A comparison of healthcare institutions by ranking involves generating their relative scores based on the infrastructure, process and other quality dynamics. Being a top-ranking institute depends on the overall score secured against the hospital quality parameters that are being assessed for ranking. However, each of the parameters is not equally important when it comes to ranking. Hence, the objective of this research is to explore the parameters which are vital as they significantly influence the ranking score. In this paper, a hybrid model is presented for knowledge extraction, which employs techniques of rough set on intuitionistic fuzzy approximation space (RSIFAS) for classification, learning from examples module 2 (LEM2) algorithm for generating decision rules, and formal concept analysis (FCA) for attribute exploration. The model is discussed using AHA US News score data for cancer specialisation. The result signifies the connection between quality attributes and ranking. Finally, the leading attribute and its particular values are identified for different states of ranking. Copyright © 2020 Inderscience Enterprises Ltd.