Impact of Big Data in the Healthcare Sector | Opallios – Big Data …
The Big Players in Big Data
There is a good deal of buzz around Big Data—hardly a week goes by without a new startup or business preaching the virtues of mining massive tracts of information. Understandably, many have trouble seeing beyond the hype, but nowhere are the benefits of Big Data more tangible than in the healthcare sector.
IBM predicts a 20% decrease in patient mortality as the medical field gears up to analyze streaming patient data with large-scale software applications. That’s not just a return on investment (though it is)—that’s using information to save human lives. Even now, major corporations like Microsoft, Dell, IBM, and Oracle are pioneering data-mining platforms that will help medical professionals stay on top of patient data and provide improved medical care.
The Big Data industry deals primarily in collections of data that are beyond the ability of common software to capture, process, and manage in a timely manner. The amount of data processed can range from a few dozen terabytes to many petabytes of information. A plethora of Big Data tools have been released recently to make meaning of it all—you could say they’re finding new ways to make data earn its space. Read on to learn more about Big Data’s colossal impact on the healthcare industry.
Mining Data to Save Lives
The biggest risk any patient takes when admitted to a hospital is being examined by another human. Missed warning signs, overlooked risk factors, and cursory assessments are often part and parcel of hectic admission wards. That’s why healthcare providers are beginning to turn to computers to help them make quick and accurate decisions about patient health. There are endless exciting opportunities for Big Data in the future of healthcare:
• Reducing readmissions. What if a pile of data could tell you how likely a patient was to be readmitted after treatment? Doctors would able to judge whether patients would benefit from short or longer stays, as well as track specific treatment factors leading to the reoccurrence of ailments. That saves time (and money) for both the doctor and the patient.
• Accessing data anywhere. Using secure data querying technologies capable of parsing enormous amounts of information, it’s possible for medical professionals to access remote data for a more timely and complete understanding of their patient’s ailment.
• Point-of-care decision-making. Imagine tools and equipment with built-in data processing capable of helping doctors make instant, life saving calls at point-of-care. The most obvious application for this is in fast-paced areas such as the hospital emergency room.
• Innovative smart devices. We’re already surrounded by intelligent devices capable of funneling large amounts of data at light speed. Why not put a chip in an in-home diabetes monitor that can send back frequent and useful data about a patient’s in-home treatment? Take it a step further, and make smart toothbrushes, smart toilets, and smart scales capable of reporting instantly on a patient’s condition.
• Genome sequencing. Though still a distant dream, whole genome sequencing may be the most intriguing item on this list. When the human genome was first decoded, it took over ten years to process the data (which is in petabytes)—now it takes merely a week. As Big Data technology expands to process even larger amounts of data, every-day genome sequencing may become available to the private sector.
Many of these possibilities are already in the process of being implemented, but some possibilities are a little farther off. Either way, it’s encouraging to consider that the amassment of huge quantities of data—a practice frowned upon by many privacy-loving citizens—can and will have a profound effect on lives saved and improved by medical technology.
Big Data Making a Difference
According to Business Week, New York-Presbyterian began implementing Microsoft technology in 2010 to scan patient records—and they have reduced instances of fatal blood clots by nearly a third. Also featured was Seton Healthcare Family, which used data-mining to discover that a bulging jugular vein is a predictor that patients admitted for congestive heart failure will be arriving back through the hospital’s spinning doors almost as soon as they’ve been sent out.
In a recent article, fastcompany.com highlighted a bad case of readmissions at the Washington Hospital Center, a healthcare facility near D.C. Emergency room doctors were starting to see an unsettling trend in the amount of patients returning to the ER with the same issues they had before. When it became clear that the center was going to be penalized for readmissions, they worked with a computer scientist at Microsoft Research to parse data from over 300,000 ER visits.
They found two major red flags—ER visits longer than 14 hours, and the word “fluid.” Readmission rates for American hospitals are as high as 20% within the first thirty days, and costs Medicare $17 billion a year. Washington Hospital center hopes to use the information it gleaned to get at the root of its readmission woes.
Hospitals across the country have begun utilizing Big Data software applications to make dramatic improvements in the diagnosis of patient health. In many cases, new or existing data is collected, parsed, and applied to analyze small but significant pieces of evidence that would be overlooked by a doctor simply reading a chart. In 2011 alone, Big Data generated over $30 billion dollars in revenue, according to research from IDC. That number is only expected to go up as the healthcare sector warms up to new technologies.
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Impact of Big Data in the Healthcare Sector | Opallios – Big Data …
