero-to-five star rating system with explicit flags across thirteen psychological categories. The platform's design, prompt engineering decisions, and clinical taxonomy structure are documented as academically significant artifacts throughout. Expert validation was conducted through a two-track asynchronous survey methodology, with healthcare professionals with clinical backgrounds evaluating the system's clinical credibility and taxonomy design, and digital media practitioners evaluating its information architecture, AI integration, and ethical positioning. The project contributes a concrete, ethically grounded example of how AI can be integrated into provider-facing clinical tools, demonstrating that meaningful personalization and clinical decision-support capability can be achieved through accessible platform infrastructure without displacing the specialist judgment that makes psychosocial care most effective.
Open access
Digital Mental Health Interventions
Electronic Health Records Systems
Artificial Intelligence in Healthcare and Education
⢠Address interoperability limits in healthcare supply chains. ⢠Use NFTs as identifiers that reference patient data distributed across multiple blockchains. ⢠Propose a Selective Trust Architecture, with patient-controlled access via token ownership. ⢠Introduce the concept of Layer-2 Interoperability in decentralized healthcare systems. ⢠Link artifact design to theory development in blockchain and trust research.
Momodu Mustapha, Susan Konyeha, Akinola Samson Olayinka
This study examines user trust and perception of cryptographic technologies specifically SHA3-512 hashing, SERPENT encryption, and Zero-Knowledge Proofs (ZKP) in the context of centralized Electronic Health Record (EHR) systems. As healthcare institutions increasingly migrate patient data to digital platforms, the security and privacy properties of underlying cryptographic mechanisms have become critical determinants of user confidence and system adoption. Using a quantitative, survey-based design, data were collected from 92 healthcare practitioners, IT professionals, and system administrators actively engaged with EHR systems in Auchi, Nigeria. A Random Forest classifier was trained to predict perceived satisfaction levels (Low, Neutral, High) based on respondents' assessments of cryptographic effectiveness, usability, and trust. Results indicate that trust in ZKP is the strongest predictor of overall perception, followed by confidence in SERPENT encryption and SHA3-512 integrity guarantees. The model achieved a classification accuracy of 63.3% on a held-out test set derived from this exploratory sample, with a Kappa statistic of 0.52 reflecting moderate agreement beyond chance. Balanced accuracy across classes (approximately 0.49â0.50) and low per class sensitivity confirm that the findings should be interpreted as preliminary and directional rather than definitive. Key themes from open ended feedback analyzed using TF-IDF text mining reveal that while respondents broadly recognize the security value of these cryptographic mechanisms, concerns about system slowdown, usability complexity, and insufficient user education present barriers to wider adoption. This study contributes a pilot-level empirical baseline for understanding stakeholder perception of layered cryptographic security in resource-constrained healthcare environments, and highlights the need for larger-scale replication studies. Keywords: SHA3-512; SERPENT encryption; Zero-Knowledge Proofs; healthcare data security; user perception; Electronic Health Records; Random Forest
Background: Healthcare organizations face unprecedented challenges in maintaining process compliance due to increasingly federated data and systems topologies, coupled with complex state, federal, and jurisdictional regulatory compliance and verification requirements. The emergence of distributed ledger technology (DLT) and artificial intelligence presents both transformative opportunities and significant compliance challenges. These emerging technologies enable computing paradigms that shift toward data locality models where computational models meet the data rather than moving sensitive patient information across organizational boundaries. This computational approach offers innovative pathways to mitigate data breach risks, while simultaneously introducing new verification complexities as the underlying technologies continue to advance: healthcare entities must cryptographically prove that operations performed on locally-held data were executed according to approved specifications while enabling selective disclosure capabilities across entity lines. However, traditional verification mechanisms lack the cryptographic guarantees necessary for these privacy-preserving, multi-entity healthcare workflows, creating substantial risks in clinical decision-making, patient privacy, and regulatory adherence. Objective: This paper introduces the ZK-PRET Business Process Prover framework that integrates Object Management Group (OMG) business process standards with zero-knowledge cryptographic verification to enable privacy-preserving healthcare process compliance across distributed systems. Methods: We developed a multi-layer architecture combining formal business process modeling, zero-knowledge proof generation, and regulatory compliance verification. The framework extends established OMG standards with cryptographic verification capabilities to achieve verifiable compliance, privacy preservation, and regulatory accountability. Implementation testing was conducted in synthetic data environments designed to represent real-world healthcare scenarios.