In his presentation, Dr. Mark Blatt speaks extensively about the significance of genomic data for healthcare, the means for its application, and the issues that are related to it from the point of view of data analytics. The presenter is very well-versed on the topic, and he uses multiple examples to explain his points. He briefly mentions that in the past, genome sequencing was extremely difficult and resource-consuming. Indeed, having been launched in 1990, the Human Genome Project took 13 years to complete in 2003 (National Human Genome Research Institute, 2015, para. 1). However, today it is cheaper, easier, and still a problem for data analytics in healthcare.
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Dr. Blatt, as well as other researchers (Cui, 2015; Phillips et al., 2014; Raghupathi & Raghupathi, 2014), believe that genomic sequencing will become a routine test very soon, which will make it a regular part of the big data in healthcare analytics. The idea promises challenges as well as benefits. Genome tends to change, in particular, in response to certain interventions like chemotherapy. As a result, the process of cancer treatment requires frequent genetic resequencing, which presupposes analyzing enormous amounts of data (hundreds of exabytes). Moreover, Dr. Blatt points out that the genome of various organs also varies. Thus, the sequencing, which will become typical and frequent, is likely to take up much time and work again.
Apart from that, there is the issue of the exchange of these data amounts, which requires network storage. Dr. Blatt points out that the technology is developing, which is likely to enable faster sorting, storing, and exchange of the terabytes and exabytes of data. Cui (2015) also asserts that these developments are indeed likely to resolve the issues. Similarly, Blatt believes that data analytics has reached its maturing quite recently and mentioned that cloud-based storage networks that can handle the strain are in development. In other words, Dr. Blatt admits that genomic sequencing is a problem, but he is certain that solutions can be found. One of the examples of such solutions is the haplotype map (HapMap project), which is aimed at facilitating the search for the genes and their variations, and which researchers can use to study genes and related factors and test drugs (National Human Genome Research Institute, 2012).
Of the possible applications of genomics, Dr. Blatt mentions primarily pharmacogenomics because it is well-understood by modern science and is actively used to find out the mechanisms of drugs’ pathways. As a result, when the genomic sequencing becomes a regular test, healthcare specialists will be able to provide customized, personified prescriptions for their patients. Similarly, there is the possibility of a more accurate diagnosis, which Dr. Blatt also mentions and illustrates. These two applications seem to be very promising from the point of view of modern researchers (Cui, 2015, pp. 208-207). Apart from that, Dr. Blatt mentions genomics potential for cancer treatment and risk factors determination, which are also of great interest for healthcare (Phillips et al., 2014).
Dr. Blatt also mentions that there is still much to discover about genes. There are diagnoses that might be explained with the help of genomics but not with the current level of knowledge about it. The importance of the HapMap project becomes apparent in this connection. Thus, the presentation offers an insight into the topic of genomics from the point of view of healthcare and healthcare analytics, and it demonstrates the vast potential and the challenging nature of the field that is still developing rapidly to offer solutions to the related issues.
Cui, J. (2015). Genomic data analysis for personalized medicine. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 187-218). New York, NY: CRC Press.
National Human Genome Research Institute. (2012). International HapMap Project. Web.
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National Human Genome Research Institute. (2015). All about the Human Genome Project (HGP). Web.
Phillips, K., Trosman, J., Kelley, R., Pletcher, M., Douglas, M., & Weldon, C. (2014). Genomic sequencing: Assessing the health care system, policy, and big-data implications. Health Affairs, 33(7), 1246-1253. Web.
Raghupathi, W. & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3. Web.