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Wan J, Zeng Y. Prediction of hepatic metastasis in esophageal cancer based on machine learning. Sci Rep 2024; 14:14507. [PMID: 38914571 PMCID: PMC11196737 DOI: 10.1038/s41598-024-63213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 05/27/2024] [Indexed: 06/26/2024] Open
Abstract
This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/ ) for predicting the risk of hepatic metastasis in esophageal cancer.
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Affiliation(s)
- Jun Wan
- Department of Emergency surgery, Yangtze University Jingzhou Hospital, jingzhou, China
| | - Yukai Zeng
- Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, No. 126 Xiantai street, Changchun, Jilin, China.
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2
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Sundaram A, Subramaniam H, Ab Hamid SH, Mohamad Nor A. An adaptive data-driven architecture for mental health care applications. PeerJ 2024; 12:e17133. [PMID: 38563009 PMCID: PMC10984189 DOI: 10.7717/peerj.17133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture's effectiveness. To further assess the architecture's practical application, a prototype architecture for predicting pandemic anxiety was developed.
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Affiliation(s)
- Aishwarya Sundaram
- Institute for Advanced Studies, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Hema Subramaniam
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Siti Hafizah Ab Hamid
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Azmawaty Mohamad Nor
- Department of Educational Psychology and Counselling, Faculty of Education, Universiti Malaya, Kuala Lumpur, Malaysia
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3
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Zhuang Y, Zhang L. Promoting TEFCA with Blockchain Technology: A Decentralized Approach to Patient-centered Healthcare Data Management. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:824-833. [PMID: 38222410 PMCID: PMC10785864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
The Trusted Exchange Framework and Common Agreement (TEFCA) is a framework consisting of seven principles designed to create a secure and seamless health information exchange system across various healthcare settings. The ultimate goal of TEFCA is to facilitate public health surveillance, increase interoperability, promote data sharing, and ensure patient-centered healthcare data management. While the implementation of these principles is challenging, blockchain technology, with its unique features such as transparency, auditability, immutability, and anonymity, can provide a promising solution to the development of TEFCA. This article delves into the potential of blockchain technology to promote TEFCA design. By providing an immutable and transparent ledger, blockchain ensures data integrity, openness, and patient privacy. Overall, the use of blockchain technology can help address the challenges of implementing TEFCA principles and promote patient empowerment and control over their health data, improve data interoperability, and enhance healthcare quality.
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Affiliation(s)
- Yan Zhuang
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Health Science Center of Peking University, Beijing, China
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN
| | - Luxia Zhang
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Health Science Center of Peking University, Beijing, China
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4
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Li MP, Liu WC, Sun BL, Zhong NS, Liu ZL, Huang SH, Zhang ZH, Liu JM. Prediction of bone metastasis in non-small cell lung cancer based on machine learning. Front Oncol 2023; 12:1054300. [PMID: 36698411 PMCID: PMC9869148 DOI: 10.3389/fonc.2022.1054300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/21/2022] [Indexed: 01/12/2023] Open
Abstract
Objective The purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm. Methods Patients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models. Results A total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC. Conclusion This study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making.
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Affiliation(s)
- Meng-Pan Li
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,The First Clinical Medical College of Nanchang University, Nanchang, China
| | - Wen-Cai Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,The First Clinical Medical College of Nanchang University, Nanchang, China,Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Bo-Lin Sun
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Nan-Shan Zhong
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Zhi-Li Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Shan-Hu Huang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Zhi-Hong Zhang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China,*Correspondence: Jia-Ming Liu, ; Zhi-Hong Zhang,
| | - Jia-Ming Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China,*Correspondence: Jia-Ming Liu, ; Zhi-Hong Zhang,
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Vargas C, Mira da Silva M. Case studies about smart contracts in healthcare. Digit Health 2023; 9:20552076231203571. [PMID: 37822961 PMCID: PMC10563467 DOI: 10.1177/20552076231203571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 09/07/2023] [Indexed: 10/13/2023] Open
Abstract
The Internet of Things (IoT) such as devices and sensors are a fast growth reality which our bureaucratical and archaic institutional system is not yet ready to embrace its functionalities. In the health system, many developments are made, and smart devices are the key to preventing, studying, investigating, and solving a lot of diseases and improving our health system. But along with this, innovation is necessary for the hospitals, for example, to have a proper system that provides storage of health data information and respects the General Data Protection Regulation (GDPR) with the use of smart contracts that secure the integrity and disclosure of the patient's data, since the majority of hospitals still use paper, physical records to store data. In this study, we will briefly analyse and explain three different suggested methods to deal with the challenges that Internet of Medical Things (IoMT) encounters. We will not choose which one is the best because of the different features and the countries they are proposed but will emphasize the benefits and challenges which one has.
