Integration of Genomic Data in Electronic Health Records

Sax, Ulrich; Schmidt, Steffen

doi:10.1055/s-0038-1634006

Cited by 26 publications

(4 citation statements)

References 29 publications

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“…EHR systems will need to be remodeled to integrate results of genome sequencing with other clinical information, preferably by leveraging existing informatics networks. 20 In particular, EHR systems will need to include genomic clinical decision support tools to allow clinicians to practice genomic medicine at point of care.…”

Section: Ehrs and Genomic Medicine: The Current Statementioning

confidence: 99%

Ethical, legal, and social implications of incorporating genomic information into electronic health records

Hazin

Malin

Koenig

et al. 2013

Genetics in Medicine

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The inclusion of genomic data in the electronic health record raises important ethical, legal, and social issues. In this article, we highlight these challenges and discuss potential solutions. We provide a brief background on the current state of electronic health records in the context of genomic medicine, discuss the importance of equitable access to genome-enabled electronic health records, and consider the potential use of electronic health records for improving genomic literacy in patients and providers. We highlight the importance of privacy, access, and security, and of determining which genomic information is included in the electronic health record. Finally, we discuss the challenges of reporting incidental findings, storing and reinterpreting genomic data, and nondocumentation and duty to warn family members at potential genetic risk.

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Section: Ehrs and Genomic Medicine: The Current Statementioning

confidence: 99%

Ethical, legal, and social implications of incorporating genomic information into electronic health records

Hazin

Malin

Koenig

et al. 2013

Genetics in Medicine

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“…From a bioinformatics perspective, the integration of genomic information in EHRs may lead to genotype-to-phenotype correlation analyses[167,168], and thus increase the importance of bioinformatics integration with laboratory and clinical information systems[169]. The ability to review radiological images or search for possible clinically relevant features within them has shown great promise by the imaging informatics community[170-174].…”

Section: Electronic Health Recordsmentioning

confidence: 99%

Biomedical informatics and translational medicine

Sarkar

2010

J Transl Med

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Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.

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“…Alone, or integrated with existing biomedical resources, these EPR systems constitute a rich resource for many types of data driven knowledge discovery as we demonstrate in this paper. In the coming years, as these data are also coupled to the expected explosion in personal genomic data, the translational meeting of ‘bench and bedside’ is expected to push scientific advancements in personalized medicine [4], [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts

et al. 2011

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Electronic patient records remain a rather unexplored, but potentially rich data source for discovering correlations between diseases. We describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner. By extracting phenotype information from the free-text in such records we demonstrate that we can extend the information contained in the structured record data, and use it for producing fine-grained patient stratification and disease co-occurrence statistics. The approach uses a dictionary based on the International Classification of Disease ontology and is therefore in principle language independent. As a use case we show how records from a Danish psychiatric hospital lead to the identification of disease correlations, which subsequently can be mapped to systems biology frameworks.

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Integration of Genomic Data in Electronic Health Records

Cited by 26 publications

References 29 publications

Ethical, legal, and social implications of incorporating genomic information into electronic health records

Ethical, legal, and social implications of incorporating genomic information into electronic health records

Biomedical informatics and translational medicine

Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts

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