Shortening diagnostic odysseys for children and adults using medical records
Using electronic medical record data to shorten diagnostic odysseys for rare genetic disorders in children and adults in two New York City health care settings
This project uses electronic medical records and computer tools to spot people who may have an undiagnosed rare genetic condition so they can be referred for genetics evaluation and testing.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Icahn School of Medicine at Mount Sinai NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project ID | NIH-11266162 on NIH RePORTER |
What this research studies
Researchers will run rules-based and natural language processing algorithms on electronic health records in two New York City health systems to flag infants, children, and adults with unusual illness patterns. Flagged patients will be invited to a new Mount Sinai Genetics Outreach clinic staffed by medical geneticists for clinical evaluation and possible diagnostic testing. The team will pilot-identify about 200 children aged 0–12 with a high chance of having an undiagnosed rare genetic trait, and will survey and educate pediatricians at five practices about diagnostic delays and testing. Study outcomes will include how many flagged patients receive referrals, the results of genetic evaluations, and whether the approach shortens the time to diagnosis.
Who could benefit from this research
Good fit: Ideal candidates are infants, children (especially ages 0–12), and adults who receive care in the participating New York City health systems whose records show unexplained or atypical illness and who have not yet had a genetics evaluation.
Not a fit: Patients who already have a confirmed genetic diagnosis, whose symptoms have clear non-genetic causes, or who do not receive care within the participating NYC health systems are unlikely to benefit from this project.
Why it matters
Potential benefit: If successful, this approach could lead to earlier diagnosis of rare genetic conditions and faster access to appropriate genetic care and testing.
How similar studies have performed: Prior work by the team showed that EHR-based rules and NLP algorithms can accurately identify atypically ill infants and children and that flagged cohorts are enriched for patients who had genetic testing, but using these algorithms to refer patients and measure clinical diagnostic outcomes is newer.
Where this research is happening
New York, United States
- Icahn School of Medicine at Mount Sinai — New York, United States (Active)
Researchers
- Principal investigator: Gelb, Bruce D — Icahn School of Medicine at Mount Sinai
- Study coordinator: Gelb, Bruce D
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.