Authors:
Michael Reep
;
Bo Yu
;
Duminda Wijesekera
and
Paulo Costa
Affiliation:
George Mason University, United States
Keyword(s):
Genetic Privacy, Electronic Medical Records, Ontology, Health Care, Genomic Medicine, SWRL (Semantic Web Rule Language).
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Confidentiality and Data Security
;
Health Information Systems
Abstract:
Clinical medical practice and biomedical research utilize genetic information for specific purposes.
Irrespective of the purpose of obtaining genetic material, methodologies for protecting the privacy of
patients/donors in both clinical and research settings have not kept pace with rapid advances in genetics
research. When the usage of genetic information is not predicated on the latest laws and policies, the result
places all-important patient/donor privacy at risk. Some methodologies err on the side of overly stringent
policies that may inhibit research and open-ended diagnostic activity, whereas an opposite approach advocates
a high-degree of openness that can jeopardize patient privacy, inappropriately identify disease susceptibility
of patients and their genetic relatives, and thereby erode the doctor-patient privilege. As a solution, we present
a framework based on the premise that acceptable clinical treatment regimens are captured in workflows used
by caregivers and researche
rs and therefore their associated purpose are inherent to and therefore can be
extracted from these workflows. We combine these purposes with applicable consents that are derived from
applicable laws and practice standards to ascertain the releasability of genetic information. Given that federal,
state and institutional laws, rules and regulations govern the use, retention and sharing of genetic information,
we create a three-level rule hierarchy to apply the laws to a request and auto-generate consents prior to
releasing. Our hierarchy also identifies all pre-conditions that must be met prior to the genetic information
release, any restrictions and constraints to be enforced after release, and the penalties that may be assessed for
violating these terms. We prototype our system using open source tools, while ensuring that the results can
be added to existing Electronic Medical Records (EMR) systems.
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