5 Fundamental Questions to Ask About Precision Medicine and Big Data

November 10, 2016 Mike Pallagi

Michael Snyder, PhD, Director of the Center of Genomics and Personalized Medicine at Stanford University, asked and answered five essential precision medicine and big data questions in Chapter 16 of his 2016 book, “Genomics and Personalized Medicine: What Everyone Needs to Know.” (While the author doesn’t use the phrase “precision medicine” in this chapter, that’s exactly what he’s talking about.) Throughout the chapter, Snyder echoed many of the themes and perspectives we’ve explored in this blog, but injected into the discussion some fresh real-world examples that add exciting new dimensions to the concepts.

These questions include:

  1. How much data can be gathered about a single person? Snyder touched on the familiar territory of collected “omics” and other personal data and how it will yield vast quantities of information, noting that with a series of moments gathered throughout a single life, vast volumes of data could readily be accrued, but then issued a warning to those who might try to reduce data quantities by throwing out raw data and keeping mere summaries, noting that “as new algorithms are continuously developed that improve the analysis of information that can be extracted from raw data … individuals will get their raw data reanalyzed frequently, resulting in an improvement in interpretation. 
  1. How much data can be gathered about a group of people? In line with many of his thought-leader peers, Snyder predicted that millions will soon link their genomic sequences to their medical records, which will yield large interconnected databases and enable exciting discoveries. Still, he’s concerned about the ability of these databases to collect data in a readily sharable format. He added that health information is very heterogenous and that a single condition can have a variety of labels, and he supported this position by citing a fascinating anecdote: “…to measure the ability of blood to clot, a test was developed in the 1930s called prothrombin time. The reagents used to perform this test varied widely, however, and the prothrombin time reported from different labs could not be compared until the international normalized ratio was developed to standardize values across labs.” He then related this to how there are various ways to gather measurements like blood pressure, and maintained that these various approaches may yield different numbers. Thus, wrote Snyder, it's essential for data to be entered in a widely used format and enriched with details describing how the numbers were arrived upon so that we can make comparisons of real value.
  1. How can a large database assist in medical care? The author described how data—rather than assumptions—can drive treatments and facilitate the dawn of an era of “data-driven medicine.” He then suggested that treated subjects will present a unique reaction, which will inform a "living database" and supply the data that will help other patients someday.
  1. How can big data guide lifestyle decisions? Snyder answered this question in part by giving a practical example inspired by Parkinson’s disease link to pesticides, and how it would be logical for those whose DNA indicates a risk for the disease to refrain from working around pesticides. 
  1. What are the opportunities for industry in big data medicine? The author succinctly answered this question by citing how Amazon, Microsoft, Google, and others are all ramping up their efforts to capitalize on the opportunity to handle big data and derive information from it. Snyder wrote that these initiatives will count on scientists who can make sense of health data, as well as professionals who can create original health IT applications.

Purchase the book here.


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