The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability
Open accessAbstract
Interoperability in healthcare has traditionally been focused around data exchange between business entities, for example, different hospital systems. However, there has been a recent push towards patient-driven interoperability, in which health data exchange is patient-mediated and patient-driven. Patient-centered interoperability, however, brings with it new challenges and requirements around security and privacy, technology, incentives, and governance that must be addressed for this type of data sharing to succeed at scale. In this paper, we look at how blockchain technology might facilitate this transition through five mechanisms: (1) digital access rules, (2) data aggregation, (3) data liquidity, (4) patient identity, and (5) data immutability. We then look at barriers to blockchain-enabled patient-driven interoperability, specifically clinical data transaction volume, privacy and security, patient engagement, and incentives. We conclude by noting that while patient-driving interoperability is an exciting trend in healthcare, given these challenges, it remains to be seen whether blockchain can facilitate the transition from institution-centric to patient-centric data sharing.
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“First, the designs of conventional access control strategies [14]-[16] require external key authorities with a Public Key Infrastructure (PKI), which can be extremely complex and usually demands expensive resources to achieve secure EHRs sharing [25].”
From Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems (2019) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“More importantly, conventional access control systems mainly rely on a predefined point of access, i.e. a centralized cloud server, and this can lead to the central point of failure for e-health networks [25].”
From Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems (2019) · cites this paper as [25]
Experimental — accuracy not yet measuredIn the citing paper
“Our system is a hyperledger composer [19] based Blockchain application that alleviates these problems with an architecture that allows our system to be both extensible and scalable: a blockchain based EACMS, off chain data storage and a patient-centered mobile and web interface.”
From EACMS: Emergency Access Control Management System for Personal Health Record Based on Blockchain (2019) · cites this paper as [19]
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Citations
Cites
Bitcoin: A Peer-to-Peer Electronic Cash SystemPreprint
Nakamoto, S. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3440802 · Cited by 14,677
Decentralizing Privacy: Using Blockchain to Protect Personal Data
Zyskind, G., Nathan, O., & Pentland, A. '. (2015) · 2015 IEEE Security and Privacy Workshops · doi:10.1109/spw.2015.27 · Cited by 2,617
MedRec: Using Blockchain for Medical Data Access and Permission Management
Azaria, A., Ekblaw, A., Vieira, T., & Lippman, A. (2016) · 2016 2nd International Conference on Open and Big Data (OBD) · doi:10.1109/obd.2016.11 · Cited by 2,526
Blockchain distributed ledger technologies for biomedical and health care applications
Kuo, T.-T., Kim, H.-E., & Ohno-Machado, L. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx068 · Cited by 1,350
Yue, X., Wang, H., Jin, D., Li, M., & Jiang, W. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0574-6 · Cited by 1,240
Blockchain technology in healthcare: The revolution starts here
Mettler, M. (2016) · 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2016.7749510 · Cited by 1,237
SMART on FHIR: a standards-based, interoperable apps platform for electronic health records
Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv189 · Cited by 829
Walker, J., Pan, E., Johnston, D., Adler-Milstein, J., Bates, D. W., & Middleton, B. (2005) · Health Affairs · doi:10.1377/hlthaff.w5.10 · Cited by 811
Fhirchain: applying blockchain to securely and scalably share clinical data
Zhang, P., White, J., Schmidt, D. C., Lenz, G., & Rosenbloom, S. T. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.07.004 · Cited by 796
Secure and Trustable Electronic Medical Records Sharing using Blockchain
Dubovitskaya, A., Xu, Z., Ryu, S., Schumacher, M., & Wang, F. (2017) · PubMed Central · doi:10.48550/arxiv.1709.06528 · Cited by 465
Simple Demographics Often Identify People Uniquely
Sweeney, L. (2018) · Figshare · doi:10.1184/r1/6625769 · Cited by 456
The HL7 Clinical Document Architecture
Dolin, R. H., Alschuler, L., Beebe, C. E., Biron, P. V., Boyer, S. L., Essin, D., Kimber, E., Lincoln, T. M., & Mattison, J. E. (2001) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2001.0080552 · Cited by 448
Blockchain Technology: Applications in Health Care
Angraal, S., Krumholz, H. M., & Schulz, W. L. (2017) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.117.003800 · Cited by 430
Some Simple Economics of the Blockchain
Catalini, C., & Gans, J. S. (2016) · National Bureau of Economic Research · doi:10.3386/w22952 · Cited by 404
Self-citationPatients in context--EHR capture of social and behavioral determinants of health
Adler, N. E., & Stead, W. W. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1413945 · Cited by 317
Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2
Domains, B. (2015) · Cited by 299
Some Simple Economics of the BlockchainPreprint
Catalini, C., & Gans, J. S. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2874598 · Cited by 237
Self-citationGold, R., Cottrell, E., Bunce, A., Middendorf, M., Hollombe, C., Cowburn, S., Mahr, P., & Melgar, G. (2017) · The Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2017.04.170046 · Cited by 215
Cohen, D. J., Keller, S., Hayes, G. R., Dorr, D. A., Ash, J. S., & Sittig, D. F. (2016) · JMIR Human Factors · doi:10.2196/humanfactors.5919 · Cited by 153
