Risk and Compliance CoP

IHE: Analysis of Optimal De-Identification Algorithms for Family Planning Data Elements

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  • December 3, 2016
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IHE: Analysis of Optimal De-Identification Algorithms for Family Planning Data Elements

This is a use of the IHE published De-Identification Handbook against a use-case. The conclusion we came to is an important lesson, that sometimes the use-case needs can’t be met with de-identification to a a level of ‘public access’.  That is that the ‘needs’ of the ‘use-case’ required so much data to be left in the resulting-dataset, that the resulting-dataset could not be considered publicly accessible. This conclusion was not much of a problem in this case as the resulting-dataset was not anticipated to be publicly accessible. The de-identification recommended was still useful as it did reduce risk, just not fully. That is that the data was rather close to fully de-identified; just not quite. The reduced risk is still helpful. Alternative use-case segmentation could have been done. That is we could have created two sets of use-cases, that each targeted different elements while also not enabling linking between the two resulting-datasets. However this was seen as too hard to manage, vs the additional risk reduction. Further articles on De-Identification

        

IHE IT Infrastructure White Paper Published The IHE IT Infrastructure Technical Committee has published the following white paper as of December 2, 2016:

Analysis of Optimal De-Identification Algorithms for Family Planning Data Elements  

The document is available for download at http://ihe.net/Technical_Frameworks. Comments on all documents are invited at any time and can be submitted at ITI Public Comments.