Abstract
The wide adoption of wireless communication and mobile devices has facilitated the development of numerous applications to provide citizens with convenient access to health-related tracking and management services. Most of those services require the storage of some personal data and therefore resort to common user authentication practice (e.g., using a username and password combination) to ensure data is delivered to the appropriate party. As a result, users often find themselves having to maintain or memorize many combinations of accounts and their associated login credentials during their interaction with different services throughout the lifespan. Given the advancement of blockchain and distributed ledger technologies, a wealth of services in various domains including health care has explored the feasibility of migrating existing centralized services to such decentralized infrastructures. Because of this exploration, traditionally centralized authentication approaches managed by one party can no longer support the need of onboarding users, managing, and monitoring user activities and transactions in a decentralized manner. A community of researchers has hence been formed to study blockchain-based identity solutions, such as decentralized identities and self-sovereign identities, that would allow users to have a more common way to identify themselves when accessing a plethora of services. The main goal of these identity methods is to eliminate the need of requiring users to maintain multiple identifiers or online credentials as each individual has only one identity that truly represents themselves. These identities would be established and secured by cryptographic principles such that they still preserve at least the same security and privacy levels as their centralized counterparts. In this chapter, we first present a systematic overview of the underlying motivations and principles of blockchain-based identities to provide the audience with a basic understanding of how such identities operate and the pressing need to incorporate them. We will also introduce two of the popular blockchain-based identity frameworks currently adopted in decentralized applications. We then discuss the potential applications of these identities and their feasibility using the health care domain as a case study to hopefully inspire our readers with ideas that can be further investigated as research solutions in the health care or other domains. Lastly, we will conclude the chapter with additional discussions on the practicality of blockchain-based identities and the potential caveats or limitations associated. This chapter will serve as a cornerstone for healthcare executives, informaticians, and security/privacy experts to further investigate and make infrastructural decisions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The complete standards document can be found at https://www.w3.org/TR/did-core.
References
Benantar, M.: Access Control Systems: Security. Identity Management and Trust Models. Springer Science & Business Media, Berlin (2005)
Brixey, J., Johnson, T.R., Zhang, J.: Evaluating a medical error taxonomy. In: Proceedings of the AMIA Symposium, p. 71. American Medical Informatics Association (2002)
Buterin, V., et al.: Ethereum white paper: a next generation smart contract & decentralized application platform. First version 53 (2014)
Cameron, K., Posch, R., Rannenberg, K.: Proposal for a common identity framework: A user-centric identity metasystem http://www.identityblog.com/wp-content/images/2009/06. UserCentricIdentityMetasystem. html (2009)
Carlisle, B., Kimmelman, J., Ramsay, T., MacKinnon, N.: Unsuccessful trial accrual and human subjects protections: an empirical analysis of recently closed trials. Clinical Trials 12(1), 77–83 (2015)
Chadwick, D.W.: Federated identity management. In: Foundations of Security Analysis and Design V, pp. 96–120. Springer, Berlin (2009)
Dunphy, P., Petitcolas, F.A.P.: A first look at identity management schemes on the blockchain. IEEE Secur. Privacy 16(4), 20–29 (2018)
Embi, P.J., Jain, A., Clark, J., Bizjack, S., Hornung, R., Martin Harris, C.: Effect of a clinical trial alert system on physician participation in trial recruitment. Arch. Int. Med. 165(19), 2272–2277 (2005)
Evans, S.R.: Clinical trial structures. J. Exp. Stroke Trans. Med. 3(1), 8 (2010)
Fernández-Alemán, J.L., Señor, I.C., Lozoya, P.Á.O., Toval, A.: Security and privacy in electronic health records: A systematic literature review. J. Biomed. Inf. 46(3), 541–562 (2013)
Fiedler, B.A.: Device failure tracking and response to manufacturing recalls. In: Managing Medical Devices Within a Regulatory Framework, pp. 263–275. Elsevier (2017)
Hardt, D. et al.: The oauth 2.0 authorization framework. Technical report, RFC 6749 (2012)
