What process is used to predict outcomes based on observed data and historical information?

The process of predicting outcomes based on observed data and historical information is known as predictive modeling. This technique leverages statistical algorithms and machine learning methods to analyze patterns in existing data, allowing organizations to forecast future events, behaviors, or trends. By utilizing historical data, predictive modeling can identify relationships and patterns that can inform decision-making processes in various fields, including healthcare, business, and finance.

Predictive modeling is particularly valuable in healthcare for anticipating patient outcomes, readmissions, and the likelihood of disease progression, enabling proactive management and improved patient care strategies. This approach goes beyond simple data description; it not only summarizes past data but also creates models that can support future predictions.

In contrast, other terms such as data mining primarily focus on discovering patterns in large datasets without the specific intent of prediction. Clinical information systems are designed to manage patient data and support clinical operations but do not inherently involve predictive analytics. Descriptive statistics, while essential for summarizing and understanding historical data, do not lend themselves to making forecasts about future events.