The trajectory of data storage innovation drives the future of healthcare

The trajectory of data storage innovation drives the future of healthcare

January 13, 2020
Health IT
Chip Elmblad
By Chip Elmblad

Innovation in the modern health sciences is inextricably dependent on innovation in information technology. Advances in emerging disciplines such as precision medicine, population health, pharmaceutical research, and bioinformatics hold the promise of a future where diagnoses can be more accurately descriptive, treatment can be more precisely prescriptive, and outcomes can be more highly predictive.

These innovations in health sciences rely on developments in one or more information technologies: quantum computing, imaging capabilities, analytical engines, robotic technologies, and nanotechnologies, among others. In virtually all cases, they are dependent on what these technologies need as input or produce as output – data.


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If you’ve been around the IT landscape for a few decades, you’re aware that innovation in data storage has a trajectory. That trajectory can be summarized by the words “denser,” “faster,” and “more reliable.”

Denser means we’ve gone from Megabyte-scale to Gigabyte to Terabyte and now Petabyte-scale as inputs and outputs to drive innovation. Faster means we’ve gone from hundreds to hundreds-of-thousands of IOPs (inputs/outputs per second) and from tens of seconds to tens of microseconds in latency. More reliable means we’ve gone from hours of acceptable data unavailability per year to seconds. The timeline for this trajectory thus far has been a period of over sixty years. Every part of our lives has been improved by this innovation: Data is knowledge, and we are limited only by how much data can be collected and how quickly we can correlate it, analyze it and implement its implications. Nowhere is that more apparent than in the health sciences.

For each of the advanced health technologies mentioned above, the path to accelerated discovery and implementation runs squarely through data storage innovation. Let’s look at one example – precision medicine.

As the leader of the Human Genome Project, scientist Francis Collins had a limited tool set at his disposal to map the human genome. Over a period of ten years and at a cost of $50 million, he and his colleagues were able to identify the genetic cause of cystic fibrosis. From that point, it took only five years for the Human Genome Project to map the complete human genome – billions of pairs of genes. By 2025, it’s predicted that between 100 million and 2 billion human genomes will have been sequenced, resulting in many exabytes of base data.

Without increasing the pace of innovation in data technologies, simply storing all this data will be a much larger challenge than analyzing it and advancing precision medicine as a result. In other words, humanity would miss its opportunity to advance the state of medical science in this area. Therefore, we must consider the following dimensions of innovation that have tremendous potential to meet these historic opportunities:

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