Big Data Improving Clinical Outcomes

16th August 2015 |Big data, Analytics, Technologies, Services, Clinical

From banking to retail, many sectors have already embraced big data, regardless of whether the information comes from private or public sources. Traditionally, the healthcare sector has lagged behind other industries in the use of big data. Part of the problem stems from resistance to change as providers are accustomed to making their own decisions, rather than relying on protocols based on big data. However, with regulatory and marketplace changes, players in the healthcare space are now looking at analytics and big data from a different perspective altogether.

One area where big data & related technologies have made rapid inroads is in the prediction of infectious diseases. The WHO reported about 198 million malaria cases in 2013. It also estimates that there are over 50-100 million cases of dengue infections every year. To facilitate timely predictions on the outbreak of dengue and malaria, IBM partnered with some researchers from the Johns Hopkins University. The result was an open-source modeling application – Researchers now have open access to use any kind of data and can quickly correlate it with disease data. Leveraging this, health-care agencies can now proactively identify outbreaks very early on before an outbreak, contain its spread, and allocate resources more efficiently.

Another area which can leverage big data is the healthcare industry which generates huge amounts of clinical, research, device and sentient data. Hospitals and healthcare providers can tap into this data to gain a deeper understanding of a patient’s current and future health, risk, and cost in order to deliver higher value care and improve efficiency.

An example to cite in this regard is the initiative undertaken by New York city-based Mount Sinai Medical Center. In-order to take advantage of a new model in health-care which pays hospitals to keep people healthy, Mount Sinai Medical Center teamed up with renowned data scientist, Jeff Hammerbach. In a unique pilot study, the team leveraged big data & related technologies to build a computer model which uses factors like diseases, past hospital visits, or even race, to predict a patient’s likelihood of returning to the hospital.

Each night, the Mount Sinai Data Warehouse collects clinical, operational and financial information generated by the hospital and its faculty practice associates. A supercomputer then sweeps through 1.5 petabytes of this data to pinpoint patient patterns, which physicians may have missed. Instead of only combing through reports directly relating to a patient’s condition, the system also digs deeper into family history, and genetics to paint a comprehensive picture of what’s happening biologically, helping physicians draw inferences.

The cumulative results provide a list of high-risk, chronically ill patients needing more follow-up and extra help. The hospital staff would then closely monitor & keep track of such patients and work towards helping them remain healthy and avoid hospitalization. The results—Readmission rates have fallen by over a half! So far, the hospital has saved over $20 million every year since it turned to big data analytics in 2003.

These examples show that big data is already impacting our everyday lives. They also reinforce the fact that leveraging big data in healthcare will be crucial in controlling spiraling health-care costs. Taking a cue from Mount Sinai Medical Center, other healthcare agencies and providers should also leverage data science and big data analytics in their overall health care regimen.

How else can Big data and related technologies impact or influence health care services? Please leave your comments in the section below.

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