Matching primary care patients to the best depression treatment
Official title: Improving Outcomes in Depression in Primary Care
Funded at Harvard Medical School · NIH-11414813 · Data updated 2026-11-12
This project uses routine information from primary care patients with moderate to severe depression to match them to either an antidepressant or a brief behavioral therapy so more people get better.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Harvard Medical School NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | May 18, 2022 |
| Project end | April 30, 2027 |
| Funding year | 2025 |
| Award (this year) | $462,453 |
| NIH project number | 3R01MH121632-04S2 |
| Project ID | NIH-11414813 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults seen in primary care with moderate to severe depressive symptoms who are willing to try antidepressant medication or brief behavioral therapy are ideal candidates.
✕Less likely to help
People with only mild symptoms, those already under specialty psychiatric care, or those unwilling to try the study treatments are less likely to benefit.
What this research studies
You'll be asked to provide routine information about your symptoms, medical history, and other measures collected in primary care. Researchers will apply machine-learning to those baseline measures to create a precision rule that predicts whether an antidepressant or a brief behavioral activation therapy (HAP) is more likely to help you. Patients will be enrolled in a randomized design to test whether following this precision rule improves remission rates and is more cost-effective than usual care. The team will also identify patients who are unlikely to respond to either option and should be referred to specialist care.
Why it matters
Potential benefit: If successful, this could help more people get the right depression treatment faster and increase their chances of remission.
How similar studies have gone: Antidepressants and the Healthy Activity Program have shown benefit in past trials, but using machine-learning rules to match individuals to the best option is a newer approach with limited prior proof.
Where this research is happening
-
Harvard Medical School
Boston, United States Active
Researchers
- Principal investigator: Patel, Vikram H — Harvard Medical School
- Study coordinator: Patel, Vikram H
Related research
Other NIH-funded projects on the same topics.
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Single-session intervention and interpersonal therapy for teen depression in Mozambican clinics
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Shared Decision Making for Depression Care in Primary Care
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Single-Session Intervention to Help Adolescent Depression in Mozambican Clinics
A one-visit intervention delivered by non-specialists aims to reduce depression symptoms for adolescents in Mozambican primary care, with implementation in real-world clinic settings.
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ETUDES: Improving Care for Suicidal Youth in Pediatric Primary Care
A center-led effort to help pediatric primary care teams better detect, triage, and manage suicidal thoughts and behaviors in teens.
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Personalizing depression treatment in primary care
This project uses patient information and machine learning to match adults with moderate to severe depression in primary care to the antidepressant or brief behavioral therapy most likely to help them.
-
Behavioral telehealth for anxious and depressed youth in low-resource clinics
A short, friendly program uses telehealth to help kids 8 and up in low-resource primary care settings feel less anxious and sad.
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.