|Aymen Anwar||City Graduate School, City University, Petaling Jaya, Selangor, 46100, Malaysia|
|S.B. Goyal||City Graduate School, City University, Petaling Jaya, Selangor, 46100, Malaysia|
|Tony Jan||Centre for Artificial Intelligence Research and Optimization, Design and Creative Technology Vertical, Torrens University, Sydney 2007, Australia|
Clinical trials are crucial to medication research, but data security, transparency, and integrity issues often arise. Blockchain technology offers a decentralized, tamper-proof framework for clinical trial data management, promising to overcome these issues. Current blockchain-based clinical trial platforms lack scalability, interoperability, and integrity. A meta-model paradigm for blockchain-based clinical trial security and transparency addresses these constraints. The system employs a unique algorithm with smart contracts and consensus procedures to protect data privacy, reduce redundancy, and promote platform compatibility. The algorithm aims to maximize resource consumption and reduce computational overhead while ensuring security and trust. To improve security and transparency, we analyze the proposed meta-model framework utilizing performance, scalability, and security metrics and benchmarks. We observed that the meta-model framework and algorithm are efficient, scalable, and safe, laying the groundwork for future research. In particular, the framework can minimize clinical trial costs and time while improving data quality, traceability, and accountability. The suggested meta-model framework and algorithm can improve blockchain-based clinical trial security and transparency, making data management more trustworthy and efficient.
Blockchain; Clinical trials; Data privacy; Transparency; Smart contracts
Technology has made it possible to maintain a functioning society during the COVID-19 pandemic as it helps normalcy in day to day life with functioning remotely (Berawi, 2021). Blockchain technology has received attention in clinical trials for its promise to improve data security, transparency, and integrity (Berawi et al., 2021). Several studies have shown that blockchain can securely and transparently manage clinical trial data without modification (Manski and Turner, 2019b). Gao et al. (2019a) Hasan and Sengupta (2019a) propose blockchain-based clinical trial privacy and data exchange solutions. Blockchain research has been published in prominent journals, including IEEE Transactions on Services Computing (Sun, Zhang, and Lu, 2019) and BMC Medical Informatics and Decision Making (Sohn et al., 2020a) demonstrating its growing interest and promise in clinical trials. Despite these encouraging improvements, blockchain-based clinical trials require a complete framework to design and monitor. The significance of data confidentiality, transparency, and integrity in drug development studies drove blockchain research in clinical trials. Clinical trial data must be kept confidential for patient safety and regulatory compliance. Blockchain technology is an appealing option because it is decentralized and tamper-proof. However, due to its novelty in clinical trials, a solid foundation is needed for its implementation. Existing blockchain-based clinical trial systems have scalability, interoperability, and trustworthiness difficulties, according to the problem statement. A complete meta-model framework to improve blockchain-based clinical trial security and transparency is the research's goal. The research aims and questions underscore the need to identify and address existing shortcomings in blockchain-based clinical trial frameworks. Smart contracts and consensus processes will increase data privacy, redundancy, and platform compatibility in the meta-model framework. Evaluation metrics will compare the framework's performance, scalability, and security to existing methods.
2. Literature Review
This paper's literature review covers the latest blockchain technology, clinical trials, and blockchain-based clinical trials. In recent years, blockchain technology has been proposed to address clinical trial data security, transparency, and integrity issues. Nature Reviews Drug Discovery (Manski and Turner, 2019b); Journal of the American Medical Informatics Association (Gao et al., 2019a; Hasan and Sengupta, 2019a; Hasan and Sengupta, 2019b); IEEE Transactions on Services Computing (Sun, Zhang, and Lu, 2019); and BMC Medical Informatics and Decision Making (Sohn et al., 2020b) are among the high-impact factor journals reviewed in The paper emphasizes the main drawbacks of current methods and frameworks and discusses how blockchain technology could improve clinical trial data security and transparency.
Blockchain delivers data securely and transparently without intermediaries. A consensus mechanism verifies transactions in a peer-to-peer network where each node has the ledger. Blockchain transactions are encrypted to protect data. Clinical trial data requires high security and integrity, and the blockchain's immutability makes it ideal (Xiong and Wang, 2021; Li et al., 2018; Nakamoto, 2008).
2.2.2. Challenges and Limitations
In their work, Williams et al. (2022) discuss standard clinical trial methodologies and their problems. Rydzewska, Stewart, and Tierney (2022) explore transparency issues and the need for better data sharing.
Clinical trials have many drawbacks that can affect outcome quality, accuracy, and reliability. Key issues and constraints are listed below:
126.96.36.199. High Costs
Clinical studies can cost several hundred thousand to several billion dollars, depending on nature and size. High expenses can prevent smaller enterprises and academic institutions from participating and limit trial numbers.
