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Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the continuity of care record standard

Jing, X, Kay, S, Marley, T, Hardiker, NR and Cimino, J 2011, 'Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the continuity of care record standard' , Journal of Biomedical Informatics . (In Press)

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Abstract

Objectives: the current volume and complexity of genetic tests, and the molecular genetics knowledge and health knowledge related to interpretation of the results of those tests, are rapidly outstripping the ability of individual clinicians to recall, understand and convey to their patients information relevant to their care. The tailoring of molecular genetics knowledge and health knowledge in clinical settings is important both for the provision of personalized medicine and to reduce clinician information overload. In this paper we describe the incorporation, customization and demonstration of molecular genetic data (mainly sequence variants), molecular genetics knowledge and health knowledge into a standards-based electronic health record (EHR) prototype developed specifically for this study. Methods: we extended the CCR (Continuity of Care Record), an existing EHR standard for representing clinical data, to include molecular genetic data. An EHR prototype was built based on the extended CCR and designed to display relevant molecular genetics knowledge and health knowledge from an existing knowledge base for cystic fibrosis (OntoKBCF). We reconstructed test records from published case reports and represented them in the CCR schema. We then used the EHR to dynamically filter molecular genetics knowledge and health knowledge from OntoKBCF using molecular genetic data and clinical data from the test cases. Results: the molecular genetic data were successfully incorporated in the CCR by creating a category of laboratory results called “Molecular Genetics” and specifying a particular class of test (“Gene Mutation Test”) in this category. Unlike other laboratory tests reported in the CCR, results of tests in this class required additional attributes (“Molecular Structure” and “Molecular Position”) to support interpretation by clinicians. These results, along with clinical data (age, sex, ethnicity, diagnostic procedures, and therapies) were used by the EHR to filter and present molecular genetics knowledge and health knowledge from OntoKBCF. Conclusions: this research shows a feasible model for delivering patient sequence variants and presenting tailored molecular genetics knowledge and health knowledge via a standards-based EHR system prototype. EHR standards can be extended to include the necessary patient data (as we have demonstrated in the case of the CCR), while knowledge can be obtained from external knowledge bases that are created and maintained independently from the EHR. This approach can form the basis for a personalized medicine framework, a more comprehensive standards-based EHR system and a potential platform for advancing translational research by both disseminating results and providing opportunities for new insights into phenotype-genotype relationships.

Item Type: Article
Uncontrolled Keywords: Molecular genetic information, sequence variants, electronic health record, personalized information, standards, information filters
Themes: Health and Wellbeing
Schools: Colleges and Schools > College of Health & Social Care
Colleges and Schools > College of Health & Social Care > School of Nursing, Midwifery, Social Work & Social Sciences > Centre for Nursing, Midwifery & Social Work Research
Colleges and Schools > College of Health & Social Care > School of Nursing, Midwifery, Social Work & Social Sciences
Journal or Publication Title: Journal of Biomedical Informatics
Publisher: Elsevier
Refereed: Yes
ISSN: 15320464
Depositing User: NR Hardiker
Date Deposited: 22 Sep 2011 10:13
Last Modified: 20 Sep 2013 13:40
URI: http://usir.salford.ac.uk/id/eprint/17670

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