# Multi-modal Health Information Technology Innovations for Precision Management of Glaucoma

> **NIH NIH DP5** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $395,000

## Abstract

PROJECT SUMMARY/ABSTRACT
Glaucoma is the world's leading cause of irreversible blindness and will affect >110 million
people by 2040. Early detection and treatment are critical, as symptoms typically do not present
until the disease is advanced. A data-driven precision medicine approach is needed to better
identify individuals who are at greatest risk of developing the disease and who are at greatest
risk of progressing quickly to vision loss. While there has been considerable progress in eye
imaging and testing to improve glaucoma monitoring, precision management of glaucoma is
incomplete without accounting for patients' co-existing systemic conditions, concurrent systemic
medications and treatments, and adherence with prescribed glaucoma treatment.
Understanding how systemic conditions, and specifically vascular conditions such as
hypertension, impact glaucoma presents growing public health importance given the increasing
co-morbidities facing aging populations. Preliminary studies have demonstrated the predictive
value of systemic data, even without ophthalmic endpoints. Similarly, measuring medication
adherence is important for guiding patient counseling and engagement and avoiding
downstream interventions such as surgeries, which carry high cost and morbidity. These factors
are important for providing a more comprehensive perspective of glaucoma management and
for improving patient outcomes, yet they are relatively understudied.
I propose applying multi-modal advancements in health information technology (IT) to address
these gaps and achieve the following specific aims: (1) Develop machine learning-based
predictive models classifying patients at risk for glaucoma progression using systemic electronic
health record (EHR) data from a diverse nationwide patient cohort; (2) evaluate how integrating
blood pressure (BP) data from novel smartwatch-based home BP monitors enhance predictive
models for risk stratification in glaucoma, and (3) measure glaucoma medication adherence
using innovative flexible electronic sensors to validate their use for future interventions aimed at
improving adherence and clinical outcomes in glaucoma. These studies would leverage state-
of-the-art methods in big-data predictive modeling as well as cutting-edge advancements in
sensor technologies. This multi-faceted approach will build a foundation for a health IT
framework geared toward improving risk stratification and generating novel therapeutic targets
for glaucoma patients.

## Key facts

- **NIH application ID:** 10690577
- **Project number:** 5DP5OD029610-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Sally Liu Baxter
- **Activity code:** DP5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $395,000
- **Award type:** 5
- **Project period:** 2020-09-10 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10690577

## Citation

> US National Institutes of Health, RePORTER application 10690577, Multi-modal Health Information Technology Innovations for Precision Management of Glaucoma (5DP5OD029610-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10690577. Licensed CC0.

---

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