How to De-Identify Your Data: Balancing Accuracy and Privacy
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I'm not sure what measures Mimic takes (beyond just stripping demographic info), but a given patient's pattern of healthcare interactions make quite a unique fingerprint -- and some parts of that fingerprint are likely public information for some patients.
E.g., imagine a celebrity who (you can find from the tabloids) was treated at X hospital for a sprained ankle on 2012-07-14, and gave birth to a daughter at Hospital Y on 2014-04-01. If her record -- completely "anonymized" -- is in a data set that lets you search for patients matching these two events... it seems fairly likely you'd be able to narrow it down to only a few candidates, or quite likely an exact match. And then once you have her pseudonym/ID, does the rest of the record reveal anything interesting? An abortion no one knew about (possibly not even her partner)? A venereal disease treatment?
Even the fact that a patient had an appointment at a given clinic is sensitive data -- e.g., seeing an IVF specialist, or oncologist, etc..
It's a tricky field to navigate.
If someone is interested in tools for data de-identification, ARX [1, 2] is an open source software that (among other features) supports exactly the set of methods used in this study.
Full disclosure: I'm one of the developers of ARX.
[1] Website: http://arx.deidentifier.org
[2] Source: https://github.com/arx-deidentifier/arx