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Taming the Wild West of precision medicine

Facing a promising but unknown frontier, organizations need to rethink how they process, access and store the vast amounts of data coming in.

aws May 16, 2018 04:45 pm

Pledging to deliver the right care to the right person at the right time, an increasing number of providers — 70 percent, according to HIMSS Analytics’ Essentials Brief: 2017 Precision Medicine Study — are implementing precision-medicine applications in their organization. Over the last five years, precision medicine has transitioned from vague hype to a rapidly expanding market, one analysts expect to exceed $87 billion worldwide by 2023.

One indicator of its growing value is the January 2018 publication of the first HIMSS Media Precision Medicine survey, which evaluated where organizations stood on the path toward adoption and innovation. Among its key findings, the survey noted that 67 percent of respondents embraced the promise of precision medicine, with 55 percent of providers naming it a top priority.

But, as Janet King, senior director of market insights at HIMSS, notes, there is a substantial gulf between promise and implementation. In order to get a stronger sense of “rubber-meets-the-road” deployment of precision-medicine tools, HIMSS “wanted to evaluate where organizations stand on the trail toward adoption and innovation,” King said.

And what did it find? Despite the strong embrace of providers, precision medicine implementation still trails behind several other clinical and strategic priorities such as value-based care and population health. And while most organizations placed precision medicine on their roadmap, a clear majority remained in education mode — evaluating strategic opportunities, holding tactical conversations and keeping up to date with the latest advances.

(Read how Eric Topol, one of 2018’s Healthcare 10xers, is advancing personalized medicine) 

“Value-based care, population health and precision medicine: It’s an interdependent ecosystem,” King said. “If you can unlock precision medicine, then you can deliver better value to more people. The reality is that organizations still face a lag in execution.”

On the frontier

What explains precision medicine’s implementation gap? Partly, it’s a matter of maturity.

“We’re in the Wild West of it,” said Jim Metcalf, chief data scientist at the Healthy Nevada Project. “We’re synthesizing data sources in new ways and are in the early days of understanding the significance of various genetic mutations. Precision medicine will look drastically different in 50 years.”

Launched in 2016, the Healthy Nevada Project is combining genetic, clinical, environmental and socioeconomic data to better understand the complex interplay between these factors and their related effects on population health. The project embodies another key finding from the survey: Organizations are convinced that cutting-edge technologies such as Big Data, analytics and AI are necessary enablers for precision-medicine initiatives.

(John Quackenbush describes the state of genomic medicine in his Pioneer Profile here)

“The use of genomic medicine — and its application to personalized medicine — will only continue to grow,” said Judy Hanover, healthcare product marketing manager for Pure Storage, an independent all-flash data-platform vendor. “As successes continue to mount for personalized medicine, demand from patients is growing and more research is being done, expanding personalized medicine options for more conditions and patients.”

Indeed, it’s access to huge data sets that gives organizations the ability to scale personalized medicine across populations. But the vast amount of data coming in offers a possible explanation for the gap between provider interest and execution: According to the survey, more than half of all respondents cited data integration as a key barrier to precision-medicine feasibility.

The rise of the data scientist

With this data tsunami come equally large questions about the role of the data repository. Integration is becoming more complicated, Metcalf argued, because of the sheer volume of data and disparate sources that need to be organized, stored and processed. No longer is the EHR the container, but now just another source of information.

“There’s a reason we’re seeing the rise of the data scientist today,” Metcalf said. “Data science recognizes the importance of organizing, normalizing and bringing together 25 different data sources for the analysis it requires. If organizations want to adopt precision-medicine initiatives, they need to establish pathways to understand and organize their data.”

Hanover agreed. She said that the speed and size requirements of healthcare data science will overwhelm existing storage solutions.

“Scalability is key given the expanding nature of data, as is uptime and availability,” she explained. “Because of the size of the data sets involved and the complexity of the models in use, clinical researchers in these areas need high-performance, high-bandwidth infrastructure to build and train models, to bring data sets online and to do this quickly and efficiently to help bring this technology from the bench to the bedside — where it can help patients — more efficiently.”

And that’s the key challenge: getting data into day-to-day clinical experiences. In the HIMSS Media survey, participants cited data integration/interoperability and integrating genomics data into clinical workflow as key barriers to implementing precision-medicine applications in their organization.

“Precision medicine is not an island by itself,” King said. “Organizations need to consider storage and analytics — different pieces of the puzzle so they can deliver on the promise of precision medicine.”

In the past, storage — to meet regulatory requirements — was valued more highly than fast access and processing. But that equation is flipping as Big Data-enabled techniques make precision medicine a reality. Both King and Metcalf cited the importance of pulling vast amounts of heterogenous data together to drive precision medicine forward.

At the end of the day, “storage is affordable, manipulation is expensive,” said Metcalf. “You need both to organize the data to get meaningful information.”

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