Imagine a world where medical data is instantly accessible for research, yet completely private and secure. That's the promise of MedPromptExtract, a groundbreaking tool that combines the power of AI with clever prompt engineering to revolutionize how we handle medical records. Extracting key information from medical documents like discharge summaries is traditionally a laborious, manual process. This poses a significant hurdle for digitizing medical records, especially in resource-constrained settings. MedPromptExtract tackles this challenge head-on with a three-step automated pipeline. First, it anonymizes sensitive patient data using EIGEN, a cutting-edge AI model that learns to redact identifying information with remarkable accuracy. This ensures patient privacy while preserving valuable clinical details. Next, using NLP and regular expressions, the tool efficiently extracts structured data from standardized fields like 'Diagnosis' or 'Medications.' Think of it as a super-powered search-and-find, pulling out precise information in seconds. Finally, for the more nuanced information buried within free-flowing physician notes, MedPromptExtract employs the magic of prompt engineering with large language models (LLMs). By crafting specific questions, the tool guides the LLM to extract complex clinical insights, like whether a patient experienced a drop in oxygen saturation or required a ventilator. This is where the real innovation lies – unlocking the treasure trove of unstructured medical data. Testing on real discharge summaries from a hospital specializing in Acute Kidney Injury (AKI), MedPromptExtract demonstrated impressive accuracy. The AI-powered anonymization was swift and effective, and the structured data extraction achieved 100% accuracy. While the LLM-driven extraction of complex features showed promising results, with seven out of twelve features achieving high accuracy, there's still room for improvement. The research team found that the LLM sometimes made assumptions based on context, leading to occasional inaccuracies. For example, the LLM might assume a patient received general anesthesia simply because they underwent surgery, which isn't always the case. Future work will focus on refining the prompts and providing more specific instructions to the LLM to address these nuances. MedPromptExtract isn't just a research project; it's a practical tool already deployed in a real-world hospital setting. With its user-friendly interface, clinicians can easily customize prompts and extract the specific information they need. This tool has the potential to transform healthcare by accelerating research, improving clinical decision-making, and ultimately, leading to better patient outcomes. It's a powerful example of how AI can be harnessed for good, unlocking the potential of medical data while safeguarding patient privacy.