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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. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Reep, M.; Yu, B.; Wijesekera, D. and Costa, P. (2018). Sharing Genetic Data under US Privacy Laws. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF; ISBN 978-989-758-281-3; ISSN 2184-4305, SciTePress, pages 349-360. DOI: 10.5220/0006550303490360

@conference{healthinf18,
author={Michael Reep. and Bo Yu. and Duminda Wijesekera. and Paulo Costa.},
title={Sharing Genetic Data under US Privacy Laws},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF},
year={2018},
pages={349-360},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006550303490360},
isbn={978-989-758-281-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - HEALTHINF
TI - Sharing Genetic Data under US Privacy Laws
SN - 978-989-758-281-3
IS - 2184-4305
AU - Reep, M.
AU - Yu, B.
AU - Wijesekera, D.
AU - Costa, P.
PY - 2018
SP - 349
EP - 360
DO - 10.5220/0006550303490360
PB - SciTePress