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).

Data analytics is a different in that as a field it involves many varieties of methods useful in looking at datasets in support of decision-making (Sarker, 2021). For example, it involves descriptive, predictive, and prescriptive analytics. Descriptive analytics can be used to summarize historical data to help one understand what has happened in the past. Predictive analytics uses statistical models and machine learning techniques to make forecasts based on historical data. Prescriptive analytics goes a step more by helping one make recommendations for desired outcomes based on the data. In nursing, data analytics can help with things like monitoring patient vitals, predicting hospital readmissions, or making the most of staffing levels to achieve the best quality patient care.