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The knowledge management system is used to generate, store, distribute, use, and reuse the valuable information, knowledge, and insights by means of information technologies.

The knowledge system in healthcare is to create, store, share, and reuse valuable information. Adoption of knowledge management systems in the healthcare industry is very challenging. Gathering of information from various sources and processing of that information needs the latest tools and technologies. Big data knowledge system refers to finding the knowledge from raw data. Big data analytics is the way to transform the raw big healthcare data into knowledge. Data, business intelligence, and machine learning are the most important in big data knowledge systems. The big raw healthcare data is transformed to insights using business intelligence, and then, insights are transformed to knowledge with machine learning and artificial intelligence. Figure 3.4 depicts the information flow and knowledge discovery process of healthcare big data.

Healthcare is based on knowledge such as medical knowledge, clinical knowledge, health service knowledge, and disease knowledge. So, healthcare services are knowledge-based services. All this knowledge enables the doctors to make better decisions in the treatment process with care. This knowledge is continuously evolving, based on new diseases and new drugs. The healthcare professional must learn constantly to provide up to date patient care.

The healthcare industry uses the “healthcare knowledge management systems”, for better services to the patients with improved efficacy.

Advantages of knowledge management system in healthcare are as follows [16]:

 • Improved patient services

 • Medical error reduction

 • Reduction of healthcare cost

Figure 3.4 Knowledge discovery process of big data in healthcare.

The big data knowledge system transforms the raw data set into data insights, which lead to a better treatment at a lower cost.

Knowledge discovery is the method for transforming raw big data into useful information. The important elements of knowledge discovery are data, analytical tools, methods, and understanding of the domain [17]. Big data analytics is used to discover meaningful insights from the big data set. Machine learning, cloud computing, data science, natural language processing, text analytics, predictive analytics, statistical investigation, data mining, and artificial intelligence are advanced big data analytical techniques. This technique gains new knowledge from the data. All of the data is not readily usable in big data analytics. They have to undergo a “data cleansing process” to make it understandable. Understanding the details about, where the raw data come from and how they have to be treated before analyzing them, are also important. So, the data have to go through a process called “Extract, Transform, and Load” (ETL) before it can be analyzed. The data are harvested, that is, “Extracted”. Then, the data is formatted to make it readable, by an application that is “Transformed”. Then, the data is stored in the memory for reuse, that is, “Loaded”. This is called the ETL process.