Using Big Data Analytics in Healthcare Industry

Why Enterprises need Big Data Analytics in Healthcare?

The Amount of Data in Healthcare is increasing at an astonishing rate. However, in general, the industry has not deployed the level of data management and analysis necessary to make use of those data. As a result, healthcare executives face the risk of being overwhelmed by a flood of unusable data. Consider the many sources of data. Current medical technology makes it possible to scan a single organ in 1 second and complete a full-body scan in roughly 60 seconds. The result is nearly 10 GB of raw image data delivered to a hospital’s Picture Archive and Communications System (PACS).

Clinical areas in their digital infancy, such as pathology, proteomics, and genomics, which are the key to personalized medicine, can generate over 2TB of data per patient. Add to that the research and development of advanced medical compounds and devices, which generate terabytes over their long development, testing and approval processes. You can also explore more about Enterprise Data Strategy in this insight.

Doctors Are Drowning In Data

Technology isn't enough to improve healthcare. Doctors must be able to distinguish between valuable data and information overload. One of the hopes of Electronic Health Records (EHRs) is that they will revolutionize medicine by collecting information that can be used to improve how we provide care.

Getting useful data from EHRs can occur if real data is input. It doesn't always happen. To see patients, document encounters, enter smoking status, create coded problems lists, update medication lists, e-prescribe medications, order tests, find, open, and review multiple prior notes, schedule follow-up appointments, search for SNOWMED codes, search for ICD-9 codes, and find CPT codes to bill encounters(tasks previously delegated to a number of people) and compassionately interact with patients, providers have to take shortcuts. But We have to Say HealthCare Drowning in Data Elements not yet interoperable onto one Platform.

First, the Data Exchange and Interoperability between EMRs, HIEs, Hospitals, Nursing Homes, Homecare, ERs, portals, etc., must be addressed and industry standards need to emerge on the technology, but also the costs need to be defined.

Who is going to pay for what and when?

It seems like the deepest pockets in the industry – pharmaceuticals and insurance – have put a dime into technology solutions or Big Data. They have the most to gain. It is a huge disconnect because physicians and hospitals cannot afford to capitalize on this startup by ourselves. I believe that they will need to be influenced to contribute to this effort, in kind or with cash, for this system to be made whole and meaningful. HIT industry leaders need to sit down with busy clinicians to create a workflow of automated Big Data in a way that provides all the stakeholders with the data to improve all levels of efficiencies and outcomes. Decisions Through Data-Small data, Predictive modeling expansion, and real-time analytics are three forms of data analytics Healthcare data will continue to accumulate rapidly. If practices, hospitals, and healthcare systems do not actively respond to the flood of unstructured data, they risk forgoing the opportunity to use this data in managing their operations.

Small data and Real-Time Analytics are two methods of data analytics that allow practices, hospitals, and healthcare organizations to extract meaningful information. Predictive Modeling is best suited for organizations managing large patient populations. With all three methods, the applicable information mined from raw data supports improvements in the quality of care and cost efficiency. The use of Small Data, Real-Time Analytics, and Predictive Modeling will revolutionize the healthcare field by increasing those opportunities beyond reacting to emerging problems

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