Hyperledger.org. Hyperledger Indy: https://www.hyperledger.org/use/hyperledger-indy
Jøsang, A., Pope, S.: User centric identity management. In: AusCERT Asia Pacific Information Technology Security Conference, pp. 77. Citeseer (2005)
Just, v, Marc, D., Munns, M., Sandefer, R.: Why patient matching is a challenge: research on master patient index (MPI) data discrepancies in key identifying fields. In: Perspectives in Health Information Management, vol. 13(Spring) (2016)
Lippi, G., Mattiuzzi, C., Bovo, C., Favaloro, E.J.: Managing the patient identification crisis in healthcare and laboratory medicine. Clin. Biochem. 50(10–11), 562–567 (2017)
Lundkvist, C., Heck, R., Torstensson, J., Mitton, Z., Sena, M.: Uport: A platform for self-sovereign identity. https://whitepaper.uport.me/uPort_whitepaper_DRAFT20170221.pdf (2017)
Mahon, E., Roberts, J., Furlong, P., Uhlenbrauck, G., Bull, J.: Barriers to clinical trial recruitment and possible solutions: a stakeholder survey. Appl. Clin. Trials 24 (2015)
Nakamoto, S.: A peer-to-peer electronic cash system (2008)
Narayanan, A., Bonneau, J., Felten, E., Miller, A., Goldfeder, S.: Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton University Press (2016)
Nauta, J.C., Joosten, R.: Self-sovereign identity: A comparison of Irma and Sovrin. Technical report, Technical Report TNO2019R11011 (2019)
Confessore, N.: Cambridge Analytica and Facebook: The Scandal and the Fallout So Far: https://www.nytimes.com/2018/04/04/us/politics/cambridge-analytica-scandal-fallout.html
Patel, M.X., Doku, V., Tennakoon, L.: Challenges in recruitment of research participants. Adv. Psychiatric Treatment 9(3), 229–238 (2003)
Payne, J.K., Hendrix, C.C.: Clinical trial recruitment challenges with older adults with cancer. Appl. Nurs. Res. 23(4), 233–237 (2010)
Rackoff, C., Simon, D.R.: Non-interactive zero-knowledge proof of knowledge and chosen ciphertext attack. In: Annual International Cryptology Conference, pp. 433–444. Springer, Berlin (1991)
Roman, R., Zhou, J., Lopez, J.: On the features and challenges of security and privacy in distributed internet of things. Comput. Netw. 57(10), 2266–2279 (2013)
sequoiaproject.org. Cross organizational patient identity management: Challenges and opportunities: http://sequoiaproject.org/wp-content/uploads/2017/02/2017-02-22-HIMSS-2017-Patient-Matching-Challenges-and-Opportunities-v001.pdf
Sovrin.org. What if someone steals my phone: https://sovrin.org/wp-content/uploads/2019/03/What-if-someone-steals-my-phone-040319.pdf
Sporny, M., Burnett, D.C., Longley, D., Kellogg, G.: Verifiable credentials data model 1.0: Expressing verifiable information on the web. Draft 7 (2018)
Sullivan, J.: Subject recruitment and retention: barriers to success, Appl. Clin. Trials (2004)
The 1984 National Minimum Drinking Age Act. Cambridge Analytica and Facebook: The Scandal and the Fallout So Far: https://alcoholpolicy.niaaa.nih.gov/the-1984-national-minimum-drinking-age-act
Windley, P.J.: How Sovrin works. Windely. com (2016)
Wood, G., et al.: Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper 151(2014), 1–32 (2014)
Doffman, Z.: Facebook Dark Web Deal: Hackers Just Sold 267 Million User Profiles For $540: https://www.forbes.com/sites/zakdoffman/2020/04/20/facebook-users-beware-hackers-just-sold-267-million-of-your-profiles-for-540/#7d4a52527c85
Zhang, P., Kamel Boulos, M.N.: Blockchain solutions for healthcare. In: Precision Medicine for Investigators, Practitioners and Providers, pp. 519–524. Elsevier (2020)
Zhang, P., Downs, C., Le, N.T.U., Martin, C., Shoemaker, P., Wittwer, C., Mills, L., Kelly, L., Lackey, S., Schmidt, D., et al.: Towards patient-centered stewardship of research data and research participant recruitment with blockchain technology. Front. Blockchain 3, 32 (2020)
Zhang, P., Schmidt, D.C., White, J., Lenz, G.: Blockchain technology use cases in healthcare. In: Advances in Computers, vol. 111, pp. 1–41. Elsevier (2018)
Zhang, P., Stodghill, B., Pitt, C., Briody, C., Schmidt, D.C., White, J., Pitt, A., Aldrich, K.: Optrak: Tracking opioid prescriptions via distributed ledger technology. Int. J. Inform. Syst. Soc. Change (IJISSC) 10(2), 45–61 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Zhang, P., Kuo, TT. (2021). The Feasibility and Significance of Employing Blockchain-Based Identity Solutions in Health Care. In: Patnaik, S., Wang, TS., Shen, T., Panigrahi, S.K. (eds) Blockchain Technology and Innovations in Business Processes. Smart Innovation, Systems and Technologies, vol 219. Springer, Singapore. https://doi.org/10.1007/978-981-33-6470-7_11
Download citation
DOI: https://doi.org/10.1007/978-981-33-6470-7_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6469-1
Online ISBN: 978-981-33-6470-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)