# Improved tailoring of depression care using customized clinical decision support

> **NIH NIH R01** · KAISER FOUNDATION RESEARCH INSTITUTE · 2021 · $373,803

## Abstract

PROJECT SUMMARY
Treatments for mental health conditions such as unipolar depression provide modest average benefit but have
wide variation between individuals and within individuals over time. Evidence-based customized treatment
protocols would improve the mental health care of many people by providing treatment recommendations for
individuals that take into account potential variation because of personal characteristics such as current health
status, symptoms, and response to earlier treatment. Generating customized treatment protocols requires
large amounts of data, such as from networks of health systems that can link electronic health records from
millions of individuals. Current statistical approaches for discovering customized treatment protocols are limited
in three important ways.
 First, current approaches rely on scientists to select the patient characteristics to use to customize
treatments instead of using data to find the patient characteristics that will lead to improved, customized care.
Second, customized treatment protocols discovered with current statistical methods assume no unobserved
differences between individuals who receive various treatment options. Third, investigators do not have ways
to know if the available data contain enough information to discover and compare customized treatment
protocols precisely enough to make clinical decisions. We will address these three limitations by developing
new statistical tools for discovering customized treatment protocols using electronic health records data. Our
research team has expertise and experience in statistics, epidemiology, and mental health care. We will
integrate methods that have been successfully used in other settings to improve statistical approaches for
discovering customized treatment protocols and address these three important limitations.
 We will extend machine learning tools for selecting important pieces of information to the time-varying data
structure required for discovering customized treatment protocols. We will build approaches that use available
knowledge about the size of unobserved differences between groups of people who received different
treatments to assess how those differences change study results. By building on the math used to estimate the
sample sizes needed for precision in randomized trials with complex designs, we will develop new formulas for
determining how many people with a particular condition and who took a particular drug are needed in a health
system to provide enough accurate information to discover customized treatment protocols.
 Using data from the electronic health records of more than 15,000 patients, we will discover customized
treatment protocols for depression. By improving statistical tools and addressing current limitations, our
customized treatment protocols will have immediate impact for people living with unipolar depression. The
statistical tools we develop will also be useful for discovering customized treatment pr...

## Key facts

- **NIH application ID:** 10164627
- **Project number:** 5R01MH114873-04
- **Recipient organization:** KAISER FOUNDATION RESEARCH INSTITUTE
- **Principal Investigator:** Erica Moodie
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $373,803
- **Award type:** 5
- **Project period:** 2018-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10164627

## Citation

> US National Institutes of Health, RePORTER application 10164627, Improved tailoring of depression care using customized clinical decision support (5R01MH114873-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10164627. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*