Rahurkar, S., Vest, J. R., & Menachemi, N. (2015) · Health Affairs · doi:10.1377/hlthaff.2014.0729 · Cited by 133
Volume and value of big healthcare data
Dinov, I. D. (2016) · Journal of Medical Statistics and Informatics · doi:10.7243/2053-7662-4-3 · Cited by 130
Progress In Interoperability: Measuring US Hospitals’ Engagement In Sharing Patient Data
Holmgren, A. J., Patel, V., & Adler‐Milstein, J. (2017) · Health Affairs · doi:10.1377/hlthaff.2017.0546 · Cited by 120
Stewart, B. A., Fernandes, S. M., Rodriguez-Huertas, E., & Landzberg, M. J. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001750 · Cited by 92
Transitional Chaos or Enduring Harm? The EHR and the Disruption of Medicine
Rosenbaum, L. R. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1509961 · Cited by 75
Your Patient Is Now Reading Your Note: Opportunities, Problems, and Prospects
Klein, J. W., Jackson, S. L., Bell, S. K., Anselmo, M. K., Walker, J., Delbanco, T., & Elmore, J. G. (2016) · The American Journal of Medicine · doi:10.1016/j.amjmed.2016.05.015 · Cited by 68
Downing, N. L., Adler‐Milstein, J., Palma, J. P., Lane, S., Eisenberg, M. A., Sharp, C., Collaborative, N. C. H., & Longhurst, C. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw063 · Cited by 51
Chung, A. E., Sandler, R. S., Long, M. D., Ahrens, S., Burris, J. L., Martin, C. F., Anton, K., Robb, A., Caruso, T. P., Jaeger, E., et al. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv191 · Cited by 50
Just, B. H., Marc, D. T., Munns, M., & Sandefer, R. H. (2016) · PubMed Central · Cited by 50
FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical DataPreprint
Zhang, P., White, J., Schmidt, D. C., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.03227 · Cited by 48
Zhou, Y., Ancker, J. S., Upahdye, M., McGeorge, N., Guarrera, T. K., Hedge, S., Crane, P. W., Fairbanks, R. J. T., Bisantz, A. M., Kaushal, R., et al. (2014) · Journal of Innovation in Health Informatics · doi:10.14236/jhi.v21i1.36 · Cited by 39
Duplicate patient records--implication for missed laboratory results.
Joffe, E., Bearden, C. F., Byrne, M. J., & Bernstam, E. V. (2012) · PubMed Central · Cited by 18
Applications of health information exchange information to public health practice.
Kierkegaard, P., Kaushal, R., & Vest, J. R. (2014) · PubMed · Cited by 14
Joffe, E., Byrne, M. J., Reeder, P., Herskovic, J. R., Johnson, C. W., McCoy, A. B., & Bernstam, E. V. (2013) · Europe PMC (PubMed Central) · Cited by 6
Flagged first, then by citations
Research works printed but not linked: 9
- Health Affairs Health Policy Brief : Interoperability. : 2014.
- Office of the National Coordinator for Health Information Technology : Connecting health and care for the nation: A shared nationwide interoperability roadmap. : 2015.
- Julia Adler-Milstein. Moving past the EHR interoperability blame game. N Engl J Catal n.d. https://catalyst.nejm.org/ehr-interoperability-blame-game/ (accessed March 28 2018).
- Gordon W.J. Wright A. and Landman A.: Blockchain Technology in Health Care: Decoding the hype. NEJM Catal 2017; . https://catalyst.nejm.org/decoding-blockchain-technology-health/.
- Bipartisan Policy Center Health Information : Technology initiative. In: Clinician perspectives on electronic health information sharing for transitions of care. : 2012.
- Access of individuals to protected health information (45 CFR § 164.524). : 2014.
- Centers for Medicare & Medicaid Services (CMS) and HHS : Medicare and medicaid programs; electronic health record incentive program—Stage 3 and modifications to meaningful use in 2015 through 2017. : 2015.
- Ivan Drew: Moving toward a blockchain-based method for the secure storage of patient records, NIST/ONC. : 2016.
- Gordon W.J. and Landman A.: Secure, decentralized, interoperable medication reconciliation using the Blockchain. : NIST/ONC 2016.
Cited by
Sorted by citations
Blockchain technology in healthcare: a systematic review
Agbo, C. C., Mahmoud, Q. H., & Eklund, J. M. (2019) · Healthcare · doi:10.3390/healthcare7020056 · Cited by 1,131
Blockchain for healthcare data management: opportunities, challenges, and future recommendations
Yaqoob, I., Salah, K., Jayaraman, R., & Al-Hammadi, Y. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05519-w · Cited by 687
Using Blockchain for Electronic Health Records
Shahnaz, A., Qamar, U., & Khalid, A. (2019) · IEEE Access · doi:10.1109/access.2019.2946373 · Cited by 594
Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems
Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2019) · IEEE Access · doi:10.1109/access.2019.2917555 · Cited by 574
“Recently, there has been a growing interest in employing the blockchain technology to promote medical and e-health services [1]–[3].”
BackgroundA Survey on Blockchain for Information Systems Management and Security
Berdik, D., Otoum, S., Schmidt, N., Porter, D., & Jararweh, Y. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102397 · Cited by 605
Metadata
- Authors
- Gordon, W. J., & Catalini, C.
- Affiliations
- Brigham and Women's Hospital (US)
- National Bureau of Economic Research (US)
- Corresponding author
- Gordon, William J.
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2018-01
- Volume
- 16
- Pages
- 224-230
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC6068317
- OpenAlex
- W2810476200
- Cited by
- 828
- References
- 34
- Citations by year
- 2026: 52 · 2025: 80 · 2024: 94 · 2023: 126 · 2022: 151 · 2021: 135 · 2020: 112 · 2019: 78
- FWCI
- 15.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- blockchain, health data sharing, data privacy, data security, patient identity, data immutability, data aggregation, patient engagement, incentives, governance, barriers to adoption
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