# Utilizing Technology and AI Approaches to Facilitate Independence and Resilience in Older Adults

> **NIH NIH P30** · JOHNS HOPKINS UNIVERSITY · 2022 · $241,065

## Abstract

Abstract: Palliative care (PC) is an interdisciplinary concept aimed at improving the wellbeing of 
persons with serious illness throughout their course of illness including end-of-life. PC enables
care decisions that align with patient and caregiver preferences. For persons with Alzheimer’s 
Disease and Related Disorders (ADRD), PC is particularly challenging, as determining a patient’s 
current status on the ADRD disease course is often difficult. Successful PC services in patients 
with ADRD focuses on integrating therapeutic regimens with timely identification and alleviation 
of physical, psychosocial, and decision-making needs of patients and their families. PC can 
promote ethical, equitable, and efficient population health principles by achieving optimal 
healthcare utilization by avoiding overuse and underuse. Many challenges hinder appropriate 
levels of PC integration for persons with ADRD at both the patient- and population-levels. There 
is ongoing discussion on optimal timing of PC delivery in addition to variation in availability of 
well-trained teams and resources to deliver PC. One ubiquitous challenge is the care system’s 
ability to identify those persons that will benefit most from PC. Moreover, there are documented 
disparities in delivery of PC, such that minority race/ethnicity patients receive too little, too late 
care compared to their majority counterparts. Artificial intelligence (AI) predictive modeling 
techniques may enable accurate and timely identification of persons with ADRD who are likely to 
benefit from PC assessment. To achieve our goal of using advanced AI analytic tools to improve 
PC received by persons with ADRD, the project has the following objectives:1) To develop and 
validate advanced predictive models (PM) to identify persons with ADRD who are likely to benefit 
from PC assessment; 2) To evaluate the impact of PM based palliative care interventions on 
population-level healthcare utilization outcomes; 3) To assess the disparities in PC services 
delivery and healthcare utilization in African American and other minority populations with 
ADRD; 4) To initiate first stage of technology transfer of the advanced analytic tools we develop 
by undertaking initial pilots and developing both publicly accessible software and integration into 
the JHU “ACG” population-based platform. Dr. Chintan Pandya (PI) and team will develop 
machine learning prediction models to identify ADRD patients likely to benefit from PC 
assessment. These models will be developed using data captured in electronic health record (EHR)
and other large electronic databases (e.g., insurance claims) of patients with ADRD cared for 
within a large patient/consumer population. In addition to sharing open architecture free-access 
tools at the conclusion of this project, we plan on integrating the software-based algorithms we 
develop into our widely used (reaching 250+ million patients in 20+ nations) Johns Hopkins ACG 
predictive modeli...

## Key facts

- **NIH application ID:** 10652012
- **Project number:** 3P30AG073104-02S3
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Peter M. Abadir
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $241,065
- **Award type:** 3
- **Project period:** 2021-09-30 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10652012

## Citation

> US National Institutes of Health, RePORTER application 10652012, Utilizing Technology and AI Approaches to Facilitate Independence and Resilience in Older Adults (3P30AG073104-02S3). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10652012. Licensed CC0.

---

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