  This paper takes on the problem of automatically identifying
clinically-relevant patterns in medical datasets without compromising patient
privacy. To achieve this goal, we treat datasets as a black box for both
internal and external users of data that lets us handle clinical data queries
directly and far more efficiently. The novelty of the approach lies in avoiding
the data de-identification process often used as a means of preserving patient
privacy. The implemented toolkit combines software engineering technologies
such as Java EE and RESTful web services, to allow exchanging medical data in
an unidentifiable XML format as well as restricting users to the need-to-know
principle. Our technique also inhibits retrospective processing of data, such
as attacks by an adversary on a medical dataset using advanced computational
methods to reveal Protected Health Information (PHI). The approach is validated
on an endoscopic reporting application based on openEHR and MST standards. From
the usability perspective, the approach can be used to query datasets by
clinical researchers, governmental or non-governmental organizations in
monitoring health care services to improve quality of care.
