Yair Bannett
Stanford University, CA·Medicine & Clinical Research·Early-career
Attention-Deficit/Hyperactivity Disorder (ADHD) affects 8–10% of U.S. children, and most are treated by primary care providers (PCPs), the family doctors and pediatricians they see routinely.
- Fields
Medicine & Clinical Research
Public Health & Epidemiology
Computer Science
- Topics
attention-deficit/hyperactivity disorder
pediatrics
primary care
- Grants
- 1 known grant, through Jul 2027
What they work on
- Develop guideline-based quality measures for primary care treatment of children aged 4–11 with ADHD, combining machine learning analysis of free-text clinical notes with structured electronic health record data.
- Assess how closely primary care providers follow evidence-based ADHD treatment guidelines, detect sociodemographic disparities in care, and reduce bias in the machine learning models.
- Engage family and clinician stakeholders to prioritize quality improvement interventions for ADHD care that they consider feasible, acceptable and important, including ones aimed at reducing disparities.
More about the research
The quality of this care varies, and differences linked to families' social and demographic backgrounds may drive some of these gaps. Poor care can lead to lifelong health problems or unneeded treatment. Fixing these gaps first requires good ways to measure care quality. That is hard, because much of what doctors do is recorded only in free-text clinical notes.
Why it matters
Children with ADHD and their families could benefit from more consistent, guideline-based treatment in primary care. Quality measures drawn automatically from health records could help clinics track care, spot disparities between groups of children, and choose improvements that families and clinicians support. The approach may later extend to other childhood mental and behavioral health conditions.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Matthew Engelhard
Duke University, NC·Computer Science·Early-career
Develop machine-learning models that predict autism spectrum disorder and ADHD from early electronic health record data, such as diagnosis codes, labs, vitals and clinical notes, while correcting for differences in health care use.
Matthew Churpek
University of Wisconsin-Madison, WI·Medicine & Clinical Research
Improve early identification and risk stratification of infected hospital patients by applying natural language processing and deep learning to structured and free-text electronic health record data from multiple centers.
Nina Bickell
Icahn School of Medicine at Mount Sinai, NY·Public Health & Epidemiology
Use NYC-wide visit data on 12 million patients to describe how mixed or uniform outpatient practices are by insurance and other factors, and test links to quality measures in pediatric preventive care, obstetric procedures and chronic disease management.
The school
Stanford University: 87 labs on PI Pages, $1.6B a year on research, 739 research doctorates a year.
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