First, the author explains that while the genomics process doesn’t paint a complete picture of “cancer behaviors and identify vital biomarkers that help determine what types of treatments patients will respond to,” he maintains high hopes for big data’s potential to address this, and notes that oncology-field stakeholders are now incentivizing hospitals and researchers to administer effective treatments yielded from big data analysis.
Next, Huss explains that, to zero in on the factors that “are most vital for achieving desired outcomes,” data reduction is critical, as it converts incomprehensible “patterns and formats in order to diagnose and develop personalized treatments for patients.”
After that, the author calls on the healthcare sector to reassess the necessary skill sets for oncologists in “this era of Big Data-based innovation” so that they can use data to make spontaneous decisions and treat their patients both quickly and accurately.
In his conclusion, Huss reiterates the importance of data reduction and lauds the federal government’s Precision Medicine Initiative as an effort that promises to effectively capitalize on big data’s promise by “strapping it down to develop individualized treatments as we continue to see the ‘one size fits all’ method be ineffective.”
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