# Leveraging EHR data to evaluate key treatment decisions to prevent suicide-related behaviors

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2020 · $842,134

## Abstract

PROJECT SUMMARY/ABSTRACT
Key focus: Research leveraging EHR data (PAR-18-929) to prevent suicide-related behaviors (SRBs)
Objectives: To use augmented Veterans Health Administration (VHA) EHR data to develop Personalized
Treatment Rules (PTRs) to help guide clinicians in making key treatment decisions for mentally ill patients
aimed at reducing SRBs over the next 12 months.
Specific aims: We will focus on two decisions: the decision of primary care physicians on how to treat patients
coming to them for help with common mental disorders (CMD; “the PCP study”); and the decision of VHA
Suicide Prevention Coordinators on whether to hospitalize patients who just made nonfatal suicide attempts or
treat them as outpatients (“the SPC study”). Both are recognized as critical decisions, with no globally optimal
treatment path for either and little guidance on how to decide among the treatment options.
Research design: We will use a prospective observational design. The PCP study will be based on EHR data
for the roughly 583,000 incident PCP visits of VHA patients for help with a CMD in 2010-2016. An incident visit
will be defined as where the patient had not received other CMD treatment in the prior 12 months. The five
broad PCP treatment options are pharmacotherapy, referral to psychotherapy, pharmacotherapy plus
psychotherapy, pharmacotherapy plus measurement based collaborative care, and referral to a psychiatrist.
The outcomes will be either an SRB (the primary outcome, either suicide death or administratively-recorded
nonfatal suicide attempt) over the next 12 months or psychiatric hospitalization with suicidality over the same
follow-up period (the secondary outcome). These outcomes occurred after 12,292 2010-2016 incident visits.
The SPC study will be based on the 67,196 2010-2016 VHA Suicide Behavior Reports completed after a
nonfatal VHA outpatient suicide attempt. Roughly half of these cases were hospitalized and the others treated
as outpatients. A repeat SRB occurred over the next 12 months for 19,829 of these cases.
Methods: A best-practice method of balancing baseline covariates will be used to adjust for nonrandom
assignment across treatment options. Baseline covariates will include: prior EHR data; EHR data available for
the focal treatment decision, including information abstracted from clinical notes with natural language
processing; small-area geocode data for patient addresses; individual-level data from the LexisNexis Social
Determinants of Health Database on patient finances, employment, marital status, and criminal justice
involvement; and information about prior practice patterns of treating clinicians and practices-resources of
treatment settings. A cutting-edge ensemble machine learning method will be used to analyze these weighted
data to develop PTRs. Cross-validation in the 2010-2016 data and validation in 2017-2018 data (not available
until the third year of the study) will be used to estimate out-of-sample performance of the ...

## Key facts

- **NIH application ID:** 9863809
- **Project number:** 1R01MH121478-01
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** RONALD C KESSLER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $842,134
- **Award type:** 1
- **Project period:** 2020-01-01 → 2023-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9863809

## Citation

> US National Institutes of Health, RePORTER application 9863809, Leveraging EHR data to evaluate key treatment decisions to prevent suicide-related behaviors (1R01MH121478-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9863809. Licensed CC0.

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