# Computerized Adaptive Suicidal Risk Stratification and Prediction

> **NIH NIH R44** · ADAPTIVE TESTING TECHNOLOGIES · 2022 · $85,001

## Abstract

Project Summary
The software to be created with this SBIR, the Computerized Adaptive Test for Suicide Scale – Expanded
(CAT-SSE), will help healthcare systems to identify and monitor suicide risk in a more robust and valid way
than is currently done. In the final stage of Phase 2, we proposed to integrate the validated CAT-SSE with
Epic, the EHR used at the University of Massachusetts Memorial Healthcare (UMass) system. Notably, this
two-way, deep integration will allow us to import the MHRN indicators and use them in the CAT-SSE risk
stratification equations and deliver the results and clinical decision support in the EHR for clinicians to access
and act on at the point of care.
Through the first administrative supplement (R44MH118780-01A1), our aims with the Epic integration were to
(1) build the de-identification model (see Figure), and (2) build the tools to facilitate translation of this approach
at other sites. We have since deployed a multi-tier architecture for building the CAT-SSE into Epic such that
patient identifiers are not shared with ATT. We were able to successfully transfer data between ATT and the
UMass Epic servers within an Epic test environment. In this Phase 2 administrative supplement, we are
requesting funds for UMass IT to complete the refinement of the existing integration of the CAT-SSE into their
Epic EHR instance.
The main outcomes of the integration refinement will be: (1) Development of elements that ensure clinical
usability and interpretability are maximized. These elements include but are not limited to data visualization,
guidance language, and Epic alerts. (2) Development of remote and in-person CAT-SSE delivery options for
CAT-SSE participants. (3) Successful real-time import of CAT-SSE scores from ATT that leverage MHRN data
to calculate a suicide risk prediction. It is estimated that a total of 370 hours of IT work are required to complete
the above outcomes.
Unfortunately, Due to COVID-19, the project encountered major enrollment delays, which forced the study
team to revise the initial enrollment targets and divert all available funds to maximize subject enrollments. In
addition, UMass IT department changed its priorities to advance COVID-related patient care projects (e.g.
telehealth) over research projects. Because of the increased IT labor post-pandemic, and utilizing all available
resources to recover the enrollment loss during COVID peaks, we will need additional funds to complete this
important part of the project. Details on how these funds will be used are summarized in the budget
justification.

## Key facts

- **NIH application ID:** 10611259
- **Project number:** 3R44MH118780-03S1
- **Recipient organization:** ADAPTIVE TESTING TECHNOLOGIES
- **Principal Investigator:** Edwin D Boudreaux
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $85,001
- **Award type:** 3
- **Project period:** 2019-05-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10611259

## Citation

> US National Institutes of Health, RePORTER application 10611259, Computerized Adaptive Suicidal Risk Stratification and Prediction (3R44MH118780-03S1). Retrieved via AI Analytics 2026-06-03 from https://api.ai-analytics.org/grant/nih/10611259. Licensed CC0.

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