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July 30, 2012
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Healthcare Goes Big

Earlier, in the “Big Data in Genomics and Cancer Treatment” blog post, I explored how the extensive amount of information in DNA analysis mostly comes from the vast array of characteristics associated with people’s DNA make up and with different cancer variations. The case with today’s healthcare is very similar. Each patient is unique and has thorough medical history records that allow doctors to make evaluations and recommendations for future treatments. These records also contain various drugs, therapies, diets, and regimens that must coincide with the patient’s condition and which, if not followed correctly, could endanger the patient’s life.

“Doctor, can I have some of that Big Data?”

Currently, the medical field is overflowing with big data and there is huge potential for improvement in treatment quality and overall patient experience. With the use of big data analytics, health care and pharmaceutical companies could significantly advance the services that they offer their patients.

Through big data analytics, there could be much more control over hospital operations. According to the Top Ten Innovations for 2012 article for the 2012 Medical Innovation Summit, data could “track outcomes for clinical and surgical procedures, including length of stay, readmission rates, infection rates, mortality, and comorbidity prevention”. The article also stated that, “Healthcare big data requires advanced technologies to efficiently process it with tolerable elapsed time, so organizations can create, collect, search, and share data, while still ensuring privacy.” This brings up a very important point: how can healthcare organizations take advantage of the benefits that many big data technologies provide while also ensuring privacy?

Healthcare companies must be able to balance the privacy of their individual patients with the overall health of the population. To meet that need, companies can still analyze patient records through the HIPAA-compliant privacy framework – a security framework that eliminates patient identification and still allows data analysis. This framework complies with federal law and, most importantly, brings about exciting improvements at various hospitals in their goals to improve today’s healthcare. Aside from that, there is also the Nationwide Health Information Network (NHIN) Exchange – a way for healthcare professionals to securely exchange information while following specific standards, services, and policies. The NHIN Exchange is helping to achieve the Health Information Technology for Economic and Clinical Health act (HITECH) of 2009.

Big Data Role Models in Healthcare

With the help of big data companies, hospitals and pharmacies have the ability to understand a wide range of patient data, which decreases the chances of missing any warning signs or medical miscalculations. So far, for example, New York Presbyterian Hospital has decreased potentially fatal blood clot cases by approximately 25% and the Seton Healthcare Family hospital has been able to predict (and prepare for) probable congestive heart failure cases. A clinical analytics company called Humedica is focusing specifically on congestive heart failure and has developed a predictive analytic model that also allows doctors to be aware of high-risk CHF patients before they are admitted into a hospital. The Sax Institute has also launched a project called The Secure Unified Research Environment (SURE), which allows health researchers to access patients’ medical information (identities are protected) through a data center. While still in its infancy, this project will compile a lot of research about the consistency of care in respect to the age, wealth, and overall living condition of various patients, so that doctors will be able to analyze all the factors contributing to a patient’s medical case.

Another very impressive project is IBM’s Watson (yes, the one that became popular after a game of “Jeopardy!”)– a computing system that can be used as a tool for doctors and researchers in the medical field. Watson is capable of analyzing the meaning and context of human language, allowing doctors to have an evidence-providing adviser on patient conditions in near real time. To give you a sense of its power: Watson is able to examine about one million books and analyze the information in them all in about three seconds. This kind of speed and precision can prove very helpful for doctors when they are faced with difficult medical cases, especially ones that require quick treatment. This big data system can positively change doctor-patient communication and help to facilitate efficient health care.

Explorys, a healthcare data company, has already developed a secure software platform with the help of Apache Hadoop and offers doctors the ability to aggregate, analyze, manage, and research all of the information they need to make the right decisions every day. Through its platform, Explorys has compiled an extensive healthcare database, which is already being used by 11 other major healthcare companies.

Apixio, another medical search company, uses Hadoop to analyze structured and unstructured data to provide meaningful results when healthcare professionals search specific issues in Apixio’s Medical Information Navigation Engine (MINE). Any kind of data can be put through MINE (forms, CT scans, emails) and doctors can then extract the information they need based on specific symptoms. Vishnu Vyas, a natural language scientist at Apixio, explained MINE as “Google for doctors, only better, because it’s patient-centric and determines how data relate to one another.”

Big Potential

Bill Schmarzo, chief technology officer at EMC, shared a helpful list of Big Data Business Opportunities in health care. Here are a few:

  • Ability to access any data source, no matter where it is located, using new federated query, data discovery and semantic management technologies.  This allows health care providers to gain a more timely, more complete understanding of the patient’s current situation so that they can prescribe the appropriate and most effective treatments.
  • New instrumentation opportunities to increase the amount and real-time nature of data being captured about patients’ health care (blood monitoring, smart toothbrushes, etc.)
  • In-memory capabilities to facilitate real-time, life-saving decisions at the point of care, especially in high stress, immediate need areas like the emergency room.
  • Real-time monitoring of key patient health care metrics that leverages in-memory computing to more rapidly evaluate incoming patient data streams (from the multitude of new health metrics capturing sensors), flag areas of concern, and score potential health-related issues.

By harnessing big data, healthcare industries can see significant benefits and accelerate development, particularly by using the power of Apache Hadoop. Healthcare in the United States is costly and, as an open source platform, Hadoop makes big data analytics affordable. Professionals could revolutionize their medical businesses and provide the best care possible to their patients. Most importantly, if healthcare companies learned to manage big data efficiently, there could be a wider availability of data and, consequently, a much more global knowledge of patient treatments, therapies, and drugs. For the healthcare world, in this case, Apache Hadoop may be just what the doctor ordered.


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