Which process involves examining raw data to draw conclusions about information?

Data analytics is the process that involves examining raw data to draw meaningful conclusions and insights. This practice encompasses various techniques and methods for analyzing data, including statistical analysis, predictive modeling, and data visualization. The goal of data analytics is to transform raw data into actionable intelligence that can influence decision-making in healthcare and other fields.

In healthcare, data analytics can help identify trends, optimize operations, improve patient outcomes, and support evidence-based practices, making it an essential aspect of healthcare information systems management. By applying analytical techniques, organizations can gain a deeper understanding of patient behaviors, treatment effectiveness, and operational efficiencies.

Understanding the differences among the other concepts helps clarify why data analytics is the best fit for this question. Data mining, while related, focuses more on discovering patterns or relationships in large datasets rather than the overall conclusions drawn from data in a broader sense. Clinical information systems are designed to manage and store patient information, providing tools for documentation and real-time access to data rather than drawing conclusions from that data. Descriptive statistics summarizes raw data without making inferences or predictions, which distinguishes it from the broader scope of data analytics that includes interpretation and conclusion-drawing processes.