Improving the process of gathering and analyzing medical research data
Official title: Automating Tabular Data Extraction to Accelerate Evidence Synthesis and Systematic Literature Review
Funded at Nested Knowledge, INC · NIH-11008123 · Data updated 2026-11-12
This study is working on a new tool that uses artificial intelligence to make it quicker and cheaper for doctors to find and review important research about medical treatments, so patients can get better and faster information about their options.
Quick facts
| Grant type | Sbir 1 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Nested Knowledge, INC NIH-funded |
| Lab location | 1 site (Saint Paul, United States) |
| Project start | September 10, 2024 |
| Project end | March 9, 2026 |
| Funding year | 2024 |
| Award (this year) | $304,971 |
| NIH project number | 1R43LM014655-01 |
| Project ID | NIH-11008123 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for benefiting from this research include patients seeking the latest evidence-based treatments and therapies for their conditions.
✕Less likely to help
Patients who are not actively seeking new treatment options or who are not involved in clinical decision-making may not receive direct benefits from this research.
What this research studies
This research focuses on developing an automated platform to streamline the process of conducting Systematic Literature Reviews (SLRs), which are essential for evaluating the safety and efficacy of medical treatments. By utilizing artificial intelligence, the project aims to reduce the time and cost associated with SLRs, which currently take an average of 18 months and $141,000 to complete manually. The new platform will facilitate the search, screening, and extraction of relevant data, making it easier for healthcare providers to access up-to-date information for decision-making. Patients can benefit from faster and more accurate assessments of treatment options as a result of this improved methodology.
Why it matters
Potential benefit: If successful, this research could lead to quicker access to reliable medical information, ultimately improving patient care and treatment decisions.
How similar studies have gone: Previous research has shown that automation and AI can significantly enhance data analysis processes in clinical settings, indicating a promising potential for this approach.
Where this research is happening
-
Nested Knowledge, INC
Saint Paul, United States Active
Researchers
- Principal investigator: Kallmes, Kevin — Nested Knowledge, INC
- Study coordinator: Kallmes, Kevin
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.