Data Mining to Identify Trends in Community Health

Difference between Data Mining and Data Analytics

Data mining and data analytics are often used interchangeably in the health and nursing fields, but they actually represent distinct processes with unique goals and methodologies. It is helpful to know the differences between the two can because understanding how they work and what they are used for can improve the implementation of data-driven decision-making in healthcare.

Data mining is the process of discovering patterns and relationships within large datasets (Gupta & Chandra, 2020). Involved in the process of data mining is the use of algorithms and statistical models that can be used to identify some of the hidden patterns, trends, and correlations within the data that might not be immediately apparent to the user. In health and nursing, data mining can identify risk factors for diseases, identify trends in community health, predict patient outcomes, or uncover patterns in patient care that could facilitate development of improved treatment methods. Techniques of data mining can be things like clustering, classification, or association rule learning—and all of these are commonly used in data mining to analyze datasets so as to extract meaningful information (Gupta & Chandra, 2020).