Consent Management Frameworks For Health Information Exchange

Consent Management Frameworks For Health Information Exchange

Authors

  • Ajay Aakula Associate, Cognizant Technology Solutions, Plano, Texas, USA
  • Mahammad Ayushi Associate Professor, Bangladesh Institute of Technology, Bangladesh

Downloads

Keywords:

consent management frameworks, health information exchange

Abstract

The proliferation of health information exchanges (HIEs) has fundamentally transformed the landscape of healthcare delivery, facilitating the seamless sharing of electronic health records (EHRs) across various healthcare providers and institutions. However, with the increased exchange of sensitive health information comes the paramount concern of safeguarding patient privacy and ensuring that consent for data sharing is adequately managed. This paper delves into the intricate and multifaceted frameworks designed for consent management within HIEs, critically analyzing their structure, implementation, and effectiveness. Consent management, a cornerstone of patient autonomy and data privacy, necessitates the creation of robust frameworks that account for the varying degrees of consent that patients may wish to exercise regarding the sharing of their personal health information (PHI). Given the complexity of modern healthcare systems and the involvement of numerous stakeholders, developing and operationalizing these frameworks presents significant challenges from both a legal and technological standpoint.

This paper aims to explore the various consent management models, such as opt-in, opt-out, granular consent, and dynamic consent, as well as their applicability and limitations in HIE settings. Each model is examined in the context of ensuring compliance with evolving privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and other regional health data protection laws. Additionally, the paper assesses how consent frameworks are integrated with the technical architecture of HIEs, including the use of advanced technologies like blockchain, artificial intelligence (AI), and machine learning (ML), which offer potential solutions to the complexities of managing patient consent dynamically while ensuring security and traceability.

A key focus of the study is the exploration of how these frameworks are operationalized to respect patient preferences in real-time. This involves an in-depth analysis of the technical tools used to capture, store, and manage patient consent at various levels of granularity, enabling patients to have fine-grained control over who has access to specific aspects of their health data. The paper also discusses the technical and ethical implications of enforcing consent directives across different healthcare systems, which may vary in their technological maturity and data-sharing practices. Furthermore, the interoperability challenges between different health information exchange platforms, each employing potentially divergent consent management protocols, are addressed, with suggestions for standardization efforts that could enhance seamless and secure data sharing.

In addition to technical and regulatory aspects, this research investigates the role of patient education and engagement in the success of consent management frameworks. The paper highlights the importance of ensuring that patients are adequately informed about their rights, the implications of sharing or withholding consent, and how they can update their preferences as their care needs evolve. Moreover, the ethical considerations surrounding consent in the context of emergent technologies like big data analytics and AI-driven health applications are discussed, particularly focusing on the potential for secondary data use that may fall outside the original consent parameters. The study evaluates current best practices in providing transparent, user-friendly consent interfaces while maintaining the legal and ethical rigor required for handling sensitive health data.

Furthermore, the paper presents case studies from different regions and healthcare systems that have implemented innovative consent management frameworks within HIEs. These case studies provide insight into the practical challenges and successes of deploying these frameworks in real-world settings. They also illustrate the potential of emerging technologies, such as distributed ledger technology (DLT) and smart contracts, in enabling decentralized consent management, thus empowering patients with greater control over their health information while enhancing the security and transparency of data exchanges. However, these case studies also underscore the limitations and obstacles that need to be addressed, including issues related to scalability, user adoption, and the alignment of consent frameworks with clinical workflows.

Finally, the paper offers recommendations for future directions in consent management for HIEs, emphasizing the need for a balance between technological innovation, regulatory compliance, and the preservation of patient trust. The discussion includes the potential impact of future regulatory changes, such as stricter data privacy laws and the growing emphasis on patient-centric care models, on consent management practices. In conclusion, the research provides a comprehensive examination of the consent management frameworks in HIEs, emphasizing their critical role in safeguarding patient privacy, promoting trust in health information exchanges, and ensuring compliance with stringent regulatory standards.

Downloads

Download data is not yet available.

References

M. Al-Shorbaji, S. H. H. Abdullatif, and A. H. Qadri, "The Role of Health Information Exchange in Health System Reforms: A Review of Literature," Journal of Health Management, vol. 20, no. 1, pp. 12-25, 2018.

Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.

