A Privacy-Preserving OCR-LLM System for Coronary Syndrome Subtyping From Admission HPI: Multicenter Validation in China and the US

An automated clinical data management workflow integrating Optical Character Recognition (OCR), optimized prompt engineering, and large language models (LLMs). The system processes unstructured inpatient/ED records (primarily admission history of present illness and related narrative text) to extract prespecified key clinical indicators (e.g., left ventricular ejection fraction, coronary syndrome subtype, medications) and to classify cases into prespecified coronary artery disease categories (e.g., unstable angina, STEMI, NSTEMI, chronic coronary syndrome). The workflow outputs structured fields and a classification result with supporting evidence excerpts.

Standard manual process in which experienced clinicians review patient medical records and extract the same prespecified clinical indicators and coronary artery disease categories using routine clinical judgment and documentation review. This manual abstraction serves as the human benchmark for comparing diagnostic accuracy, completeness, and operational efficiency against the automated OCR-Prompt-LLM workflow.