Journal:Secure record linkage of large health data sets: Evaluation of a hybrid cloud model
| Full article title | Secure record linkage of large health data sets: Evaluation of a hybrid cloud model |
|---|---|
| Journal | JMIR Medical Informatics |
| Author(s) | Brown, Adrian P.; Randall, Sean M. |
| Author affiliation(s) | Curtin University |
| Primary contact | Email: adrian dot brown at curtin dot edu dot au |
| Year published | 2020 |
| Volume and issue | 8(9) |
| Article # | e18920 |
| DOI | 10.2196/18920 |
| ISSN | 2291-9694 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://medinform.jmir.org/2020/9/e18920/ |
| Download | https://medinform.jmir.org/2020/9/e18920/pdf (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Background: The linking of administrative data across agencies provides the capability to investigate many health and social issues, with the potential to deliver significant public benefit. Despite its advantages, the use of cloud computing resources for linkage purposes is scarce, with the storage of identifiable information on cloud infrastructure assessed as high-risk by data custodians.
Objective: This study aims to present a model for record linkage that utilizes cloud computing capabilities while assuring custodians that identifiable data sets remain secure and local.
Methods: A new hybrid cloud model was developed, including privacy-preserving record linkage techniques and container-based batch processing. An evaluation of this model was conducted with a prototype implementation using large synthetic data sets representative of administrative health data.
Results: The cloud model kept identifiers on-premises and used privacy-preserved identifiers to run all linkage computations on cloud infrastructure. Our prototype used a managed container cluster in Amazon Web Services to distribute the computation using existing linkage software. Although the cost of computation was relatively low, the use of existing software resulted in an overhead of processing of 35.7% (149/417 minutes execution time).
Conclusions: The result of our experimental evaluation shows the operational feasibility of such a model and the exciting opportunities for advancing the analysis of linkage outputs.
Keywords: cloud computing, medical record linkage, confidentiality, data science
Introduction
Background
In the last 10 years, innovative development of software applications, wearables, and the internet of things has changed the way we live. These technological advances have also changed the way we deliver health services and provide a rapidly expanding information resource, with the potential for data-driven breakthroughs in the understanding, treatment, and prevention of disease. Additional information from patient-related devices like mobile phone and Google search histories[1], wearable devices[2], and mobile phone apps[3] provides new opportunities for monitoring, managing, and improving health outcomes in new and innovative ways. The key to unlocking these data is in relating details at the individual patient level to provide an understanding of risk factors and appropriate interventions.[4] The linking, integration, and analysis of these data has recently been described as "population data science."[5]
Record linkage is a technique for finding records within and across one or more data sets thought to refer to the same person, family, place, or event.[6] Coined in 1946, the term describes the process of assembling the principal life events of an individual from birth to death.[7] In today’s digital age, the capacity of systems to match records has increased, yet the aim remains the same: linking records to construct individual chronological histories and undertake studies that deliver significant public benefit.
An important step in the evolution of data linkage is to develop infrastructure with the capacity to link data across agencies to create enduring integrated data sets. Such resources provide the capability to investigate many health and social issues. A number of collaborative groups have invested in a large-scale record linkage infrastructure to achieve national linkage objectives.[8][9] The establishment of research centers specializing in the analysis of big data also recognizes the issue of increasing data size and complexity.[10]
As the demand for data linkage increases, a core challenge will be to ensure that the systems are scalable. Record linkage is computationally expensive, with a potential comparison space equivalent to the Cartesian product of the record sets being linked, making linkage of large data sets (in the tens of millions or greater) a considerable challenge. Optimizing systems, removing manual operations, and increasing the level of autonomy for such processes is essential.
A wide range of software is currently used for record linkage. System deployments range from single desktop machines to multiple servers, with most being hosted internally to organizations. The functional scope of packages varies greatly, with manual processes and scripts required to help manage, clean, link, and extract data. Many packages struggle with large data set sizes.
Many industries have moved toward cloud computing as a solution for high computational workloads, data storage, and analytics.[11] An overview of cloud computing types and service models is shown in Table 1. The business benefits of cloud computing include usage-based costing, minimal upfront infrastructure investment, superior collaboration (both internally and externally), better management of data, and increased business agility.[12] Despite these advantages, uptake by the record linkage industry has been slow. One reason for this is that the storage of identifiable information on cloud infrastructure has been assessed as high-risk by data custodians. Although security in cloud computing systems has been shown to be more robust than some in-house systems[13], the media reporting of data breaches has created an impression of insecurity and vulnerability.<ref name="JohnMajor18">John, J.; Norman, J. (2018). "Major Vulnerabilities and Their Prevention Methods in Cloud Computing". In Peter, J.; Alavi, A.; Javadi, B.. Advances in Big Data and Cloud Computing. Springer. pp. 11–26. doi:10.1007/978-981-13-1882-5_2. ISBN 9789811318825.