S. Kumari, “AI-Powered Cloud Security for Agile Transformation: Leveraging Machine Learning for Threat Detection and Automated Incident Response ”, Distrib Learn Broad Appl Sci Res, vol. 6, pp. 467–488, Oct. 2020

A. M. Greenhalgh, D. A. Whitten, and H. A. Tufano, "Patient Consent and Data Sharing: Current Issues and Perspectives," Health Information Science and Systems, vol. 8, no. 1, pp. 1-9, 2020.

H. Wang, H. J. Chen, and K. C. Chen, "Consent Management in Health Information Exchanges: A Systematic Review," Journal of Biomedical Informatics, vol. 98, pp. 103285, 2019.

F. K. Kull, "A Framework for the Management of Consent in Electronic Health Records," International Journal of Medical Informatics, vol. 125, pp. 97-104, 2019.

S. T. Baradaran and K. A. B. Ismail, "Understanding the Role of Patient Consent in Electronic Health Information Exchange," International Journal of Health Policy and Management, vol. 7, no. 9, pp. 786-793, 2018.

R. C. Brajer and P. G. Zand, "The Impact of Health Information Exchange on Patient Outcomes: A Systematic Review," Journal of Health Care for the Poor and Underserved, vol. 29, no. 1, pp. 205-218, 2018.

J. Liu, S. Z. Li, and T. G. Moore, "Dynamic Consent: A New Paradigm for Patient Autonomy," Journal of Medical Ethics, vol. 45, no. 3, pp. 179-183, 2019.

T. K. McGraw and M. A. Vanderpool, "Implementing Consent Management Frameworks for Health Information Exchange," Journal of Health Information Management, vol. 33, no. 4, pp. 120-132, 2019.

M. P. de Lima and F. P. O. Teixeira, "Data Sharing in Health Information Exchanges: Regulatory and Ethical Considerations," International Journal of Medical Informatics, vol. 130, pp. 103964, 2019.

C. A. Shapiro, A. D. M. Harten, and K. A. Jones, "Patient Engagement and Consent Management: A Survey of Current Practices," Journal of Health Communication, vol. 25, no. 5, pp. 403-412, 2020.

A. Z. Struck and D. J. Becker, "Challenges in Implementing Patient Consent Models in Health Information Exchange," BMC Medical Informatics and Decision Making, vol. 20, no. 1, pp. 8-15, 2020.

S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019

Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.

K. C. Horning and G. F. Schmid, "Interoperability in Health Information Exchanges: Addressing Consent Management Issues," Journal of Health Information Management, vol. 34, no. 1, pp. 45-50, 2020.

R. F. Guimaraes, F. C. S. da Silva, and A. R. E. Gomes, "Ethical Considerations in Health Information Exchanges: A Consent Management Perspective," Journal of Healthcare Management, vol. 65, no. 2, pp. 132-144, 2020.

S. Wong, "The Role of Consent in Health Data Sharing: Implications for Research," Journal of Biomedical Ethics, vol. 32, no. 3, pp. 15-22, 2020.

H. C. Xu and L. Z. Zhang, "A Comprehensive Review of Consent Management Frameworks for Electronic Health Records," Journal of Health Informatics Research, vol. 5, no. 2, pp. 95-112, 2020.

J. Li, "Leveraging Blockchain Technology for Enhanced Consent Management in HIEs," International Journal of Information Management, vol. 45, pp. 200-205, 2020.

L. M. Tan, "Towards Patient-Centric Consent Management in Health Information Exchange," Journal of Medical Systems, vol. 44, no. 5, pp. 1-10, 2020.

F. H. Wang, "Dynamic Consent Models in Health Information Exchange: A Comparative Analysis," Journal of Health Communication, vol. 25, no. 4, pp. 309-315, 2020.

C. Parikh, "Policy Implications for Consent Management in Health Information Exchanges," Health Affairs, vol. 39, no. 7, pp. 1128-1135, 2020.

C. H. Hill, "Patient Education and Engagement in the Consent Process for HIEs," Journal of Health Literacy, vol. 5, no. 2, pp. 100-109, 2020.

Downloads

Published

21-10-2020

How to Cite

Ajay Aakula, and Mahammad Ayushi. “Consent Management Frameworks For Health Information Exchange”. Journal of Science & Technology, vol. 1, no. 1, Oct. 2020, pp. 905-3, https://thesciencebrigade.com/jst/article/view/475.
PlumX Metrics

Plaudit

License Terms

Ownership and Licensing:

Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.

License Permissions:

Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.

Additional Distribution Arrangements:

Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.

Online Posting:

Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.

Responsibility and Liability:

Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.

Loading...