š These environments enable comprehensive modeling and testing of multi-entity process orchestration patterns while maintaining privacy protections essential for healthcare research and development. All scenarios, clinical examples, and process expressions presented in this paper utilize synthetic data to ensure no real patient data, clinical records, or identifiable health information was used. Results: The ZK-PRET Business Process Prover framework demonstrates practical applicability across many healthcare domains including treatment planning, telemedicine coordination, healthcare administration, consumer health services, multi-entity clinical trials, and supply chain management. Implementation results demonstrate cryptographic verification capabilities that enable mathematical prevention of regulatory violations rather than post-hoc detection. The results demonstrate configurable privacy preservation through zero-knowledge verification and consistent proof sizes suitable for modeling complex orchestrations, while leveraging already widely used Web 2 process models, suitable for multiple runtime deployment topologies. Conclusions: Zero-knowledge healthcare process verification represents a foundational technology for regulatory compliance in distributed healthcare systems. While agentic AI systems present important opportunities for automation, the underlying requirement for verifiable process compliance through cryptographic means brings broader challenges. ZK-PRET Business Process Prover addresses these challenges in healthcare transformative flows, enabling safer deployment of autonomous systems while maintaining regulatory standards.
Tina Yi Jin Hsieh, Carl Eriksson, Garth Meckler, Matthew Hansen ¡ 12 authors
Introduction and Objective: Traditional adverse safety events (ASE) identification relies on domain experts to manually review and annotate charts, which hinders the scalability of processing high-volume EMS data. This study explores the use of large language model (LLM) with a knowledge base to automate extraction of adverse safety events (ASE) from unstructured emergency medical service (EMS) notes for pediatric out-of-hospital cardiac arrest (OHCA) as proof of concept. Data Sources and Study Design: Pediatric OHCA records from a national EMS provider were obtained from 2017 to 2020. Leveraging the Pediatric Prehospital Adverse Safety Event Detection System (PEDS) as a foundational knowledge base, we used the LinkML framework to develop an ontology to define ASEs across six essential EMS care domains. To convert unstructured EMS narratives into structured prompts, we used the Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES) method, which generated schema-driven prompts to guide the GPT-3.5 model in identifying ASEs. By mapping unstructured data into structured concepts consistent with PEDS guidelines, the model produced targeted prompts that supported effective entity extraction. Results: We evaluated framework effectiveness with accuracy, recall, precision, F1 score, and specificity across 42 pediatric OHCA cases covering ASE-related entities. RescueGPT showed high accuracy in detecting common ASEs (Patient Rhythm, Age, Weight, Length) but revealed challenges in rare events (Failure to Establish IV Access, Incorrect Airway Equipment Size, Failure to Ventilate Patient) likely due to more inconsistent and complex documentation. Conclusions: RescueGPT demonstrates potential in scaling automated ASE detection, but performance varies by completeness and clarity of EMS narrative, particularly with rare events. Fragmented clinical documentation limits accuracy and highlights the need for standardized collection protocols in EMS systems. Future directions will focus on implementing rebalancing strategies for rare events, applying explainability methods to improve decision-making transparency, and refining text segmentation techniques to handle mixed outcomes to further improve performance.
The Distributed Medical Report Management System using Blockchain Technology is designed to revolutionize the management and sharing of medical reports by integrating blockchain technology, specifically targeting data security and patient privacy.This system enhances the protection of Electronic Health Records (EHR) through sophisticated cryptographic methods and decentralized storage solutions.It consists of four core modules-Hospital, Doctor, Patient, and Receptionist-each tailored to manage specific healthcare tasks.The Hospital Module enables secure management of patient records and administrative tasks, creating a transparent and unchangeable ledger that boosts data integrity and builds trust.The Doctor Mod-ule allows healthcare professionals to securely access and update patient records, with cryptographic safeguards ensuring that only authorized users can see or alter sensitive information, significantly reducing the risk of data breaches.The Patient Module empowers individuals by providing direct access to their medical records, allowing them to monitor their health history and control who can view their data.The Receptionist Module streamlines administrative functions, such as appointment scheduling, while securely logging all interactions on the blockchain.By replacing traditional paper-based systems, this blockchain-centric approach offers a more secure, efficient, and user-friendly method for managing sensitive patient data.Advatages include enhanced security through blockchain protection, decentralized storage minimizing risks of data loss, improved accessibility for authorized healthcare providers, and increased patient control over their health information.Ultimately, the Distributed Medical Report Management System demonstrates the transforma-tive potential of blockchain technology in creating a secure framework for managing patient records, addressing challenges in healthcare data management, and paving the way for a more efficient healthcare system.
