Personalizing depression treatment in primary care
Official title: Improving Outcomes in Depression in Primary Care
Funded at Harvard Medical School · NIH-11414812 · Data updated 2026-11-12
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.
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) | $148,046 |
| NIH project number | 3R01MH121632-04S1 |
| Project ID | NIH-11414812 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with moderate to severe depression receiving care in participating primary care settings who can consider antidepressant medication or brief behavioral activation are the ideal candidates.
✕Less likely to help
People with mild depression, those already under specialty psychiatric care, or those unable to take antidepressants or participate in brief behavioral therapy may not benefit from this approach.
What this research studies
If you join, you would share routine health and personal information that researchers will use to build a machine-learning rule to predict whether an antidepressant or a brief behavioral program (Healthy Activity Program) is likely to work best for you. Some patients will be assigned treatments based on this rule while others will receive usual assignment by chance, and outcomes like symptom remission will be compared. The team will also examine whether using the rule saves money compared with usual care. They aim to also identify people who are unlikely to respond to either option so they can be directed to specialist care.
Why it matters
Potential benefit: If successful, matching people to the treatment most likely to work could increase remission rates and reduce time spent trying ineffective options.
How similar studies have gone: Previous trials have shown that antidepressants and the Healthy Activity Program can work in primary care, but using machine-learning to pick the best treatment for each person is relatively new and not yet widely proven.
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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Matching primary care patients to the best depression treatment
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.
-
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.