Healthcare prediction using electronic health records (EHRs) is a cornerstone of modern clinical decision support. Conventional machine learning (ML) models achieve strong predictive performance but rarely provide knowledge-grounded, human-readable explanations. Large language models (LLMs) incorporated with medical knowledge graphs (KGs) offer advanced reasoning and explainability with encoded clinical facts, yet existing approaches typically perform monolithic reasoning over entire patient records, mixing task-irrelevant context and obscuring disease-specific attribution. To bridge these gaps, we propose CRU-Reason, a novel framework that integrates ML models, medical KGs, and LLMs through Clinical Rationale Units (CRUs) for knowledge-grounded and interpretable healthcare prediction. CRU is a localized structure that explicitly captures clinically meaningful communities among EHR concepts, thereby enabling LLMs to perform disease-centered, fine-grained reasoning. Experiments on two public clinical datasets demonstrate that CRU-Reason improves over the strongest evaluated EHR-prediction baselines by an average of +9.4% across drug recommendation and diagnosis prediction, while exhibiting improved rationale quality and cross-dataset robustness.

Knowledge editing (KE) has recently emerged as a promising technique to update specific facts in large language models (LLMs) without full retraining. While existing KE methods show promising results on general-domain benchmarks, their effectiveness in the medical domain remains largely unexplored. Medical knowledge editing poses unique challenges, requiring models not only to memorize new facts but also to internalize and generalize them for reliable and interpretable clinical decision-making. In this work, we propose MedEditBench, a rigorous evaluation framework for assessing medical knowledge editing. Our preliminary results reveal that current KE paradigm, which directly edits simple answers to the LLMs, often leads to superficial updates with poor generalization. To address this, we introduce Self-Generated Rationale Editing (SGR-Edit), which leverages model-generated rationales as editing targets, enabling deeper knowledge integration. Extensive experiments across diverse LLMs and KE methods demonstrate that SGR-Edit consistently improves editing efficacy and generalization. Furthermore, we examine the impact of sequential edits on in-domain medical knowledge, external-domain knowledge, as well as general model capabilities, offering practical insights for deploying KE in real-world medical applications.