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genetic clinical research to be translated into clinical application. A US Department
of Health and Human Services team is focused on integrating genomic data with
medical records to facilitate the development of personalized medicine. In 2009,
Aurora Health Care (Milwaukee, WI), which has close to two million patients,
unveiled its automated biorepository at St. Luke Medical Center. It has large-scale
data-sharing plans to integrate health records with omics data.
Optimal clinical use of genetic test results and molecularly-targeted therapies
present important challenges in patient management, which can be effectively
addressed by using electronic clinical decision support technologies. A working
group of the American Health Information Community has conducted assessment
of needs for clinical decision support in electronic health record systems to support
personalized medical practices. An action plan was suggested for government,
researchers and research institutions, developers of electronic information tools
(including clinical guidelines, and quality measures), and standards development
organizations to meet the needs for personalized approaches to medical practice. An
excellent publication has discussed the activities of stakeholder organizations to
identify and coordinate needs and opportunities for clinical decision support tools
to enable personalized medicine (Downing et al. 2009 ).
Management of Personal Genomic Data
Patient genomic data would be important for clinical decision making in a personal-
ized medical system. The management of such sizeable, yet fi ne-grained, data in
compliance with privacy laws and best practices presents signifi cant security and
scalability challenges. GenePING, an extension to the PING personal health record
system, is the fi rst personal health record management system to support the effi -
cient and secure storage and sharing of large genomic datasets (Adida and Kohane
2006 ). The design and implementation of GenePING has been published. It sup-
ports secure storage of large, genome-sized datasets, as well as effi cient sharing and
retrieval of individual datapoints (e.g. SNPs, rare mutations, gene expression lev-
els). Even with full access to the raw GenePING storage, it would be diffi cult for a
hacker to access any stored genomic datapoint on any single patient. Given a large-
enough number of patient records, an attacker cannot discover which data corre-
sponds to which patient, or even the size of a given patient’s record. The
computational overhead of GenePING’s security features is a small constant, mak-
ing the system usable, even in emergency care, on today’s hardware.
Use of EHRs for Improving Safety of New Medicines
Genetic variations infl uence susceptibility to adverse drug reaction (ADRs), and
predictive genetic tests have been developed for a limited number of ADRs. The
identifi cation of patients with ADRs, obtaining samples for genetic analysis and
rigorous evaluation of clinical test effectiveness are signifi cant challenges for
20 Development of Personalized Medicine