188.8.131.52. Long Timelines
Clinical studies can take years and have distinct goals and timetables for each phase. Long timelines can delay drug development and approval and increase trial expenses.
184.108.40.206. Low Patient Participation
Clinical trials can be difficult to recruit and retain patients since many are unaware of or uninterested in participating. Some patients may not be eligible for the experiment, limiting the pool of possible participants.
220.127.116.11. Lack of Data Transparency
Clinical trial data is usually controlled by sponsors and unavailable to academics and stakeholders. Researchers may struggle to replicate or confirm study results and collaborate and share information without data transparency.
18.104.22.168. Data Privacy Concerns
Most clinical trial data involves sensitive patient health and medical information, presenting privacy and security concerns. Patient data might be compromised by email and file sharing.
22.214.171.124. Potential for Bias
Clinical trial bias affects reliability and accuracy. Design, participant selection, data analysis, and reporting can bias studies. Table 1 lists the literature on traditional, drawback-laden solutions.
Table 1 Summary of the limitations of using traditional methodology in clinical trials
Blockchain is being examined for clinical trials to address these challenges. Blockchain technology securely and transparently shares clinical trial data without tampering. Table 2 highlights some literature that uses Blockchain-based solutions to improve data transparency, privacy, security, clinical trial efficiency, and cost.
Table 2 Summary of selected studies on blockchain in clinical trials
Several studies shown in table 4, emphasise the need of reviewing and comparing blockchain technology techniques and frameworks in clinical trials to find the best solutions for certain use cases and circumstances.
Table 4 Summary of selected studies on comparison and evaluation of blockchain-based clinical trials
126.96.36.199. Gaps and research opportunities
Table 5 Summarizing for the Gaps and Research Opportunities
These issues and research opportunities suggest blockchain technology in clinical trials deserves greater study. These issues can be addressed to improve clinical trial efficiency, security, and transparency, promoting healthcare and medical research.
3.1. Research Design
Systematic literature analysis is used to synthesize blockchain-based clinical trial research. The review process comprises formulating research questions, selecting databases and search terms, screening and selecting studies, extracting and analyzing data, and synthesizing findings.
3.2. Data Collection and Analysis
Web of Science, PubMed, and IEEE Xplore are searched for blockchain and clinical trial phrases. Search terms include "blockchain," "distributed ledger," "clinical trials," "clinical research," "data security," "data integrity," and "transparency." We search just 2015–2022 high-impact factor journals. Screening and selection entails reading study titles and abstracts and choosing relevant research based on inclusion and exclusion criteria. Non-clinical blockchain trials are excluded. Select publications' study topics, design, techniques, important findings, and limitations are extracted. Thematic analysis summarises data to find patterns.
3.3. Meta-Model Framework Development
3.3.1. Design Principles and Components
Blockchain clinical trial meta-models prioritize security, transparency, and interoperability. Blockchain-based data management, a smart contract layer for clinical trial automation, and an identity management system for user IDs and access control are part of the framework, as shown in Figure 1.
Blockchain-based data management stores clinical trial data decentralized and tamper-proof. The smart contract layer controls patient recruiting, informed consent, data collection, and analysis to automate clinical trial execution. An identity management system controls user identities and clinical trial data access to prevent unauthorized access.
3.3.2. Technical Specifications and Requirements
The framework should protect clinical trial data with GDPR and HIPAA. System audits and monitoring should prevent unauthorized data access and manipulation, as illustrated in Figure 2.
Clinical trial data security, transparency, and efficiency are improved using blockchain technology in the meta-model framework. The framework manages clinical trial data, automates and secures it.
3.4. Algorithm Development
3.4.1. Design and Implementation
The Meta-Model Framework for Blockchain-Based Clinical Trials algorithm secures, transparently stores data, automates clinical trial procedures, and controls user access. The technique is implemented by Blockchain smart contracts that enforce clinical trial guidelines.
The algorithm governs clinical trial patient recruitment, informed consent, data collection, and analysis. A Solidity smart contract is deployed on Ethereum to apply the technique.
3.4.2. Proposed Algorithms
· P stands for "Patient data," including ID, age, gender, and medical history.
· The "Informed consent form," or ICF, comprises the patient's ID, signature, and date.
· The "Clinical trial protocol," or CTP, includes inclusion, exclusion, and research design criteria.
· DCT stands for "Data collection tools," like questionnaires, medical examinations, and imaging studies.
· DAM: "Data analysis methods" include statistical analysis, machine learning, and data visualization.
· UAP: "User access levels and permissions," including admin, investigator, sponsor, and patient.
· D: "Collected data," including patient ID, age, gender, medical history, questionnaire replies, test findings, and imaging data.
· D': "Secured and transparent clinical trial data," including data (D) and user access control (UAC).
· A: "Analyzed data," focused on statistical analysis.
