Better prediction and personalized control of pain after surgery
Official title: Robust Computational and Data Analytic Tools for In-depth Understanding Postoperative Pain Mechanism with Enhanced Pain Management and Clinical Decision Making
Funded at Univ of North Carolina Chapel Hill · NIH-11192819 · Data updated 2026-11-12
This project builds computer tools to predict who will have bad pain after surgery and help doctors tailor pain control for patients recovering from operations.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project start | September 13, 2024 |
| Project end | August 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $474,544 |
| NIH project number | 5R01LM014407-02 |
| Project ID | NIH-11192819 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are people who recently had surgery and whose perioperative care and pain scores are recorded in electronic health records at participating hospitals.
✕Less likely to help
Patients without digital health records at participating sites, those with non-surgical chronic pain conditions, or anyone expecting immediate changes to their current pain care may not directly benefit from this analysis-focused work.
What this research studies
From a patient's point of view, researchers will use large amounts of electronic health record information from people who had surgery to spot patterns linked to worse or longer-lasting postoperative pain. They will build and test advanced computer models that try to separate cause and effect in messy real-world data so the results point to treatments that really help. The team will look for patient groups who respond differently to anesthesia choices and pain medicines so care can be more personalized. The work focuses on analysis of existing clinical records rather than running a new drug trial.
Why it matters
Potential benefit: If successful, the tools could help clinicians identify patients at high risk for severe or chronic post-surgical pain and choose safer, more effective pain plans that reduce unnecessary opioid use.
How similar studies have gone: Previous EHR-based prediction models have shown promise in flagging high-risk surgical patients, but applying rigorous causal methods to guide individualized postoperative pain plans is still relatively new.
Where this research is happening
-
Univ of North Carolina Chapel Hill
Chapel Hill, United States Active
Researchers
- Principal investigator: Zou, Baiming — Univ of North Carolina Chapel Hill
- Study coordinator: Zou, Baiming
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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.