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Big data analytics in Healthcare

Advancements in Big Data processing tools, data mining and data organization are causing market research firms to predict huge gains in the predictive analytics market for healthcare.

Moreover, those working with data in healthcare organizations are beginning to see how the advent of the technology is fuelling the future of patient care.

By leveraging Big Data and scientific advancements while maintaining the important doctor-patient bond, it is believed that a better health system can be created  that will go beyond curing disease after the fact to preventing disease before it strikes by focusing on health and wellness.

While there are still several roadblocks to using analytics effectively to drive care, here are three ways that Big Data use can realistically revolutionize the health field:

1. Precision Medicine and Research Get a Big Data Boost

Precision medicine promises to move away from a one-size-fits-all approach to medicine, to treating individuals by using therapies and treatment plans specific to them. It does so by tapping reams of data from tools such as mobile biometric sensors, smartphone apps and genomics.

Health data is allowing doctors to build better patient profiles and predictive models to more effectively anticipate, diagnose and treat disease.

Moreover, collaborations and partnerships between researchers and healthcare organizations are allowing them to build out pools of data that they can use to build better personalized healthcare models. These new capabilities are still in early days and it is expected that Big Data capabilities and policies will grow to allow patient data to continuously inform health research.

Experts foresee a loop of data generation with two potential outcomes. A closed loop process is already taking place today, in which information passes through a two-way channel between the patient and the company capturing the data. This system gives the patient information about their health while simultaneously affording the company data to analyse. In the future, experts aspire to an open loop system that allows the data generated to feed directly into medical research and fuel new discoveries. With the potential to replace many studies typically conducted in labs, big data will be positioned to revolutionize the process of medical research as we know it.

2. Tapping Big Data for Real-Time Infection Control

Sepsis — harmful bacteria and toxins in the tissues — is a major issue in the U.S. health system, killing one person every two minutes and accounting for nearly $24 billion in annual healthcare costs, as per the most recent study by the Sepsis Alliance.

While hospitals are looking to tackle the issue in a variety of ways, several health systems are piloting real-time analytics platforms that can sift out early warning signs of infections such as sepsis.

The data analytics pilots “determine which central lines are due for maintenance, or identify patients that are at risk for sepsis by using ‘sniffer’ algorithms to assign risk scores”.

Many organizations are already putting this in practice. For example, few health care organisation in USA has tapped Big Data to monitor sepsis in neonatal infants.This type of analytics helps to predict, and prevent infections and ultimately squeeze cost out of the system and create a safer care environment for patients.

3. Cutting Costs with Patient Data

Many healthcare organizations (47 percent) are already using predictive analytics and much of them (57 percent) believe that predictive analytics will save the organization 25 percent or more in annual costs over the next five years.

One of the many ways that predictive analytics help cut costs is by reducing the rate of hospital readmissions.

The idea of predictive analytics comes in looking for relationships that are consistent with readmission that would not have predicted or could not have understood before. Once identification of those relationships has been done, one can set up protocols on how to deal with this type of patient and manage things to prevent readmission.

Moreover, the technology can help to forecast operating room demands, optimize staffing, streamline patient care and make way for a better pharmaceutical supply chain.

The common theme here is that there’s a tremendous amount of digital data available in hospitals and in the broader healthcare community that has never been available before. The statistical, mathematical modelling techniques can be applied that produce incredible analysis efficiently with a high degree of confidence — and that can be to tackle the problems in more robust way.

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