A Decentralized Health Data Repository for Remote Patient Monitoring Using Blockchain and FHIR
Resumo
The world’s aging population increasingly faces challenges in accessing healthcare due to a shortage of healthcare professionals. Telemedicine and remote patient monitoring solutions offer a promising avenue for improving access to care, allowing for the monitoring of physiological data, activities performed, and the conditions of the patient’s environment. However, such systems must address numerous challenges, such as interoperability, security, integrity, and confidentiality of medical data. In this paper, we propose a repository architecture for medical data obtained through remote patient monitoring. Our solution relies on the Fast Healthcare Interoperability Resources (FHIR) standard to address interoperability issues, while the inherent characteristics of blockchain technology provide security, integrity, and confidentiality of stored data. In addition to remote patient monitoring, the proposed repository has the potential to be used for scientific research, data mining and analysis applications among other health applications. Ongoing implementation and testing of the repository in a real-world setting will demonstrate its performance and scalability. Meanwhile, we present the architecture and constituent elements, including data flow and smart contracts, with their responsibilities described. Overall, our proposed solution offers a promising approach to addressing the challenges of remote patient monitoring and storing medical data securely and efficiently.
Referências
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Azaria, A., Ekblaw, A., Vieira, T., and Lippman, A. (2016). Medrec: Using blockchain for medical data access and permission management. In 2016 2nd International Conference on Open and Big Data (OBD), pages 25–30.
Barbosa, V., Ferreira, H., Gomes, L., Gomes, F., Barreto, I., Monteiro, O., and Oliveira, M. (2020). SmartRES - Uma plataforma IoT para Monitoramento Inteligente em Saúde e sua Aplicação no Contexto da COVID-19. In Anais do XX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 297–307, Porto Alegre, RS, Brasil. SBC.
Brasil (2018). Lei nº 13.709, de 14 de agosto de 2018. Diário Oficial [da] República Federativa do Brasil.
Brasil (2020). Lei nº 13.989, de 15 de abril de 2012. Diário Oficial [da] República Federativa do Brasil.
Buterin, V. (2014). A next-generation smart contract and decentralized application platform. White Paper. Available online: https://ethereum.org/en/whitepaper/.
Celestrini, J., Rocha, R., Santos, C., Mota, V., Filho, J., and Andreão, R. (2019). HealthDash: Monitoramento remoto de pacientes utilizando programação baseada em fluxo de dados. In Anais do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, pages 222–233, Porto Alegre, RS, Brasil. SBC.
CFM, Conselho Federal de Medicina. (2002). Resolução CFM nº 1.643, de 26 de agosto de 2002. Diário Oficial [da] República Federativa do Brasil.
Chelongar, K. and Ajami, S. (2021). Using active information and communication technology for elderly homecare services: A scoping review. Home Health Care Services Quarterly, 40(1):93–104. PMID: 32990180.
Chen, R. (2016). Current challenges of ehrs for oncologists. Oncology Times, 38(16).
Dagher, G. G., Mohler, J., Milojkovic, M., and Marella, P. B. (2018). Blockchain: The evolution of decentralized system architectures. IEEE Access, 6:5306–5320.
de Farias, F. A. C., Dagostini, C. M., de Assunção Bicca, Y., Falavigna, V. F., and Falavigna, A. (2020). Remote Patient Monitoring: A Systematic Review. Telemedicine and e-Health, 26(5):576–583.
George, T. P. and Hopla, D. L. (2015). Advantages of personal health records. Nursing2020 Critical Care, 10(6).
Han, J. E., Rabinovich, M., Abraham, P., Satyanarayana, P., Liao, T. V., Udoji, T. N., Cotsonis, G. A., Honig, E. G., and Martin, G. S. (2016). Effect of Electronic Health Record Implementation in Critical Care on Survival and Medication Errors. The American Journal of the Medical Sciences, 351(6):576–581.
Heart, T., Ben-Assuli, O., and Shabtai, I. (2017). A review of PHR, EMR and EHR integration: A more personalized healthcare and public health policy. Health Policy and Technology, 6(1):20–25.
Hu, R., Yan, Z., Ding, W., and Yang, L. T. (2020). A survey on data provenance in IoT. World Wide Web, 23(2):1441–1463.
ISO/IEC 9834-8 (2014). Information technology – Procedures for the operation of object identifier registration authorities – Part 8: Generation of universally unique identifiers (UUIDs) and their use in object identifiers. Standard, International Organization for Standardization, Geneva, CH.
Lee, Y.-L., Lee, H.-A., Hsu, C.-Y., Kung, H.-H., and Chiu, H.-W. (2021). Implement an International Interoperable PHR by FHIR—A Taiwan Innovative Application. Sustainability, 13(1).