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Affiliation(s)
- Cristina Vargas
- Instituto Superior Técnico, University of Lisbon, Lisboa, Portugal
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Sarkar IN. Transforming Health Data to Actionable Information: Recent Progress and Future Opportunities in Health Information Exchange. Yearb Med Inform 2022; 31:203-214. [PMID: 36463879 PMCID: PMC9719753 DOI: 10.1055/s-0042-1742519] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
OBJECTIVES Provide a systematic review of literature pertaining to health information exchange (HIE) since 2018. Summarize HIE-associated literature for most frequently occurring topics, as well as within the context of the COVID-19 pandemic and health equity. Finally, provide recommendations for how HIE can advance the vision of a digital healthcare ecosystem. METHODS A computer program was developed to mediate a literature search of primary literature indexed in MEDLINE that was: (1) indexed with "Health Information Exchange" MeSH descriptor as a major topic; and (2) published between January 2018 and December 2021. Frequency of MeSH descriptors was then used to identify and to rank topics associated with the retrieved literature. COVID-19 literature was identified using the general COVID-19 PubMed Clinical Query filter. Health equity literature was identified using additional MeSH descriptor-based searches. The retrieved literature was then reviewed and summarized. RESULTS A total of 256 articles were retrieved and reviewed for this survey. The major thematic areas summarized were: (1) Information Dissemination; (2) Delivery of Health Care; (3) Hospitals; (4) Hospital Emergency Service; (5) COVID-19; (6) Health Disparities; and (7) Computer Security and Confidentiality. A common theme across all areas examined for this survey was the maturity of HIE to support data-driven healthcare delivery. Recommendations were developed based on opportunities identified across the reviewed literature. CONCLUSIONS HIE is an essential advance in next generation healthcare delivery. The review of the recent literature (2018-2021) indicates that successful HIE improves healthcare delivery, often resulting in improved health outcomes. There remain major opportunities for expanded use of HIE, including the active engagement of clinical and patient stakeholders. The maturity of HIE reflects the maturity of the biomedical informatics and health data science fields.
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Affiliation(s)
- Indra Neil Sarkar
- Brown University, Providence, RI, USA,Rhode Island Quality Institute, Providence, RI, USA,Correspondence to: Indra Neil Sarkar, PhD, MLIS, FACMI, ACHIP Brown UniversityBox G-R Providence, RI 02912USA+1 401 863 2428
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7
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Baysal MV, Özcan-Top Ö, Betin-Can A. Blockchain technology applications in the health domain: a multivocal literature review. THE JOURNAL OF SUPERCOMPUTING 2022; 79:3112-3156. [PMID: 36060094 PMCID: PMC9424065 DOI: 10.1007/s11227-022-04772-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Blockchain technology has been changing the nature of several businesses, from supply chain management to electronic record management systems and copyright management to healthcare applications. It provides a resilient and secure platform for modifications due to its distributed and shared nature and cryptographic functions. Each new technology, however, comes with its challenges alongside its opportunities. Previously, we performed a systematic literature review (SLR) to explore how blockchain technology potentially benefits health domain applications. The previous SLR included 27 formal literature papers from 2016 to 2020. Noticing that blockchain technology is rapidly growing, we extended the previous SLR with a multivocal literature review (MLR) approach to present the state of the art in this study. We focused on understanding to what degree blockchain could answer the challenges inherited in the health domain and whether blockchain technology may bring new challenges to health applications. The MLR consists of 78 sources of formal literature and 23 sources of gray literature from 2016 to 2021. As a result of this study, we specified 17 health domain challenges that can be categorized into four groups: (i) meeting regulatory requirements and public health surveillance, (ii) ensuring security and privacy, (iii) ensuring interoperability, and (iv) preventing waste of resources. The analysis shows that blockchain makes significant contributions to the solutions of these challenges. However, 10 new pitfalls come with adopting the technology in the health domain: the inability to delete sensitive data once it is added to a chain, limited ability to keep large-scale data in a blockchain, and performance issues. The data we extracted during the MLR is available in a publicly accessible online repository.
