# TIER-PALLIATIVE CARE: A population-based care delivery model to match evolving patient needs and palliative care services for community-based patients with heart failure or cancer

> **NIH NIH R56** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $167,035

## Abstract

ABSTRACT
Persons with serious illness suffer from poor symptom control, decreased quality of life (QoL) and poor
communication with their healthcare providers, especially in terms of goals of care discussions (GOCD).
Palliative care, when offered alongside disease management, offers improved symptom control, QoL,
communication, caregiver satisfaction and reduced caregiver anxiety. Due to a limited specialty-trained
palliative care workforce, however, patients and their caregivers often cannot access these benefits, especially
in the community. These needs are particularly acute in advanced cancer and HF, the two leading causes of
death in the US which also model the most common illness trajectories. The dynamic nature of these illnesses
presents distinct symptom patterns and changing functional status that require an adaptive, dynamic model of
palliative care delivery. Yet, workforce shortages prevent scaling of existing community-based specialty
palliative care models. To meet patient/caregiver dyads' needs with a limited workforce, new models that
deploy palliative care clinicians based on patient's illness trajectory and changing needs are required. The
innovative TIER-PALLIATIVE CARE (TIER-PC) model provides the right level of care to the right patients at the
right time. TIER-PC increases the number and intensity of specialty trained palliative care disciplines added to
the dyad's care team as their symptoms worsen and function declines. In Tier 1, patients who can care for
themselves and have easily managed symptoms, receive support from a community health worker (CHW)
trained to elicit illness understanding in a culturally competent way. In Tier 2, for patients with poorer function
and mild symptoms, a social worker (SW), trained in serious illness communication, joins the CHW to further
elicit patients' illness understanding and goals, and provide caregiver support. In Tier 3, as function decreases
and symptoms increase, an advance practice nurse (APN) joins the CHW+SW to manage complex symptoms.
In Tier 4, for those patients with the poorest function and worst symptoms, an MD joins to address the most
complex needs (e.g., end-of-life treatment preferences and multifaceted symptom control). The CHW follows
dyads longitudinally across all tiers and re-allocates them to the appropriate tier based on their evolving needs.
We will evaluate TIER-PC's efficacy in a multi-site, single blinded, two arm, randomized controlled trial. Patients
with advanced cancer or HF will receive regular assessments by the TIER-PC team to: address symptom and
psychosocial needs; improve illness/prognostic understanding; prescribe medications; and address goals of
care. We will enroll and randomize 400 patients with HF or cancer and their family caregivers to receive TIER-
PC or an augmented control. We will follow dyads for 12 months to determine if TIER-PC: improves patients'
symptom control and QoL (primary outcomes), patient-reported GOCDs and caregiver satisf...

## Key facts

- **NIH application ID:** 10910250
- **Project number:** 5R56NR020624-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Laura Pilar Gelfman
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $167,035
- **Award type:** 5
- **Project period:** 2023-08-18 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10910250

## Citation

> US National Institutes of Health, RePORTER application 10910250, TIER-PALLIATIVE CARE: A population-based care delivery model to match evolving patient needs and palliative care services for community-based patients with heart failure or cancer (5R56NR020624-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10910250. Licensed CC0.

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