Large language models (LLMs) have acquired substantial medical knowledge and reasoning skills during pretraining, yet eliciting these capabilities typically requires scarce rationales or costly post-training. To address this gap, we introduce **RIPT**, a lightweight framework for data-efficient endogenous capability activation. It explicitly decomposes this objective into two complementary components: reasoning enhancement and medical knowledge elicitation. For the former, we extract steering vectors from hidden activations on a small set of high-quality paired reasoning and vanilla responses to shape LLMs’ reasoning behavior. For the latter, we obtain prefix vectors via prefix tuning on simple medical QA pairs to elicit domain-specific knowledge. At inference, RIPT freezes the backbone LLM and combines steering-vector injection with prefix-vector conditioning. Rather than injecting external knowledge, RIPT aims to unlock medical reasoning capabilities already acquired by LLMs during pretraining. Experiments under limited-resource settings show that RIPT consistently improves diverse backbone LLMs and benchmarks, suggesting a generalizable pathway for lightweight medical reasoning enhancement.