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August 20, 2017

Healthcare Tech Terms: Big Data in Healthcare

Big Data in Healthcare


What does Big Data mean for Healthcare?

Big Data technology is fast becoming a catalyst for digital transformation across industries, and Healthcare is no exception. In fact, the healthcare industry, perhaps more than any other, is on the brink of a major transformation through the use of advanced analytics and big data technologies.

 Put simply, Big Data typifies massive data sets that can be analysed computationally to reveal patterns, trends, and correlations. In Healthcare, medical records and continuous monitoring & research produce quantifiable data in droves, making it a prime industry for this application.

Examples of Big Data in Healthcare

 

Examples: 5 Uses for Big Data in Health

The ability to intepret and analyse data and inputs en masse has the potential to transform a platitude of aspects of the Healthcare Industry. Here are 5 key applications for Big Data in Healthcare for 2017:

1. Reducing Fraud, Waste, and Exploitation

Big data analytics could be a game changer for healthcare fraud - a key area to blame at least in part for spiralling healthcare costs in the USA. For example, the use of predictive analytics has been credited with the saving of $200+ million in healthcare fraud in the US over the course of just one year. In this way, Big Data can be used to systematically identify and report unusual or inaccurate claims at a rate that would otherwise prove impossible.

2. The Internet of Medical Things (IoMT)

The Internet of Medical Things, or Healthcare IoT, refers to the connected infrastructure of medical applications and devices, that can communicate with a variety of new healthcare IT systems.  These smart devices are made 'smart' only through the successful transmission and interpretation of data. 

The Internet of Medical Things White Paper

 3. The rise of Value-Based Care

Numerous healthcare providers, including Medicaid, are seeking to transform their targets to focus on value-based data-driven incentives that reward top quality, cost-effective patient care. Part of this requires the demonstration that electronic health records are being used meaningfully, requiring successful interpretation of large amounts of data. Big Data brings together information from multiple sources, facilitating this care.

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4. Predictive Analytics for Improving Healthcare Outcomes

Predictive Analytics is not just valuable at the internal level, but also in understanding the nature and progression of diseases, allowing for earlier diagnoses. The ability to both combine and analyze a mass of structured and unstructured data across multiple organizational sources also aid when matching treatments with outcomes, and predicting at-risk patients for disease or readmission.

5. Patient Monitoring in Real Time

The rise of wearable devices and remote data storage also allows doctors to monitor patient conditions remotely - rather than continuous check-ups, this constant remote monitoring lowers hospital costs (check ups only occur when absolutely necessary), immediate remote consultations, and even remote prescriptions.

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These examples seem almost overwhelmingly positive, and reasonable. However, Big Data has a way to come before it is entirely ready for these transformation across the board - it's important to remember that the majority of the wealth of data provided and recorded in healthcare is unstructured. For this reason the potential of Big Data is, for now, limited by levels of Interoperability, and the reliability of data-sharing.


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