Abstract:
BACKGROUND:Over the past decade, the emergence of several large federated clinical data networks has enabled researchers to access data on millions of patients at dozens of health care organizations. Typically, queries are broadcast to each of the sites in the network, which then return aggregate counts of the number of matching patients. However, because patients can receive care from multiple sites in the network, simply adding the numbers frequently double counts patients. Various methods such as the use of trusted third parties or secure multiparty computation have been proposed to link patient records across sites. However, they either have large trade-offs in accuracy and privacy or are not scalable to large networks. OBJECTIVE:This study aims to enable accurate estimates of the number of patients matching a federated query while providing strong guarantees on the amount of protected medical information revealed. METHODS:We introduce a novel probabilistic approach to running federated network queries. It combines an algorithm called HyperLogLog with obfuscation in the form of hashing, masking, and homomorphic encryption. It is tunable, in that it allows networks to balance accuracy versus privacy, and it is computationally efficient even for large networks. We built a user-friendly free open-source benchmarking platform to simulate federated queries in large hospital networks. Using this platform, we compare the accuracy, k-anonymity privacy risk (with k=10), and computational runtime of our algorithm with several existing techniques. RESULTS:In simulated queries matching 1 to 100 million patients in a 100-hospital network, our method was significantly more accurate than adding aggregate counts while maintaining k-anonymity. On average, it required a total of 12 kilobytes of data to be sent to the network hub and added only 5 milliseconds to the overall federated query runtime. This was orders of magnitude better than other approaches, which guaranteed the exact answer. CONCLUSIONS:Using our method, it is possible to run highly accurate federated queries of clinical data repositories that both protect patient privacy and scale to large networks.
journal_name
J Med Internet Resjournal_title
Journal of medical Internet researchauthors
Yu YW,Weber GMdoi
10.2196/18735subject
Has Abstractpub_date
2020-11-03 00:00:00pages
e18735issue
11eissn
1439-4456issn
1438-8871pii
v22i11e18735journal_volume
22pub_type
杂志文章abstract:BACKGROUND:In many countries, there has been substantial progress in establishing the electronic transmission of patients' health information between health care providers, but little is known about how best to engage patients in the process. OBJECTIVE:We explored patients' views about sharing of electronic health inf...
journal_title:Journal of medical Internet research
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abstract::[This corrects the article DOI: 10.2196/13980.]. ...
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