# Using Search Engine Data for Detection and Early Intervention in Suicide Prevention

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2022 · $149,579

## Abstract

ABSTRACT. This is a request to supplement grant award R01MH123484 Using Search Engine Data for
Detection and Early Intervention in Suicide Prevention in response to NOT-OD-22-026 Administrative
Supplement for Research and Capacity Building Efforts Related to Bioethical Issues. This supplement proposal
focuses on Bioethics Research. The parent award will determine whether internet search histories and on
search behavior on the Google Search Engine donated by and prospectively collected by people with varying
degrees of suicide risk will be successful in determining proximal risk of suicide. In a previous study, people
with a recent suicide attempt donated retrospective data downloaded from the Google Take Out tool (GTO).
We were able to identify behavioral and linguistic patterns that predicted suicide attempts 30-60 days before
the event occurred. The currently funded study will ask 1,000 people with varying risk for suicide to donate
retrospective data and to continue to donate these data for 1 year. Participants complete a retrospective
interview and prospective surveys every two weeks about the occurrence of suicidal behavior and attempts.
Should we be able to demonstrate to scale the same results we found in the previous pilot project, the data
from this current study could be game changing in the detection of proximal suicide risk. Given that 77 percent
of the US population1 seek information online almost entirely using Google Search, any risk prediction
algorithm and subsequent intervention should be able to reach at-risk Americans to prevent this serious public
health outcome. However, should we be successful, there are a number of ethical, legal, and societal
implications that still need to be addressed. To understand these implications, we will qualitatively interview 50
study participants in a series of focus groups (25 with no previous experience with treatment for suicide and 25
with that experience) and 20 interventionists (clinicians and community workers) about ethical and equitable
application of such an algorithm to interventions to prevent suicide. We include the perspectives of
interventionists in this study to identify where consumers and interventionists agree on ethical, legal, and
societal implications and where there maybe divergence of opinion. Consultation with ethicists will guide the
development of the questions and interpretation of results. Guided by the Digital Health Framework, we will
present participants with different scenarios about privacy concerns (choice to share, what data to share),
risk/benefit concerns (which agent should have access to the MLA and be responsible for acting on a MLA
recommendation), accessibility and usability concerns (diversity representation and access; which
interventions are acceptable with a specific eye toward moral and equitable resource allocation), and data
management concerns (where and how the data should be stored). Participants will also be asked to consider
what potential ...

## Key facts

- **NIH application ID:** 10591819
- **Project number:** 3R01MH123484-02S1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Patricia A. Arean
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $149,579
- **Award type:** 3
- **Project period:** 2021-05-05 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10591819

## Citation

> US National Institutes of Health, RePORTER application 10591819, Using Search Engine Data for Detection and Early Intervention in Suicide Prevention (3R01MH123484-02S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10591819. Licensed CC0.

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