Data is present everywhere and can be found through many different sources.
After the concept of Big Data came into existence, all that was required to generate it was a click or tap on a screen, and voila, terabytes worth of data could be generated just by having one-time access to the internet.
Information is available in abundance today. From grocery stores to smartphones, data is generated at a large scale and in many different formats.
This led to the generation of a new buzzword called Big Data which refers to vast pools of information that might be structured or unstructured, raw or refined, in any form whatsoever.
Big Data in healthcare involves analyzing data at a mega-scale to establish patterns, derive insights, and predict outcomes. Big Data has evolved since Doug Laney first coined it in the last decade.
He defined three-dimensional characteristics of Big Data in his paper called ‘Integrated Information Theory. According to him, these characteristics are Volume, Variety, and Velocity.
Big Data present in healthcare covers a wide range of sources, including but not limited to Electronic Health Records (EHRs), Electronic Medical Records (EMRs), data from genomics, health care provider information, clinical trials, and research.
Data can be generated anywhere by anyone at any time. Doctors do a lot of data collection in the medical field by prescribing tests and procedures to patients that involve medical devices, blood tests, medications, etc.
These medical records are then transferred to different healthcare providers like clinics, hospitals, etc. And finally, they end up at an EHR, an electronic system for storing and using health information.
The analysis of these enormous pools of information is known as Big Data analytics, which needs to be performed with high accuracy and speed.
There are many types of data generated every day by healthcare providers, organizations, new technologies, etc.
These different sources produce different types of data that must be adequately managed. Big Data usually contains multiple sources that can be structured, unstructured or semi-structured.
Not all data is created equal. Some types of data need to be analyzed immediately to make quick medical decisions, for instance, data from diagnostic equipment that needs to be dealt with immediately.
This variety of data requires a different format for its extraction and storage, which has to be correctly handled by healthcare organizations if they want to carry out the analysis successfully.
On the other hand, certain information requires time before being analyzed and used for deeper insights.
Today’s Big Data Analytics techniques have evolved over the years by incorporating algorithms that perform real-time analysis and help make quick decisions.
‘Real-time’ refers to the elapsed time between data acquisition and information usage that takes no longer than a few seconds. This process is called ‘Streaming Analytics.’
Shortly, healthcare providers will make medical decisions using these patient profiles and providing personalized care.