AI in Healthcare has many sunsets and characteristics but one of the major ones is data mining. Data mining is a broad field in itself. There are several data mining techniques that can be applied and used in different scenarios in a healthcare organization.
Data mining is a broad concept and it can be used with various systems and as various systems in itself. When it comes to real-world approach, data mining can be applied as three different systems. This approach has been identified as the best way to take data mining from just theoretical discussions to actual real-world applications. It’s best to implement all three systems but some healthcare organizations that have implemented just one or two data mining systems and have been able to get some results and make progress in the field of AI in healthcare and data mining. We will these three systems below
The Analytics System
The analytics system has to do with the technology and technical expertise required in order to gather, interpret, and make sense of the data. In the analytics system, the data is put through standardized measurements and then it is used to make aggregations. The data aggregations include clinical data aggregation, financial data aggregation, and patient satisfaction aggregation. For the analytics system, the foundation is an enterprise data warehouse. An enterprise data warehouses are the core component of business intelligence and it is used for general system analysis and for data reporting.
The Best Practice System
The best practice system can also be referred to as the evidence-based best practices and it involves standardizing the everyday knowledge work. Basically, it is the application of evidence-based best practices to care delivery. When it comes to best practices and research findings that could support best practices, it takes years for them to be identified as such and then incorporated into a clinical system. Care delivery is a volatile field and so great care must be taken when it comes to putting research findings into practice. However, a string best practice system enables a healthcare organization to put research findings on AI in healthcare into actual use in ample time.
The Adoption System
The adoption system has to do with change management and driving change within a healthcare organization through the building and establishment of organizational structures. Besides establishing an organizational structure, structures are also established within the organization and they are commonly referred to as team structures. These team structures are what help with an organization-wide adoption of the best practice system. This is why it is advised that all three data mining systems be implemented together- It requires real organizational change to drive adoption of best practices throughout an organization.
AI in Healthcare Data Mining Concepts and Techniques
Implementing data mining is just one aspect of it. As I mentioned earlier, data mining is quite broad and in order to implement it properly, it is good to understand all its concepts and techniques. WHne you learn all the concepts and techniques then you can better leverage data mining for your healthcare organization.
Clustering is the process of creating groups (could be patients, customers, users, etc) that contain patients with commonalities and similarities. These commonalities could be having the same location, gender, illness, being in the same age group, etc. Clustering is useful because it makes caregiving more effective and quicker an can be useful for other things too. For examples e-commerce, companies can use clustering to classify customers and their buying behavior and use the clusters they have created to predict products these customers might be interested in and suggest it to them. It can also be used in the traveling industry to identify customers who have certain traveling interests and also in the real estate industry, the entertainment industry etc
Classification has to do with using algorithms to analyze and measure data. These algorithms have already been classified and automated. It speeds up the analysis of a data set and helps to predict possible outcomes.
Association should not be mistaken for clustering. Association has to do with the identification of commonalities within a data set. When there is big data available for AI in healthcare, the data can be analyzed for patterns and through association, these patterns can be used to uncover insights into patient needs, patient behavior, and patterns within the healthcare organization.
Decision trees aid in decision making within a healthcare organization. AI in healthcare is very broad and so with decision trees, decisions can be made at faster rates and with positive outcomes. With decision trees, you can see the cost and benefit analysis of each potential decision and you can also factor in historical data to this cost-benefit analysis. This greatly improves risk management in an organization.