information of patients.
Tactic knowledge: The tactic knowledge is gained from the course of experience which is more difficult to express. It lies sub-consciously within the human and is improved by continuous practice.
In the healthcare industry, both the tactic knowledge and the explicit knowledge are used together for the diagnosis of many diseases.
In healthcare industry, knowledge is classified into three types:
Provider knowledge: The provider knowledge integrates both tactic and explicit knowledge. For example, in healthcare, a doctor should know the standard treatment for a particular disease. Doctors will gain internal knowledge through years of experience for better treatment.
Patient knowledge: Patient knowledge is tactic knowledge. “Patient’s knowledge” is the knowledge of a patient about their medical condition. This information is important for the provider to treat the disease.
Organizational knowledge: The organizational knowledge is the data available for both provider and patient access.
The knowledge in healthcare is essential because healthcare industries are knowledge driven industries.
3.4.3 Big Data Knowledge Management Systems in Healthcare
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.
3.4.4 Big Data Analytics in Healthcare
Big data analytics is the process of discovering knowledge. The stages of healthcare big data analysis are descriptive, diagnostic, predictive, and prescriptive [7, 27]. These analytical stages give the answer for the following questions:
• What happened to the patient?—Descriptive Analytics
• Why did it happen and when?—Diagnostic Analytics
• What will happen in the future?—Predictive Analytics
• How can we make it happen or not?—Prescriptive Analytics
The following are the categories of the healthcare big data analytics [18–20].
Descriptive analytics: This analytics summarizes past data in an easily readable format. This is like reporting. This gives the information, like what happened and when. The descriptive analytics in healthcare data gives the answer for the following questions:
• How many patients were in the emergency ward in the last month?
• How many numbers of patients have been infected because of epidemic disease in the last three months?
The descriptive analytics give the details for the above question using data. The descriptive analytics gives answers in simple statistical measures such as count and percentages.
For example, how many people have been infected with COVID-19 in the last month? From descriptive analytics, the hospital management can discover the number of people infected with COVID-19 in the last month. For example, we can discover from the descriptive analytics that 10,000 people are infected with COVID-19 in the last month. Descriptive analytics looks at the last month’s data and gives the answer in number. The government and the healthcare organization are able to identify the problem with current clinical aspects and get better healthcare practice, using the report of descriptive analysis. This is the first step of analyzing the big data that transforms the raw data into precious knowledge. Descriptive analytics is mainly for reporting, monitoring, and visualization.
Diagnostic analytics: This type of analytics gives information like what happened and why. The diagnostic analytics give explanation for the descriptive analytics data. The diagnostic analytics give answers