# iSMART: intelligent Social risk Management in AD/ADRD paTients

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2024 · $752,903

## Abstract

ABSTRACT
People living with dementia (PLWD) from racial-ethnic minoritized groups and socioeconomically disadvantaged
environments are more likely to face barriers to diagnosis, care, and services. Multiple social determinants of
health (SDoH) contribute to the disparities in Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD)
progression and the quality of AD/ADRD care. Thus, AD/ADRD is a public health crisis that must be managed
not only by traditional medical care but also by addressing patients’ unmet social needs. Artificial intelligence (AI)
and large real-world data (RWD), such as electronic health records (EHR), offer an opportunity to develop
innovative approaches that improve health and health equity by addressing SDoH.
The objective of this project is to develop a machine learning (ML)-based social risk management platform -
ISMART (intelligent Social risk Management in AD/ADRD paTients) - that can be embedded into EHR
systems to improve the quality of care and quality of life of PLWD. We will use RWD from the OneFlorida+
network, a member of the National Patient-Centered Clinical Research Network (PCORnet), comprising EHR
data from >20M individuals. We will leverage our prior work that established an external exposome database
with contextual SDoH measures documenting social and physical environments and a natural language
processing pipeline that can extract person-level SDoH (including caregiver information) from clinical narratives
in EHRs. Our study will follow an intervention mapping approach that engages a Stakeholder Advisory
Committee to achieve three Specific Aims. In Aim 1, we will build an RWD cohort of PLWD and to identify key
contextual and person-level SDoH associated with PLWD care and outcomes. In Aim 2, we will develop ML-
based social risk management algorithms for dementia care and outcomes, including (a) a fair individualized
polysocial risk score (iPsRS) to screen for unmet social needs in PLWD; and (b) causal-principled AI methods
to quantify the causal, heterogeneous effect of key actionable SDoH (e.g., food) on PLWD care and outcomes.
In Aim 3, we will co-design with stakeholders the ISMART platform, including (a) prototyping ISMART platform
following a User-Centered Design process; and (b) developing recommendations for future implementation and
evaluation via focus groups and Delphi panels.
The success of our project will lead to the development of ISMART prototype for social risk management in
PLWD, with a set of strategies for future implementation and evaluation. Our innovative, structured approach to
integrating social risk management with health care of PLWD may lead to a necessary paradigm shift in US
health care delivery.

## Key facts

- **NIH application ID:** 10975418
- **Project number:** 1R01AG089445-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jiang Bian
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $752,903
- **Award type:** 1
- **Project period:** 2024-08-15 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10975418

## Citation

> US National Institutes of Health, RePORTER application 10975418, iSMART: intelligent Social risk Management in AD/ADRD paTients (1R01AG089445-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10975418. Licensed CC0.

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