· "Automated clinical trial processes," or ACP, use smart contracts for patient recruitment, informed consent, data collecting, and analysis.
· User access control (UAC): Smart contracts define user access levels and permissions.
· S stands for "Statistical analysis results," including mean, SD, and p-values.
· We used all symbols, notations, for an algorithm.
This algorithm sets the rules and circumstances for each clinical trial phase, ensures patient data meets requirements, securely collects and saves data on the blockchain, and analyses data using prescribed methods and protocols. Smart contracts control user access, and statistical analysis verifies data accuracy.
Data security, transparency, and integrity are addressed by the Blockchain-Based Clinical Trial Meta-Model Framework. We examined the Meta-Model Framework's architecture, components, performance, and scalability here. Compare the Meta-Model Framework algorithm to others and assess its pros and downsides. Finally, we discuss our study's implications for future research and blockchain's impact on clinical trials.
4.1. Meta-model framework analysis
Figure 4 The impact of the proposed framework on the drug development process and regulatory compliance
4.1.1. Architecture and Components
Module-Based Blockchain Clinical Trial: The Meta-Model Framework is flexible and scalable. The framework covers patient recruitment, informed consent, data collection, and analysis. Each component enforces clinical trial boundaries with blockchain smart contracts.
We simulated Meta-Model Framework components and architecture using clinical trial data. The Meta-Model Framework was compared to paper and cloud for performance and scalability. The Meta-Model Framework surpassed the paper-based approach in data security, transparency, and efficiency, according to our simulations. The Meta-Model Framework smart contracts registered only eligible patients with informed consent in the clinical study and securely saved their data on the blockchain. The framework automated patient recruitment, informed consent, data collection, and analysis, saving clinical trial time and money. Meta-Model Framework balanced data security, openness, and cloud performance. By eliminating a central server or database, Meta-Model Framework smart contracts reduced data leaks and cyberattacks.
4.1.2. Performance and Scalability
Table 6 Framework Performance Parameters
Simulation data shows that the Meta-Model Framework for Blockchain-Based Clinical Trials is faster and more scalable than Kim et al. (2020) and Li et al. (2018). Ren, Jiang, and Yang (2021) have higher scalability. These statistics imply the proposed approach could improve clinical trial performance and scalability over several methods.
4.2. Algorithm Evaluation
4.2.1. Metrics and Benchmarks
Meta-Model Framework for Blockchain-Based Clinical Trials algorithm transaction time and participant count were evaluated. These parameters were used to compare the algorithm to clinical trial methodologies. Transaction time impacts blockchain efficiency. The recommended approach was used to compare clinical trial step transaction time to existing methods. Different algorithms processed transactions slower than the intended ones. Blockchain scalability also depends on participant count. The algorithm's managed population was compared to existing approaches. The new approach was more scalable.
4.2.2. Comparison with Existing Approaches
Table 7 Comparison of the proposed Algorithm with existing algorithm for Transaction Time and Scalability
Overall, the Meta-Model Framework for Blockchain-Based Clinical Trials algorithm improved transaction time and scalability over prior methods. These results show that the suggested method could improve clinical trial performance and scalability compared to numerous existing approaches.
With the emerging technological advances, data are online with a relative ease of access, thus, cryptographic security of data is needed. (Tan and Heng, 2022). The algorithm and Meta-Model Framework for Blockchain-Based Clinical Trials enhance security of data, transparency, and efficiency. Smart contracts and modular design automate clinical trials, saving time and money. Transaction speed and scalability boost framework efficiency. Technical skills, regulatory frameworks, data privacy, and stakeholder resistance may challenge the framework and algorithm. The framework and algorithm's performance and scalability, legal and regulatory frameworks to ensure the ethical use of blockchain technology in clinical trials, and its implementation in low- and middle-income nations need further examination. A modular blockchain-based clinical trial structure and algorithm for security, transparency, and efficiency was created. The study also highlights blockchain's healthcare potential and the need for greater R&D to address implementation challenges. Simulated data and modest framework and algorithm evaluations limit this study. A larger study with more persons and clinical trials is needed to evaluate the framework and algorithm. Lastly, the Meta-Model Framework for Blockchain-Based Clinical Trials algorithm enhances clinical trial security, transparency, and efficiency. Large-scale performance evaluation, legal and regulatory framework construction, and feasibility in varied healthcare contexts are needed to overcome implementation problems. Blockchain technology in clinical trials may increase efficiency and efficacy, warranting more study. Low- and middle-income nations with limited healthcare and clinical trial access should test the framework and methods. Finally, the Meta-Model Framework for Blockchain-Based Clinical Trials algorithm may improve clinical trial security, transparency, and efficiency. More research is needed on ethical and legal issues, scalability, performance, and healthcare applications.
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