Cite error: The opening <ref> tag is malformed or has a bad name A culture of risk aversion leaves the record linkage units with expensive, dedicated equipment and computing resources that require managing, maintaining, and upgrading or replacing regularly.
References
- ↑ Abebe, R.; Hill, S.; Vaughan, J.W. et al. (2019). "Using Search Queries to Understand Health Information Needs in Africa". Proceedings of the Thirteenth International AAAI Conference on Web and Social Media 13 (1): 3–14. https://ojs.aaai.org/index.php/ICWSM/article/view/3360.
- ↑ Radin, J.M.; Wineinger, N.E.; Topol, E.J. et al. (2020). "Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: A population-based study". The Lancet Digital Health 2 (2): e85–e93. doi:10.1016/S2589-7500(19)30222-5.
- ↑ Lai, S.; Farnham, A.; Ruktanonchai, N.W. et al. (2019). "Measuring mobility, disease connectivity and individual risk: A review of using mobile phone data and mHealth for travel medicine". Journal of Travel Medicine 26 (3): taz019. doi:10.1093/jtm/taz019. PMC PMC6904325. PMID 30869148. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6904325.
- ↑ Khoury, M.J.; Iademarco, M.F.; Tiley, W.T. (2016). "Precision Public Health for the Era of Precision Medicine". American Journal of Prevantative Medicine 50 (3): 398-401. doi:10.1016/j.amepre.2015.08.031. PMC PMC4915347. PMID 26547538. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4915347.
- ↑ McGrail, K.; Jones, K. (2018). "Population Data Science: The science of data about people". Conference Proceedings for International Population Data Linkage Conference 2018 3 (4). doi:10.23889/ijpds.v3i4.918.
- ↑ Christen, P. (2012). Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer-Verlag. doi:10.1007/978-3-642-31164-2. ISBN 9783642311642.
- ↑ Dunn, H.L. (1946). "Record Linkage". American Journal of Public Health and the Nation's Health 36 (12): 1412–6. PMC PMC1624512. PMID 18016455. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1624512.
- ↑ Casey, J.A.; Schwartz, B.S.; Stewart, W.F. et al. (2016). "Using Electronic Health Records for Population Health Research: A Review of Methods and Applications". Annual Review of Public Health 37: 61–81. doi:10.1146/annurev-publhealth-032315-021353. PMC PMC6724703. PMID 26667605. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6724703.
- ↑ Population Health Research Network (17 April 2014). "Population Health Research Network 2013 Independent Panel Review: Findings and Recommendations" (PDF). https://www.phrn.org.au/media/80607/phrn-2013-independent-review-findings-and-recommendations-v2-_final-report-april-17-2014-2.pdf. Retrieved 15 September 2020.
- ↑ UNSW Australia (2015). "Centre for Big Data Research in Health: Annual Report 2015" (PDF). https://cbdrh.med.unsw.edu.au/sites/default/files/CBDRH_Annual%20Report_2015_160609_Final.pdf. Retrieved 15 September 2020.
- ↑ Liversidge, J.; Spencer, J.; Weinstein, E. et al. (21 December 2018). "Predicts 2019: Cloud Adoption and Increasing Regulation Will Drive Investment in IT Vendor Management". Gartner Research. https://www.gartner.com/en/documents/3896211/predicts-2019-cloud-adoption-and-increasing-regulation-w. Retrieved 15 September 2020.
- ↑ Vasiljeva, T.; Shaikhulina, S.; Kreslins, K. (2017). "Cloud Computing: Business Perspectives, Benefits and Challenges for Small and Medium Enterprises (Case of Latvia)". Procedia Engineering 178: 443–51. doi:10.1016/j.proeng.2017.01.087.
- ↑ Khalil, I.M.; Khreishah, A.; Bouktig, S. et al. (2013). "Security Concerns in Cloud Computing". Proceedings of the 10th International Conference on Information Technology: New Generations: 411-416. doi:10.1109/ITNG.2013.127.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was cleaned up for smoother reading. In some cases important information was missing from the references, and that information was added. At the time of loading of this article, the links to the Additional File 1 and 2 were broken on the original site; a request to fix the errors has been sent to the journal.
