To move past the idiosyncratic nature of most medical treatments, we need to amass properly collected and curated biological data to compare and correlate outcomes and biomarkers across varying patient populations. Historically, however, most treatment plans are deeply subjective, due to the many disparate pieces of information that physicians must tie together to make a health plan based on the individual’s specific conditions. This post introduces gene analysis using Spark on EMR and ADAM, for those new to precision medicine.ĭata-driven tailored treatments have been commonplace for certain treatments like blood transfusions for a long time. Spark is already known for being a major player in big data analysis, but it is additionally uniquely capable in advancing genomics algorithms given the complex nature of genomics research. The healthcare ecosystem has chosen a variety of tools and techniques for working with big data, but one tool that comes up again and again in many of the architectures we design and review is Spark on Amazon EMR. Achieving this mission relies on the intersection of several technology innovations and a major restructuring of health data to focus on the genetic makeup of an individual. Precision medicine typically describes an approach for treating and preventing disease that takes into account a patient’s individual variation in genes, lifestyle, and environment. The promise of precision medicine that President Obama envisions with this statement is a far-reaching goal that will require sweeping changes to the ways physicians treat patients, health data is collected, and global collaborative research is performed. “And that’s the promise of precision medicine - delivering the right treatments, at the right time, every time to the right person.“ (President Obama, 2015 State of the Union address)

This post was co-authored by Ujjwal Ratan, a Solutions Architect with Amazon Web Services.

Christopher Crosbie is a Healthcare and Life Science Solutions Architect with Amazon Web Services.

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