Abstract
We present a technique for the automatic identification of clinically-relevant patterns in medical datasets. To preserve patient privacy, we propose and implement the idea of treating medical dataset as a black box for both internal and external users of data. The proposed approach directly handles clinical data queries on a given medical dataset, unlike the conventional approach of relying on the data de-identification process. Our integrated toolkit combines software engineering technologies such as Java EE and RESTful web services, which allows exchanging medical data in an unidentifiable XML format and restricts users to computed information. Existing techniques could make it possible for an adversary to succeed in data re-identification attempts by applying advanced computational techniques; therefore, we disallow the use of retrospective processing of data. We validate our approach on an endoscopic reporting application based on openEHR and MST standards. The implemented prototype system can be used to query datasets by clinical researchers, governmental or non-governmental organizations in monitoring health care services to improve quality of care.
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Notes
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De-identification process is defined as a technology to delete or remove the identifiable information such as name, and SSN from the released information, and suppress or generalize quasi-identifiers, such as zip code date of birth, to ensure that medical data is not re-identifiable (the reverse process of de-identification.).
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Acknowledgments
The work presented in this paper was funded through National Science Foundation (NSF) TRUST (The Team for Research in Ubiquitous Secure Technology) Science and Technology Center Grant Number CCF-0424422. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NSF. This work has been partly supported by the project SAFEHR of Macao Science and Technology Development Fund (MSTDF) under grant 018/2011/AI.
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Qamar, N., Yang, Y., Nadas, A., Liu, Z., Sztipanovits, J. (2017). A Tool for Analyzing Clinical Datasets as Blackbox . In: Huhn, M., Williams, L. (eds) Software Engineering in Health Care. SEHC FHIES 2014 2014. Lecture Notes in Computer Science(), vol 9062. Springer, Cham. https://doi.org/10.1007/978-3-319-63194-3_15
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