Big Data Comes to Bear on Healthcare

Big Data Healthcare

The National Institutes of Health (NIH) is examining the use of big data for infectious disease surveillance, exploring the use of information taken from social media, electronic health records, and a range of other digital sources to provide detailed and well-timed intelligence around infectious disease threats and outbreaks that traditional surveillance methods aren’t able to. On the other side of the disease spectrum, big data analytics is also helping with the management of diabetes, a disease affecting over 422 million people globally and resulting in 1.5 million deaths per year according to the World Health Organization. Today, big data and big data analytics are delivering a range of innovative health care options as well as disease and illness monitoring and prevention tools that better the wellbeing of the world’s population.

Where is All This Data Coming From?

Thanks to the digitization of records, the spreading use of sensors, and the prolific use of mobile and standard computing devices, the data that is collected and recorded today is immense. But just because all of this data exists doesn’t mean it’s necessarily useful. Consider the ‘information’ gleaned from Twitter and Facebook posts, Snapchat and text messages, and Google and Siri question and answer sessions: certainly, some of that will be relevant to someone, but the sheer volume of non-qualitative data available can sometimes be a deterrent. Fortunately, the technology that’s evolving to collect all of this data is working hand in hand with big data management and analytics tech to ensure value.

Today, big data applications can predict future actions and with the widespread use of Internet of Things tech personalized data can be collected and monitored on an individual level. Applications such as Google Trends provide practical methods for using big data, while big data analytics helps navigate and utilize unstructured data that might otherwise seem irrelevant. And thanks to tools such as Hadoop, developers are able to construct predictive models that help organizations understand user responses and better tailor applications to these results.

Solutions for Healthcare

Already big data plays a role in biomedicine, advancing methodologies and skills and creating new cultures and modes of discovery. Some experts, in fact, believe the advances in medicine suggest we’ll be facing disruption in the industry as new systems and approaches prove their worth. Precision medicine initiatives already involve above a million volunteers in the US alone, along with several NIH-funded cohorts, and it’s likely that we’ll see the sharing of lifestyle information, genomic data, and biological samples linked to electronic health records as these schemes search for superior health care solutions. The benefits of these initiatives are collaborative and cooperative science, more efficient and better-funded research enterprises, and training advances, but all of this needs to be carefully balanced with the necessary privacy and security demands of big data.

Other advantages provided by big data analysis include a better understanding of rare diseases through the precision provided by aggregated integrated data, as well as predictive modeling able to advance diagnosis of illnesses and diseases both common and rare. Though many opportunities available through big data and big data analytics require a particular cultural shift, our high-tech environment already encourages this change.

Concerns and Further Investigations

Although experts see potential in the use of big data in the healthcare field, we’re also cautioned that unconventional data streams may lack necessary demographic identifiers or provide information that underrepresents particular groups. Further, social media can’t always be relied on as a stable data source. Nevertheless, big data research continues in many unique health care areas: multiple studies are investigating social media and online health forums for drug use and the existence of adverse reactions; one European surveillance system is collecting crowdsourced data on influenza; ResistanceOpen monitors antibiotic resistance at regional levels; and many others provide unique insight into our healthcare systems. The combination of traditional and digital disease surveillance methods is promising, and says Professor Shweta Bansal of Georgetown University, “There’s a magnitude of difference between what we need and what we have, so our hope is that big data will help us fill this gap.”

By Jennifer Klostermann

David Gevorkian

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