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Affiliation(s)
- Merve Vildan Baysal
- Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
- The Scientific and Technological Research Council of Turkey (TÜBİTAK), Ankara, Türkiye
| | - Özden Özcan-Top
- Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
| | - Aysu Betin-Can
- Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
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Luo X, Wu Y, Niu L, Huang L. Bibliometric Analysis of Health Technology Research: 1990~2020. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9044. [PMID: 35897415 PMCID: PMC9330553 DOI: 10.3390/ijerph19159044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 12/10/2022]
Abstract
This paper aims to summarize the publishing trends, current status, research topics, and frontier evolution trends of health technology between 1990 and 2020 through various bibliometric analysis methods. In total, 6663 articles retrieved from the Web of Science core database were analyzed by Vosviewer and CiteSpace software. This paper found that: (1) The number of publications in the field of health technology increased exponentially; (2) there is no stable core group of authors in this research field, and the influence of the publishing institutions and journals in China is insufficient compared with those in Europe and the United States; (3) there are 21 core research topics in the field of health technology research, and these research topics can be divided into four classes: hot spots, potential hot spots, margin topics, and mature topics. C21 (COVID-19 prevention) and C10 (digital health technology) are currently two emerging research topics. (4) The number of research frontiers has increased in the past five years (2016-2020), and the research directions have become more diverse; rehabilitation, pregnancy, e-health, m-health, machine learning, and patient engagement are the six latest research frontiers.
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Affiliation(s)
| | | | | | - Lucheng Huang
- College of Economics and Management, Beijing University of Technology, Beijing 100124, China; (X.L.); (Y.W.); (L.N.)
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Panigrahi A, Nayak AK, Paul R. HealthCare EHR. INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT 2022. [DOI: 10.4018/ijisscm.290017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Blockchain technology is currently playing a significant role in providing a secure and effective means to share information in a variety of domains, including the financial sector, supply chain management (SCM) in various domains, IoT, and the field of health care systems (HCS). The HCS application's interoperability and security allow patients and vendors to communicate information seamlessly. The absence of such traits reveals the patient's difficulties in gaining access to his or her own health status. As a result, incorporating blockchain technology will eliminate this disadvantage, allowing the HCS to become more effective and efficient. These potential benefits provide a foundation for blockchain technology to be used in various aspects of HCS, such as maintain the patient electronic health record (EHR) and electronic medical records (EMR) for various medical devices, billing, and telemedicine systems, and so on.
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10
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Zhuang Y, Zhang L, Gao X, Shae ZY, Tsai JJP, Li P, Shyu CR. Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies. J Med Internet Res 2022; 24:e36774. [PMID: 35759315 PMCID: PMC9274392 DOI: 10.2196/36774] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 05/07/2022] [Accepted: 05/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security. Objective This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS. Methods A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS. Results A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies. Conclusions This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks.
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Affiliation(s)
- Yan Zhuang
- National Institute of Health Data Science, Peking University, Beijing, China.,Advanced Institute of Information Technology, Peking University, Hangzhou, China
| | - Luxia Zhang
- National Institute of Health Data Science, Peking University, Beijing, China.,Advanced Institute of Information Technology, Peking University, Hangzhou, China
| | - Xiyuan Gao
- Department of Statistics, University of Missouri, Columbia, MO, United States
| | - Zon-Yin Shae
- Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
| | - Jeffrey J P Tsai
- Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
| | - Pengfei Li
- Advanced Institute of Information Technology, Peking University, Hangzhou, China
| | - Chi-Ren Shyu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, United States
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Albalwy F, Brass A, Davies A. A Blockchain-Based Dynamic Consent Architecture to Support Clinical Genomic Data Sharing (ConsentChain): Proof-of-Concept Study. JMIR Med Inform 2021; 9:e27816. [PMID: 34730538 PMCID: PMC8600428 DOI: 10.2196/27816] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/15/2021] [Accepted: 07/25/2021] [Indexed: 11/30/2022] Open
Abstract
Background In clinical genomics, sharing of rare genetic disease information between genetic databases and laboratories is essential to determine the pathogenic significance of variants to enable the diagnosis of rare genetic diseases. Significant concerns regarding data governance and security have reduced this sharing in practice. Blockchain could provide a secure method for sharing genomic data between involved parties and thus help overcome some of these issues. Objective This study aims to contribute to the growing knowledge of the potential role of blockchain technology in supporting the sharing of clinical genomic data by describing blockchain-based dynamic consent architecture to support clinical genomic data sharing and provide a proof-of-concept implementation, called ConsentChain, for the architecture to explore its performance. Methods The ConsentChain requirements were captured from a patient forum to identify security and consent concerns. The ConsentChain was developed on the Ethereum platform, in which smart contracts were used to model the actions of patients, who may provide or withdraw consent to share their data; the data creator, who collects and stores patient data; and the data requester, who needs to query and access the patient data. A detailed analysis was undertaken of the ConsentChain performance as a function of the number of transactions processed by the system. Results We describe ConsentChain, a blockchain-based system that provides a web portal interface to support clinical genomic sharing. ConsentChain allows patients to grant or withdraw data requester access and allows data requesters to query and submit access to data stored in a secure off-chain database. We also developed an ontology model to represent patient consent elements into machine-readable codes to automate the consent and data access processes. Conclusions Blockchains and smart contracts can provide an efficient and scalable mechanism to support dynamic consent functionality and address some of the barriers that inhibit genomic data sharing. However, they are not a complete answer, and a number of issues still need to be addressed before such systems can be deployed in practice, particularly in relation to verifying user credentials.
