# Characterizing and Predicting Patient Trajectories After Live Discharge from Hospice among Older Adults with Alzheimer's Disease and Related Dementias

> **NIH NIH R56** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $596,258

## Abstract

PROJECT ABSTRACT
Almost half of the 1.7 million hospice enrollees have Alzheimer's disease and related dementias (ADRD) as
the principal diagnosis or a comorbidity. Although hospice care is particularly beneficial to support ADRD
patients and their caregivers at the end of life (EoL), one in five hospice enrollees with ADRD experience
discharge from hospice prior to death, also known as “live discharge.” Hospice live discharge has a profound
impact on EoL care of ADRD patients due to their complex EoL conditions, highly uncertain prognosis, and
difficulty in communicating EoL care preference. Despite two recent policy changes, hospice live discharges
remain persistently high among ADRD patients. The changing hospice market landscape may keep boosting
live discharges among ADRD patients and lead to frequent care transitions and other adverse EoL outcomes
after hospice live discharge. To date, critical gaps exist in understanding outcomes after hospice live discharge
among ADRD patients. Little is known about the longitudinal patterns of care transitions in all care settings,
functional decline, and overall healthcare utilization after hospice live discharge. Although Medicare Advantage
(MA) covers half of Medicare beneficiaries, there is scant evidence about outcomes after hospice live
discharge among MA enrollees. No validated tools are available to predict patient outcomes after hospice live
discharge to facilitate discharge planning and post-discharge care coordination. This mixed-methods project
aims to fill these gaps by characterizing and predicting patient trajectories after hospice live discharge among
ADRD patients. In Aim 1, we will apply longitudinal clustering methods with national Medicare fee-for-service
(FFS) claims and MA encounter data to identify patient subgroups with heterogenous trajectories after hospice
live discharge. In Aim 2, we will develop and validate prediction models for patient trajectories and other
clinically important outcomes after hospice live discharge. We will use Medicare FFS claims, MA encounter
data, electronic health record (EHR) data from two large hospices, and linked claims/encounter data and EHR
data to develop prediction models and ensure fairness of model accuracy by patient race/ethnicity and
socioeconomic status. In Aim 3, we will conduct in-depth qualitative interviews with different stakeholders (e.g.,
physicians, patients and caregivers, and informatics staff) who are involved in the implementation of the
prediction models in care delivery. Evidence from this aim will facilitate the transition from model development
to real-world implementation in the future that can benefit ADRD patients and their caregivers. This project is
well aligned with the National Institute on Aging's priority to better understand the burden of ADRD on patient
outcomes. The proposed research will generate novel evidence to improve patient outcomes after hospice live
discharge and inform the ongoing pilot programs to de...

## Key facts

- **NIH application ID:** 11121386
- **Project number:** 1R56AG085541-01A1
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Yongkang Zhang
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $596,258
- **Award type:** 1
- **Project period:** 2024-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11121386

## Citation

> US National Institutes of Health, RePORTER application 11121386, Characterizing and Predicting Patient Trajectories After Live Discharge from Hospice among Older Adults with Alzheimer's Disease and Related Dementias (1R56AG085541-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11121386. Licensed CC0.

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