Lundereng, E. D., Nes, A. A. G., Holmen, H., Winger, A., Thygesen, H., Jøranson, N., Borge, C. R., Chen, W., Dajani, O., Mariussen, K. L., and Steindal, S. A. (2021). Health care professionals’ experiences and perspectives on using telehealth for home-based palliative care: Protocol for a scoping review. JMIR Res. Protoc., 10(10):e33305.
Malasinghe, L. P., Ramzan, N., and Dahal, K. (2019). Remote patient monitoring: a comprehensive study. Journal of Ambient Intelligence and Humanized Computing, 10(1):57–76.
Margheri, A., Masi, M., Miladi, A., Sassone, V., and Rosenzweig, J. (2020). Decentralised provenance for healthcare data. International Journal of Medical Informatics, 141(June):104197.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. White Paper. Available online: https://bitcoin.org/bitcoin.pdf.
Nofer, M., Gomber, P., Hinz, O., and Schiereck, D. (2017). Blockchain. Business & Information Systems Engineering, 59(3):183–187.
Portnoy, J., Waller, M., and Elliott, T. (2020). Telemedicine in the Era of COVID-19. The Journal of Allergy and Clinical Immunology: In Practice, 8(5):1489–1491.
Rimsza, M. E., Hotaling, A. J., Keown, M. E., Marcin, J. P., Moskowitz, W. B., Sigrest, T. D., and Simon, H. K. (2015). The Use of Telemedicine to Address Access and Physician Workforce Shortages. Pediatrics, 136(1):202–209.
Roehrs, A., da Costa, C. A., and da Rosa Righi, R. (2017). OmniPHR: A distributed architecture model to integrate personal health records. Journal of Biomedical Informatics, 71:70–81.
Roumia, M. and Steinhubl, S. (2014). Improving cardiovascular outcomes using electronic health records. Current Cardiology Reports, 16(2):451.
Shetty, S., Red, V., Kamhoua, C., Kwiat, K., and Njilla, L. (2017). Data provenance assurance in the cloud using blockchain. In Hall, R. D., Blowers, M., and Williams, J., editors, Disruptive Technologies in Sensors and Sensor Systems, volume 10206, page 102060I.
Swan, M. (2015). Blockchain: Blueprint for a New Economy. O’Reilly Media.
Szabo, N. (1997). Formalizing and securing relationships on public networks. First Monday, 2(9).
Vieira, M. A. and Carvalho, S. T. (2016a). Addressing the Concurrent Access to Smart Objects in Ubiquitous Computing Scenarios. In Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web, Webmedia ’16, page 79–82, New York, NY, USA. Association for Computing Machinery.
Vieira, M. A. and Carvalho, S. T. (2016b). (Meta)Modelagem de Espaços Inteligentes Pessoais e Espaços Inteligentes Fixos para Aplicações Ubíquas. In Anais do VIII Simpósio Brasileiro de Computação Ubíqua e Pervasiva (SBCUP), pages 1056–1065, Porto Alegre-RS, Brazil. Sociedade Brasileira de Computação - SBC.
Vieira, M. A. and Carvalho, S. T. (2021). Towards a blockchain-based architecture for data provenance management in the internet of things. In SBRC 2021 - IV WBlockchain 2021 (IV Workshop em Blockchain: Teoria, Tecnologias e Aplicações).
WHO, World Health Organization. (2016). Global strategy on human resources for health: Workforce 2030. [link]. [Online; accessed Jan-2022].
WHO, World Health Organization. (2017). 10 facts on ageing and health. [link]. [Online; accessed Jan-2022].
Yang, J., Li, Y., Liu, Q., Li, L., Feng, A., Wang, T., Zheng, S., Xu, A., and Lyu, J. (2020). Brief introduction of medical database and data mining technology in big data era. Journal of Evidence-Based Medicine, 13(1):57–69.
Zhang, P., White, J., Schmidt, D. C., and Lenz, G. (2018a). Blockchain technology in healthcare: A comprehensive review and directions for future research. Applied Sciences, 8(7):1225.
Zhang, P., White, J., Schmidt, D. C., Lenz, G., and Rosenbloom, S. T. (2018b). FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data. Computational and Structural Biotechnology Journal, 16:267–278.
Publicado
22/05/2023
Como Citar
VIEIRA, Marcos Alves; VELASCO, Gislainy Crisostomo; CARVALHO, Sergio T..
A Decentralized Health Data Repository for Remote Patient Monitoring Using Blockchain and FHIR. In: WORKSHOP EM BLOCKCHAIN: TEORIA, TECNOLOGIAS E APLICAÇÕES (WBLOCKCHAIN), 6. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 85-98.
DOI: https://doi.org/10.5753/wblockchain.2023.723.