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Affiliation(s)
- Faisal Albalwy
- Department of Computer Science, University of Manchester, Manchester, United Kingdom.,Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia.,Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Andrew Brass
- Department of Computer Science, University of Manchester, Manchester, United Kingdom.,Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Angela Davies
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
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12
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Thomson C, Beale R. Is blockchain ready for orthopaedics? A systematic review. J Clin Orthop Trauma 2021; 23:101615. [PMID: 34671546 PMCID: PMC8507193 DOI: 10.1016/j.jcot.2021.101615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/22/2021] [Accepted: 09/27/2021] [Indexed: 10/20/2022] Open
Abstract
INTRODUCTION The unique attributes of distributed ledger blockchain systems including robust security, immutability, transparency, and decentralisation, make them highly suitable solutions for many healthcare-related problems. This review examines the potential applications for blockchain technology in the field of orthopaedics, by taking a systematic approach to the evolving blockchain literature and mapping potential use cases against the current needs of orthopaedic practice. METHOD A literature search was performed using Pubmed, EMBASE, OVID and the Cochrane library with the primary aim of identifying detailed accounts of blockchain solutions and use cases in healthcare. These articles were then reviewed and mapped against current orthopaedic practice to illustrate applications specific to that specialty. RESULTS One hundred and forty-one papers were identified which described case studies, simulations, or detailed proposals of blockchain solutions in healthcare. Most studies described blockchain solutions at the simulated or prototype testing phase, with only 10 case studies describing blockchains in "real-world" use. The most frequently cited use cases for blockchain technology involved the storage, security and sharing of electronic medical records. Other blockchain solutions focused on the "Internet of Things", research, COVID 19, supply chains and radiology. There were no solutions focusing specifically on orthopaedics. Many of the described blockchain solutions had considerable scope for application in orthopaedic practice however, providing the potential for greater inter-institutional collaboration, cross border data exchange, enhanced patient participation, and more robust and transparent research practices. CONCLUSION Blockchain solutions for healthcare are increasing in number and scope and have multiple applications relevant to orthopaedic practice. The orthopaedic community needs to be aware of this innovative and growing field of computer science so that surgeons can leverage the power of blockchain safely for the future of orthopaedics.
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Affiliation(s)
- Calum Thomson
- Royal Orthopaedic Hospital, (Birmingham), The Woodlands, Bristol Rd S, Northfield, Birmingham, B31 2AP, UK,Corresponding author.
| | - Russell Beale
- Advanced Interaction Group, School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
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Zhuang Y, Sheets L, Gao X, Shen Y, Shae ZY, Tsai JJP, Shyu CR. Development of A Blockchain Framework for Virtual Clinical Trials. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:1412-1420. [PMID: 33936517 PMCID: PMC8075489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Clinical trials are essential for discovering new treatments, but there are multiple challenges to patient recruitment, patient engagement, and cost containment. Virtual clinical trials (VCT) are an innovative approach that provides potential solutions by conducting home-based, rather than site-based, clinical trials. Virtual clinical trials are still the exception rather than general practice due to technical barriers. "Blockchain," a distributed ledger technology, is a perfect match for virtual clinical trials. Its peer-to-peer design, security settings, and data transparency meet the needs of many healthcare applications. The programmable "Smart Contract" feature makes blockchain more suitable and feasible for VCT by solving computational issues. Our previous work has shown the power of applying blockchain to clinical trial recruitment. This work develops a comprehensive blockchain framework, with simulations and case studies, including patient recruitment, patient engagement, and persistent monitoring modules.
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Affiliation(s)
- Yan Zhuang
- Institute for Data Science and Informatics
| | - Lincoln Sheets
- Institute for Data Science and Informatics
- School of Medicine
| | - Xiyuan Gao
- Department of Statistics, University of Missouri, Columbia, MO, USA
| | | | - Zon-Yin Shae
- Artifical Intelligence Research Lab, Asia University, Taichung, Taiwan
| | - Jeffrey J P Tsai
- Artifical Intelligence Research Lab, Asia University, Taichung, Taiwan
| | - Chi-Ren Shyu
- Institute for Data Science and Informatics
- School of Medicine
- Artifical Intelligence Research Lab, Asia University, Taichung, Taiwan
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