Legal Ontologies for Protecting Genetic Privacy and Their Enforcement Mechanism

The use of genetic information for treatment and healthcare research has greatly increased over the last few years to provide important diagnostic data from patients to their health care providers and researchers that match phenotypes and genotypes. For patients to be willing to agree for their genetic information to be collected and used in these settings, they must be confident that their rights and directions will be protected. Researchers, healthcare professionals, medical ethicists, law enforcement representatives and other genetic information users must also be confident that they are not violating these legal protections and directives to avoid penalties. This dissertation provides a mechanism to address such concerns through an integrated approach.;Workflows are designed to operate directly in the provider's Electronic Health Record (EHR) to minimize data entry and protect genetic information at the source along with an enforcement mechanism that also incorporates the patient's informed consent. I have created a comprehensive ontology that models the applicable Federal and State laws to help ensure the appropriate safeguards are in place and the requirements for privacy protection are addressed. My contributions include creating a structured rule base with a formal framework that allows the laws to be translated into an executable rules enforceable on Electronic Health Record (EHR) systems. I developed a Rule Hierarchy Algorithm that combines the access results based on Federal, State and Organization laws, policies, and regulations into one consolidated decision including the associated directives, informed consent, and release requirements. These integrated components are enforced with a workflow system and a consent management architecture to provide the necessary security and privacy protections against misuse in the targeted legal areas including employment, insurance, research, and treatment.