The objective of this work is to present a decentralized healthcare records management system. The system is built on the Ethereum blockchain using Solidity smart contracts and a React-based web interface. And this system addresses security, integrity, and privacy limitations of traditional centralized electronic health record (EHR) systems. By enforcing the role-based access control, immutable storage of patient records, and transparent audit trails for all operations. A smart contract âHealthcareRecordsâ manages patient data operations and provider authorization, while a MetaMask-integrated frontend enables secure interaction with the blockchain through an intuitive interface. The local Hardhat Ethereum network is used to deploy the proposed work, outcome of the prototype demonstrates a secure healthcare record creation and retrieval of the patient data. This local network demonstrating the strict access control, low gas consumptions and low latency, which are suitable for real usages in healthcare environments.
Asmart-contract framework for patient identity management in digital health platforms. A major gap in current digital health ecosystems is the absence of a portable and verifiable patient identity layer across fragmented electronic health record (EHR) systems. The problem addressed is the lack of a portable, verifiable, and patient-centric identity layer across fragmented electronic health record systems, which weakens access accountability and privacy. The proposed solution couples fast healthcare interoperability resources (FHIR) with self-sovereign identity (SSI), storing FHIR payloads off-chain in the InterPlanetary file system (IPFS) and committing only encrypted pointers and policies on Polygon smart contracts. Patient identifiers and content addresses are protected with AES-256 GCMauthenticated encryption and elliptic-curve key wrapping (ECIES) for both the healthcare administrator and the patient. A web implementation in Next.js using thirdweb automates wallet creation, keystore handling, encryption, and on-chain commits. In evaluation with 50 synthetic registrations, success reached 100 percent, median end-to-end latency was 5.86 seconds, mean on-chain latency 3.77 seconds, average transaction fee 0.0401 POL/MATIC, encryption time 13.9 milliseconds, and all decryptions validated. The results indicate practical feasibility for portable identity and auditable access, with on-chain latency as the main bottleneck to be reduced through batching, cheaper layers, and broader field trials. However, this study is limited because the evaluation uses only synthetic data and singleprovider testing, without real-world patients or multi-institutional environments. Zero-knowledge proofs (ZKP) are discussed conceptually as future integration and are not implemented or benchmarked in this work.
Hari Purnama, I Putu Bakta Hari Sudewa, Tazkia Nizami, Bagas Sambega Rosyada ¡ 6 authors
Electronic Medical Records (EMRs) are mandatory in Indonesia following the Ministry of Health regulation, which raises significant challenges in data security and patient-centric access control. Current implementations rely on centralized healthcare systems or third-party vendors, creating risks of unauthorized access, data leakage, and uncertain data integrity. To address these issues, this study proposes DecMed, a decentralized EMR management framework built on IOTA Distributed Ledger Technology (DLT). DecMed integrates Capability-Based Access Control (CapBAC), Proxy Re-Encryption (PRE), and the InterPlanetary File System (IPFS) to enforce patient ownership of medical data. Patients actively grant or revoke access, define access duration, and selectively share data with healthcare personnel. The system is implemented using smart contracts in the Move programming language on the IOTA ledger, while encrypted clinical data is stored on IPFS. Evaluation through unit testing of various unauthorized access scenarios demonstrates that DecMed effectively enforces fine-grained access rules, preserves data confidentiality and integrity, and ensures compliance with national healthcare requirements.
