Improving patient safety by predicting medical errors using electronic health records.
Official title: Learning alerting models for clinical care from EMR data and human knowledge
Funded at University of Pittsburgh at Pittsburgh · NIH-10909221 · Data updated 2026-11-12
This study is working on smart computer systems that use patient records to help doctors spot and avoid mistakes in treatment, making sure patients get the right care based on their specific needs.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project start | September 30, 2022 |
| Project end | June 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $614,315 |
| NIH project number | 5R01EB032752-10 |
| Project ID | NIH-10909221 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients undergoing complex medical treatments, such as liver transplantation, who are at risk of experiencing medication errors.
✕Less likely to help
Patients who are not receiving complex medical treatments or those with stable, low-risk conditions may not benefit from this research.
What this research studies
This research focuses on developing advanced computer-based alert systems that utilize data from electronic medical records (EMRs) to help clinicians identify and prevent medical errors. By analyzing past patient data, the project aims to create machine learning models that predict necessary medical actions for patients based on their conditions. If a patient's treatment deviates from what is expected for similar cases, an alert will be generated to notify healthcare providers, potentially preventing adverse events. This approach seeks to enhance clinical decision-making and improve patient outcomes.
Why it matters
Potential benefit: If successful, this research could significantly reduce the occurrence of preventable medical errors, leading to safer patient care.
How similar studies have gone: Previous research has shown promise in using machine learning and EMR data to improve clinical decision-making, indicating that this approach has potential for success.
Where this research is happening
-
University of Pittsburgh at Pittsburgh
Pittsburgh, United States Active
Researchers
- Principal investigator: Hauskrecht, Milos — University of Pittsburgh at Pittsburgh
- Study coordinator: Hauskrecht, Milos
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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.