This paper describes a time-changing feature selection1 framework based on hierachical distribution method for extracting knowledge from health records. In the framework, we propose three steps for time-changing feature selection. The first step is a qualitative-based search, to find qualitative features (or, structural time-changing features). The second step performs a quantitative-based search, to find quantitative features (or, value time-changing features). In the third step, the results from the first two steps are combined to form hybrid search models to select a subset of global time-changing features according to a certain criterion of medical experts. The present application of the time-changing feature selection method involves time-changing episode history, an integral part of medical health records and it also provides some challenges in time-changing data mining techniques. The application task was to examine time related features of medical treatment services for diabetics. This was approached by clustering patients into groups receiving similar patterns of care and visualising the features devised to highlight interesting patterns of care.