Digital transformation is reshaping healthcare systems worldwide, with nursing practice positioned at the forefront of technology-driven innovation. As nurses increasingly engage in data management, coordination of care, and decision-making across complex health systems, the need for secure, transparent, and trustworthy digital infrastructures has become paramount (Khezr et al., 2019). Among emerging technologies, blockchain, a decentralised and tamper-resistant ledger system, and smart contracts, which enable automated and verifiable transactions, present promising opportunities to enhance trust, accountability, and interoperability within nursing workflows (Khezr et al., 2019; Saeed et al., 2022). Smart contractâs core features: immutability, transparency, and decentralisation, make it particularly suited for addressing long-standing challenges in healthcare data management and nursing administration. In nursing contexts, potential applications include secure sharing of patient records, real-time tracking of clinical documentation, automated credential verification, and protection of consent and privacy (Kuo et al., 2017; Naresh et al., 2025). It may further facilitate process automation in areas such as nursing resource allocation, performance auditing, and continuing education accreditation. Despite its promise, the adoption of blockchain technology in nursing remains in its infancy. Most studies have centered on technical or conceptual models rather than empirical evaluations, and few have examined the direct or indirect impacts on nursing efficiency, patient safety, or care coordination (Hasselgren et al., 2020). Implementation challenges, such as scalability, interoperability with existing hospital information systems, regulatory ambiguity, and user acceptance also persist (Saeed et al., 2022). Moreover, the ethical implications related to data ownership and governance in decentralised environments warrant careful consideration in nursing settings. Given these gaps, a scoping review is needed to synthesise current evidence on the applications and impacts of blockchain and smart contract technologies in nursing practice. This review will explore their roles across nursing service delivery, management, and education, with particular attention to how these technologies influence efficiency, trust, accountability, and data security. By consolidating interdisciplinary insights, this study seeks to guide future research and inform policy and practice frameworks for integrating blockchain and smart contract technologies into the nursing profession.
Yan Watequlis Syaifudin, Vipkas Al Hadid Firdaus, Imam Fahrur Rozi, Chandrasena Setiadi ¡ 8 authors
The digitization of health records has enhanced clinical efficiency, but amplified risks related to data privacy, integrity, and auditability.While permissioned blockchains offer immutability and traceability, they often fail to reconcile transparency with confidentiality-either exposing sensitive data or obscuring it beyond regulatory scrutiny.To address this gap, this paper presents an integrated framework that combines Zero-Knowledge Proofs (ZKPs) with a permissioned blockchain to enable verifiable yet private healthcare transactions.A visit centric Electronic Health Record (EHR) model supports three real-world use cases: medication validity, procedure confirmation, and demographic verification.A four-layer architecture decouples data, application logic, cryptographic trust, and audit logging, allowing end-to-end validation without raw data disclosure.Experimental evaluation across three ZKP libraries (snarkJS, ZoKrates, and gnark) on a synthetic dataset of 1,000 patient visits demonstrates sub-500 ms verification latency, with snarkJS selected for its ecosystem compatibility despite slower raw performance.End-to-end pipeline latency averages 1.35 s, confirming feasibility for batch workflows such as insurance claims.The system further includes a web-based auditor interface that validates tamper-evidence under off-chain attacks, bridging cryptographic guarantees with operational compliance.
Communication and information technologies have facilitated the rapid adoption of electronic medical records, leading to patient privacy and data security concerns. Blockchain technology offers a promising solution to address these issues. However, scalability remains a significant challenge for blockchain-based electronic health records (EHR) systems. In this study, we aimed to develop and evaluate an EHR management system based on blockchain technology. Therefore, we propose a management model based on organizations and user roles and implemented it using Hyperledger Fabric and the InterPlanetary File System (IPFS). The blockchain consists of three channels: one for patient registration and EHR retrieval and two additional channels dedicated to two hospitals for storing patientsâ EHRs. A scalable multichain e-health system using the Hyperledger Fabric platform provides a practical option to address scalability issues and protect patientsâ privacy, security, and medical data. The proposed model uses IPFS to store medical images and generate hash values, which are then stored in the blockchain. The system was evaluated using Hyperledger Explorer and Hyperledger Caliper, focusing on several performance metrics: transactions per hour, transactions per minute, blocks per hour, blocks per minute, response time, maximum latency, minimum latency, average latency, throughput, CPU and memory usage, and runtime. A comparative analysis was conducted against single-ledger EHR systems to assess the proposed systemâs performance. The Hyperledger Caliper report shows that the average latency for each organization ranges from 0.11 to 0.55, and the throughput ranges from 24.2 to 200 for 1000 assets at sending rates of 25, 50, 100, and 200.
