Imagine trying to find a needle in a haystack, but the haystack is made of millions of medical records, and the needle is a specific piece of information critical for patient care. That’s the challenge facing healthcare professionals daily. Large Language Models (LLMs), powerful AI systems that can understand and generate human-like text, offer a potential solution. However, these LLMs need precise instructions—called prompts—to perform effectively. A new research project, StructEase, is revolutionizing how we interact with these LLMs to unlock the wealth of information hidden within clinical notes. Instead of relying solely on complex algorithms or time-consuming manual prompt tweaking by experts, StructEase puts clinicians in the driver's seat. It allows medical experts to guide the LLM’s learning process through a clever, iterative feedback loop. This approach dramatically improves the AI’s accuracy in classifying clinical data, such as identifying whether a patient wore a helmet during an accident. StructEase introduces a novel sampling algorithm called SamplEase, which cleverly selects the most informative cases for expert review, maximizing the impact of their feedback while minimizing their workload. This targeted approach significantly outperforms traditional methods, leading to higher accuracy and more efficient use of expert time. StructEase is more than just an improvement in AI—it's a bridge between cutting-edge technology and real-world clinical practice. It empowers medical professionals to harness the power of AI without needing extensive technical expertise, paving the way for more accurate diagnoses, better treatment plans, and ultimately, improved patient outcomes. While initial results are highly promising, future research will explore integrating even more advanced refinement techniques and ensuring strict privacy and security protocols for real-world deployment with sensitive patient data.