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The integration of artificial intelligence (AI) into healthcare, particularly through medical documentation assistants powered by large language models (LLMs), presents significant opportunities for enhancing efficiency and accuracy in clinical record-keeping. However, the deployment of such systems introduces unique risks, including prompt-induced biases, hallucinated content, and non-compliance with regulatory standards, which can compromise patient safety and data integrity. This conceptual manuscript proposes a novel design-control framework for risk mitigation (DCFRM) tailored to prompt safety specifications in medical documentation assistants. The framework establishes a multi-layered architecture that incorporates proactive prompt engineering, real-time monitoring mechanisms, and adaptive governance protocols to mitigate risks without relying on empirical data or model training. Drawing from theoretical principles in AI safety and healthcare informatics, the DCFRM emphasizes interpretive formulas for risk propagation and decision confidence, ensuring alignment with ethical and legal imperatives. By synthesizing recent literature on AI-driven clinical tools, this work highlights the need for infrastructural safeguards that address deployment-specific vulnerabilities in dynamic clinical environments. The framework’s unique feedback topology enables iterative refinement of prompt specifications, fostering resilience against emergent threats like model drift or adversarial inputs. Ultimately, this theoretical construct aims to guide the development of safer AI assistants in healthcare, promoting trust and reliability in medical documentation processes while adhering to design-control paradigms that prioritize risk aversion over performance optimization.