Automatic referral summaries to improve specialist care
Closing the loop with an automatic referral population and summarization system
This project builds an AI tool that pulls key details from your electronic health record to create clear referral summaries for doctors treating headaches and other conditions.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Weill Medical Coll of Cornell Univ NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project ID | NIH-11332803 on NIH RePORTER |
What this research studies
If you're referred to a specialist, the team will use machine learning and natural-language tools to read and combine information from your electronic health record—like diagnoses, medications, tests, and clinical notes—to produce a concise referral form. They will train and test the system on real clinical records and compare the AI-generated summaries with clinician-written referrals to check accuracy and usefulness. The project focuses on handling varied types of medical data and creating summaries that both primary care providers and specialists can use. Privacy protections and de-identification are typically applied when researchers work with patient records.
Who could benefit from this research
Good fit: Ideal candidates are patients whose care and medical records are managed within participating health systems, especially those being referred for headaches (cephalgia) or related conditions.
Not a fit: Patients who receive care across disconnected providers or who do not have electronic records in participating systems may not see direct benefit from this project.
Why it matters
Potential benefit: If successful, this could make referrals faster and clearer, helping specialists have the information they need sooner and reducing delays in care.
How similar studies have performed: Related NLP and deep-learning tools have shown promise in extracting useful information from medical records, but fully automated, cross-system referral summarization remains novel and unproven.
Where this research is happening
New York, United States
- Weill Medical Coll of Cornell Univ — New York, United States (Active)
Researchers
- Principal investigator: Peng, Yifan — Weill Medical Coll of Cornell Univ
- Study coordinator: Peng, Yifan
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.