# Integrative Data Science Approach to Advance Care Coordination of ADRD by Primary Care Providers

> **NIH NIH K01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2024 · $119,799

## Abstract

ABSTRACT
Older adults with Alzheimer’s disease and related dementias (ADRD) require care from numerous specialists
and clinical teams to manage ADRD-related symptoms and other comorbidities. The majority of patients with
ADRD have their healthcare managed by non-specialists who often lack the time, confidence, and expertise to
manage ongoing ADRD needs, which leads to significant referral-based care that often suffers from a lack of
coordination. Supporting primary care providers in their ongoing management of care for patients with ADRD by
promoting deliberate organization of care activities and information sharing among clinical teams is a critical
opportunity to limit unintended gaps and ensure that patients with ADRD receive the high-quality multidisciplinary
care necessary for long-term wellbeing. Few solutions exist to measure and identify gaps in care coordination.
Current approaches primarily rely on single payor claims data to evaluate patient sharing relationships between
providers, which neglects to provide granular insight necessary to improve local healthcare delivery. Applying
advanced statistical modeling to EHR usage and communication data will provide critical insight into healthcare
delivery patterns necessary to accurately model and optimize referral-based care coordination.
In the proposed project, I will apply innovative knowledge representation and machine learning to improve
referral-based care coordination by developing intelligent approaches that monitor coordination activities and
recommend actionable opportunities for improvement. Under the guidance of a multidisciplinary team of mentors,
I will receive training to expand my knowledge in healthcare delivery to promote healthy aging, further my
knowledge of state-of-the-art machine learning techniques, and will develop a deeper understanding of
quantitative approaches to investigate complex sociotechnical systems. I will apply this training to address
knowledge gaps related to the formation of referral-based clinical teams in the first two aims: (1) model and
identify patterns of collaboration among healthcare providers teams treating patients with ADRD that contribute
to improved healthcare delivery; and (2) apply natural language processing to messages sent via patient portal
understand how patient and caregiver interactions influence care patterns. In aim 3, I will combine insights and
collaboration networks from the first two aims to develop explainable machine learning models to identify optimal
patterns of care coordination. I will use these optimal care coordination patterns to highlight features that cause
deviation in a patient’s treatment pathway and identify actionable steps for improvement.
This career development award will provide the rigorous training and mentorship necessary to become a fully
independent principal investigator. The research will benefit from a PI who has a strong background in
information science, knowledge representation, and collaborati...

## Key facts

- **NIH application ID:** 10914251
- **Project number:** 5K01AG083133-02
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Bryan Steitz
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $119,799
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10914251

## Citation

> US National Institutes of Health, RePORTER application 10914251, Integrative Data Science Approach to Advance Care Coordination of ADRD by Primary Care Providers (5K01AG083133-02). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10914251. Licensed CC0.

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