Transformation Of Healthcare Sector By Big Data

Relational Database Management Systems are probably the ones that we are most familiar with in 21st century computer science. Relational databases store

For example, let us consider an organization has branches in various locations and the organization has decided to store databases on one node or a central computer and to distribute them to local computers. The central node is the distributed database and it is a single logical database which is spread across different local computers and they are all connected over a network.

Relational data is when you can put data in a computer one time and it grows

Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.

In order to properly utilize big data, health care organizations are upgrading their IT and software systems. Health Information Systems (HIS) are becoming more advanced with value-added services and functionality. In fact, some health care organizations have massive amounts of unstructured data, but no viable means of converting the data into meaningful information. Fortunately, there are software programs and third-party companies who can successfully extract the data for migration or conversion purposes.

It could consist of patient-related data, data from healthcare devices like monitors and sensors, hospital records, application data measuring health metrics and everything including social media posts, webpages, emergency correspondence, research data from genomics to innovative drugs, advertisement data, newsfeeds and articles in medical journals. As much as there is scope for finding out patterns among these data, it is not easy to implement predictive analytics in healthcare industry because of the limitations like hand-written prescriptions, scanned images and medical records which comprise of unstructured and disintegrated data. Moreover, medical data is involved with legal and privacy issues. The adoption rate of analytics in healthcare industry is quite slow making it more challenging.

Artificial intelligence is changing many ways we do things within the healthcare industry. With it, comes improvement of the quality and availability of healthcare services[1]. Many companies and investors have sunk hundreds of millions of dollars into artificial intelligence[2]. With this comes mass amounts of information and data being collected daily. The amount of money and resources being invested into AI is tremendous. There is no surprise that AI is going to revolutionize the medical industry. Changing the way that people look at healthcare for the better.

The history of Health Information Management (HIM) and Health Informatics (HI) has evolved from using descriptive anatomical terminology within documents…to an electronic system where medical personnel can store and retrieve patient health information (Green & Bowie, 2016). Distinctively defined as different entities, by the American Health Information Management Association (AHIMA), both the HIM and HI bring to the forefront patient data that can be expended by diverse agencies and departments (American Health Information Management Association, 2016).

The American Bar Association (ABA) states that Big Data is the next revolution for health care operations. Health care costs are rising among both insurers, organizations, consumers and the government. Everyone wants to increase speed and efficiency without sacrificing quality of care. Health care organizations are now using Big Data, analytical tools and business intelligence to identify opportunities to reduce costs, boost efficiency and increase quality.

The healthcare sector has seen some remarkable transformation from the once chaotic, expensive and substandard services and this was achieved by analytics of big data, it has change the following areas in the health sector

Replication: An approach to enhance consistency in large Database clusters. Ayyapa Reddy Pasam, ayyappayouth@gmail.com Abstract Database systems implemented in parallel computers is called Database clustering.

Metadata present a more complete picture of the data in the database than the data itself.

In a centralized database, all data of an organization are kept in a single computer as a central processor or server. Users in remote locations access data by using WAN by application software provided to access data. The centralized database (the central processor or server) should be able to satisfy all requests from the system; therefore, it creates restricted access. But since all data resides in a single location it easier to maintain and support data. In addition, it is easier to maintain the integrity of data, because once the data is stored in a centralized database, out-of-date data is no longer available in other places.

In the world of computer science large groups of data collected are referred to as databases. When there is a multitude of these databases, a way to manage all of the information is

Traditional databases employ techniques, which manage secondary storage in order to improve the performance of the system. These are usually invisible to the user of the system.