Performance of an OCR-Prompt-LLM Integrated Workflow for Extracting Multi-dimensional Clinical Data in Ischemic Heart Disease

This research aims to evaluate a comprehensive AI-driven workflow for both clinical data extraction and diagnostic classification in coronary artery disease (CAD). Leveraging OCR and Large Language Models (LLMs), the system is designed to extract ten key clinical parameters (such as LVEF and lab results) and provide diagnostic subtypes (UA, STEMI, NSTEMI, CCS) directly from unstructured inpatient records. A man-machine comparative trial will be conducted using a test set of 308 patients, where the performance of the LLM-based workflow will be benchmarked against the average diagnostic accuracy and processing time of seven clinical physicians. The findings will provide evidence for the feasibility of using LLMs to enhance clinical data structuring and diagnostic efficiency in cardiology.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Coronary Artery Disease Arterial Occlusive Diseases Arteriosclerosis Artificial Intelligence (AI) Cardiovascular Diseases Coronary Disease Data Collection Heart Diseases Myocardial Ischemia Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Fuwai Hospital

Beijing, 100037, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients aged 18 years and older.
Clinical records of patients who were previously enrolled in the AIM-CHD (for the pilot/prompt optimization set) or SMART-CHD (for the internal validation cohort) studies.
Patients diagnosed with, or suspected of having, coronary artery disease (CAD), including subtypes: Unstable Angina (UA), STEMI, NSTEMI, and Chronic Coronary Syndrome (CCS).

Exclusion criteria

Clinical records with severe data fragmentation or missing more than 50% of the key clinical indicators.
Handwritten medical records or low-quality scans that are illegible for Optical Character Recognition (OCR) processing.
Duplicate records or records with conflicting "Gold Standard" labels that cannot be reconciled by the expert committee.

Treatment and study plan

OCR-Prompt-LLM Information Extraction Workflow

Device

The intervention is an automated clinical data management system integrating Optical Character Recognition (OCR), optimized Prompt Engineering, and Large Language Models (LLMs). The workflow processes unstructured inpatient records to extract 10 key clinical indicators (e.g., LVEF, CAD subtypes, medications) and classifies the patient into specific coronary artery disease categories (UA, STEMI, NSTEMI, CCS)

Manual Clinical Data Review

Device

Standard manual process where experienced clinical physicians collect and interpret patient information from medical records. This serves as the human benchmark for comparing diagnostic accuracy and operational efficiency.

Primary outcomes

Overall Diagnostic and Extraction Accuracy Rate

Time frame: Through study completion, an average of 3 months.

To calculate the overall accuracy rate of the LLM-based workflow across 308 cases (including the pilot set, internal validation cohort, and external validation cohort) for 10 clinical indicators (e.g., LVEF, blood glucose, etc.) and 4 diagnostic subtypes of coronary artery disease. Accuracy is defined as the proportion of cases where the LLM's extraction or diagnostic results are perfectly consistent with the 'Gold Standard' established by human clinical experts.

Sponsors and collaborators

Lead sponsor

China National Center for Cardiovascular Diseases

Other Gov

Registry information

Acronym: OPAL-CAD

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2026
First posted: Mar 30, 2026
Registry last updated: Mar 30, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed