Privacy technology to support data sharing for comparative effectiveness research: a systematic review.

TitlePrivacy technology to support data sharing for comparative effectiveness research: a systematic review.
Publication TypeJournal Article
Year of Publication2013
AuthorsJiang, X, Sarwate, AD, Ohno-Machado, L
JournalMed Care
Volume51
Issue8 Suppl 3
PaginationS58-65
Date Published2013 Aug
ISSN1537-1948
iDASH CategoryPrivacy Technology
AbstractOBJECTIVE: Effective data sharing is critical for comparative effectiveness research (CER), but there are significant concerns about inappropriate disclosure of patient data. These concerns have spurred the development of new technologies for privacy-preserving data sharing and data mining. Our goal is to review existing and emerging techniques that may be appropriate for data sharing related to CER. MATERIALS AND METHODS: We adapted a systematic review methodology to comprehensively search the research literature. We searched 7 databases and applied 3 stages of filtering based on titles, abstracts, and full text to identify those works most relevant to CER. RESULTS: On the basis of agreement and using the arbitrage of a third party expert, we selected 97 articles for meta-analysis. Our findings are organized along major types of data sharing in CER applications (ie, institution-to-institution, institution hosted, and public release). We made recommendations based on specific scenarios. LIMITATION: We limited the scope of our study to methods that demonstrated practical impact, eliminating many theoretical studies of privacy that have been surveyed elsewhere. We further limited our study to data sharing for data tables, rather than complex genomic, set valued, time series, text, image, or network data. CONCLUSION: State-of-the-art privacy-preserving technologies can guide the development of practical tools that will scale up the CER studies of the future. However, many challenges remain in this fast moving field in terms of practical evaluations and applications to a wider range of data types.
DOI10.1097/MLR.0b013e31829b1d10
Alternate JournalMed Care
PubMed ID23774511