Objectives: This study aimed to examine the clinical decision-making processes in Traditional East Asian Medicine (TEAM) by reinterpreting pattern identification (PI) as a form of dimensionality reduction. Specifically, we focused on the Eight Principle Pattern Identification (EPPI) system and investigated why the Exterior–Interior (Ext–Int) pattern is prioritized in diagnosis and treatment selection. Methods: We analyzed empirical data from the Shang-Han-Lun and tested three hypotheses: (1) whether the Ext Int dimension contains the most information about patient symptoms, (2) whether it represents the most abstract and generalizable symptom information, and (3) whether it facilitates the selection of appropriate herbal prescriptions. Quantitative measures including abstraction index, cross-conditional generalization performance (CCGP), and decision tree regression were employed. Results: The Ext–Int dimension showed the highest abstraction index and generalization capacity, particularly in the herbal prescription space. Decision tree regression further confirmed its role as a primary node in mapping symptoms to prescriptions. These findings demonstrate that the Ext–Int dimension provides abstract and generalizable representations that enhance diagnostic efficiency and treatment selection. Conclusion: This study provides an objective and quantitative framework for understanding the cognitive processes underlying TEAM. By formalizing PI as a sequential dimensionality reduction process, our findings highlight the central role of the Ext–Int pattern in bridging traditional medical reasoning with modern computational approaches. These insights have implications for the development of AI-driven diagnostic tools and the advancement of clinical practice, education, and research in both traditional and conventional medicine.