Efficiently matching patients to clinical trials is essential for advancing medical research and ensuring reliable outcomes. However, current matching methods face several challenges. These include data integrity issues from tampered records, privacy risks caused by weak anonymization, and manual processes that delay recruitment. In addition, centralized systems lack transparency, expose sensitive patient data to security vulnerabilities, and suffer from single points of failure that reduce resilience and trust. In this paper, we propose a blockchain and Large Language Models (LLMs)-driven solution for secure, trustworthy, traceable, decentralized, and transparent patientâclinical trial matching. Blockchain ensures data integrity, security, and transparency by eliminating single points of failure and enabling tamper-proof records. LLMs enhance patientâtrial matching by automating the interpretation of complex eligibility criteria, improving accuracy, and significantly reducing the time required for manual review. Our approach uses Ethereum-based smart contracts to automate workflows such as trial registration, eligibility assessment, and consent tracking. We fine-tune GPT-4, T5, and Gemini on synthetic data derived from real clinical trial records and employ majority voting to ensure consistent and unbiased eligibility decisions. A prototype Gradio interface was developed as a minimum viable product (MVP) to demonstrate seamless interaction between LLMs and smart contracts. Performance evaluation based on accuracy (0.800), precision (0.733), recall (1.000), and F1-score (0.846) demonstrates reliable eligibility prediction. Cost analysis confirms affordability, and security evaluation verifies resilience against known threats. Comparison with existing solutions highlights the frameworkâs advantages in transparency, trust, and automation. The smart contract code is publicly available on GitHub.
Qaisar Manzoor, Ch Anwar Ul Hassan, Ali Daud, Azhar Imran
In order to effectively follow up with a patient, it is essential to have a health record. The opinions, prescriptions, research, and any other data connected to the patient that are provided by medical professionals are included in this document. An individual or individuals, such as the patient, the physician, and the chemist, are taking part in the process of exchanging and managing this file. Those individuals who are authorized to do so are able to view the electronic health record (EHR) from any location, and the information contained inside the EHR is distributed across various health care providers. Under some conditions, such as those pertaining to privacy and security, the electronic health record (EHR) must be shared. On the other hand, the existing health care systems may be susceptible to system failures and hacking, which makes it difficult to deliver services that can be relied upon. Additionally, the characteristics of such systems make it difficult to exercise centralized control over admission requirements. The findings of this study propose a strategy that Ethereum may implement to promote the trading of EHR models. Ethereum allows for the addition of EHR partners to the route, which makes it simpler for individuals to communicate data with one another. Users are given the ability to decide how data can be accessed using attribute-based access control (ABAC), which may result in the system becoming more secure. It is possible to view any record that has been preserved on the blockchain by utilizing the Ethereum Fabric feature; however, the record cannot be altered or removed. This ensures that the data can be traced back to the specific source from which it originated. By utilizing proxy re encryption, which guarantees that data will not be disclosed while it is being shared, it is possible to guarantee the safety of the data.
Amit Sharma, K A Balaji, Jitha Janardhanan, Ranganathaswamy Madihalli Kenchappa ¡ 6 authors
With the rapid expansion of healthcare data, especially Electronic Health Records (EHRs), there are significant concerns about data security, privacy, interoperability, and accessibility. When utilizing conventional centralized EHR systems, patients usually lack transparency and control over their medical data. In order to handle data safely, this study proposes the B-DECIDE (Blockchain-Driven Electronic Health Record Control, Integrity, and Data Efficiency framework), a decision-making paradigm that leverages the immutability, decentralization, and cryptography aspects of blockchain technology. B-DECIDE evaluates a number of blockchain topologies, including as public, private, and consortium, together with consensus algorithms like Proof of Work (PoW) and Proof of Stake (PoS), in order to determine how effective healthcare is. The findings indicate that a hybrid on-chain/off-chain storage approach maintains scalability, enhances data integrity, reduces latency, and ensures regulatory compliance.