The extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing and accessing of data, data analysis and effective use of stored knowledge of data. The goal is to present how methods and tools for intelligent data analysis are helpful in narrowing the increasing gap between data gathering and data comprehension. This goal is achieved by applying Association Rules Technique to help in analyzing and retrieving hidden patterns for a large volume of data collected in a medical database for a large hospital. The approach used led to results normally unattainable using conventional techniques. Specifically, an episode database for Nephrology examinations, signs, symptoms and diagnosis is used. Theoretical and practical features for the Incremental Enhanced Association Rule Algorithm are presented. These features include Rules, Classification, Clarity, Automation, Accuracy, Relational Database Management Systems (RDBMS) and Raw Data.