Glaucoma affects more than 70 million people worldwide and is the world's leading cause of irreversible
blindness. The only current method to delay its development and progression is by lowering intraocular
pressure (IOP), achieved with topical administration of eyedrops. Adherence rates for glaucoma eyedrop
administration are poor, in many cases below 50%, resulting in disease progression, eventual blindness, and a
more than 2-fold increase in healthcare costs.
African Americans and Latinos carry a significantly higher glaucoma burden compared with Caucasians.
Minorities have additionally been found to have disproportionately lower rates of medication adherence.
Previously studied interventions aimed at improving glaucoma adherence had key limitations that particularly
affect minorities, including unreliable self-reported measures of adherence, lack of consideration of individual
circumstances influencing glaucoma medication management, and developing/testing interventions in
predominantly Caucasian populations.
Health information technology has experienced rapid advancement in the last decade with the electronic health
record (EHR), the proliferation of accessory mobile health technologies, and the advancement of artificial
intelligence. Although their integration holds great promise to enable screening tools for diagnosis and risk
prediction, successful integration to aid minority populations in real-world settings depends on: understanding
how the collected information relates to the patient's other (e.g. clinical) data and the patient's socio-cultural
context; seamless information exchange and interoperability with the EHR, the central portal of healthcare
delivery; and integration of algorithmic findings into workflows involving both providers and patients to deliver
information and/or recommendations in a simple, actionable manner.
Glaucoma is a complex chronic disease, spanning decades of patients' lives and requiring ongoing monitoring
and evaluation, thus making it an ideal application for the use of health IT to reduce racial disparities. In this
proposal, we aim to accomplish this by: demonstrating the effectiveness of a flexible electronic eyedrop sensor
to generate granular digital signatures of an individual's adherence and contextualizing this data in a socio-
cultural context with patient interviews (Aim 1), combining adherence data with EHR variables to construct
machine learning models to predict IOP control and enhance clinical risk stratification (Aim 2), and prototyping
a dynamic dashboard for intervention coordination (Aim 3). Altogether, success of this innovative,
comprehensive, culturally-tailored, and scalable health IT framework will improve medication adherence and
slow disease progression among minorities, therefore narrowing this important racial health disparity.