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Electronoc Medical Records And Genomic Information

868 words - 4 pages

The Electronic Medical Records and Genomics (eMERGE) Network is a National Human Genome Research Institute (NHGRI)–funded consortium which has been developing tools and practices for the utilization of electronic medical records (EMR) and genomic information towards patient healthcare. As collaborative effort between 9 different centers, eMERGE is targeted towards the utilization of phenotypic information to find out causative factors for genetic disorders, pharmacogenomics studies, predisposition of individuals to certain conditions such childhood obesity, autism and the integration of the results genetic studies into EMRs [1].
Any study of such a nature requires that the quality of data found in both the EMR and the genetic repository meet a certain level of uniformity and quality. This can be a challenge for most EMR systems. There is an inherent variation in the EMRs due to different styles of implementation of the EMR systems, difference in data recording styles of different physicians and the variations due to disease requirements for the patients. In addition to these, there is a large amount of medical information captured as free notes. The processing of such information requires natural language processing which is still in the research arena. The area is not mature enough to guarantee uniform and good quality information to facilitate clinical decision support systems [2]. To give good phenotypic information for genetic studies, and integrated EHR needs to have information regarding the mutations present in an individual and also the family history, though the family member may not exhibit the clinical syndrome [2]. Such details however may not be available in most EHR systems today. The size of the genetic data is an additional complication in the integration.
Apart from potentially being very large, an additional attribute of next-generation sequencing genomic data is that it has uneven depth of coverage and hence data regarding the quality of the data need to be stored. A way to reduce the complexity of genomic data is to process it to a set of variants or even further to a list of known pathologic variants and thus only store variants or selected variants [3]. This solution however poses some specific requirements on the structure of the EHR to accommodate the integration of genetic test. For example, it must be possible for the EHR to store the various components of the results (the gene, protein/nucleotide variant, single nucleotide polymorphism, copy-number variant etc.) in a discrete computable format. They should be suitable for interoperability, data transfer and updation as the tests evolve. This solution also poses an additional challenge of dealing with false positives and false negatives being generated with the...

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