# Leveraging the electronic health record and behavioral nudges to promote primary and specialist palliative care for inpatients with serious illness: A pragmatic trial

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $663,869

## Abstract

PROJECT SUMMARY
Millions of Americans living with serious illness experience burdensome symptoms and receive aggressive
care that is not aligned with their goals and preferences. A growing body of evidence suggests that palliative
care, which entails a supportive approach to care focused on maximizing quality of life, improves patient-
centered, clinical, and economic outcomes. For this reason, national guidelines recommend that clinicians
either provide palliative care themselves (primary) or consult experts (specialist) as part of standard serious
illness care. For these reasons, most hospitals in the U.S. have invested in specialist palliative care programs.
Yet, palliative care delivery remains insufficient among patients with serious illness, particularly those with
advanced Alzheimer's Disease and Related Dementias (ADRD), and use of specialist palliative care services
is often inefficient and inequitable, largely due to clinicians' difficulty identifying which patients are most likely to
benefit from them. Many hospitals have begun to implement prognostic triggers in the electronic health record
(EHR) to facilitate more reliable and equitable patient identification, however, none have been rigorously tested
for their effects on patient-centered outcomes. Furthermore, palliative care triggers cannot solely rely on the
limited workforce of palliative care specialists, but rather approaches that promote primary and specialist
palliative care are needed, yet evidence is lacking for how to optimally do so. The main objective of this study
is to evaluate a strategy that combines an EHR-based prognostic-trigger with two effective clinician-directed
nudges to provide either primary or specialist palliative care for seriously ill hospitalized patients. Specifically,
the behavioral intervention involves a simple EHR alert to the primary clinicians caring for identified patients
that requires them to actively choose whether or not to provide primary palliative care, and only if they decline,
a default order for specialist palliative care is entered from which they can opt-out. We will conduct a hybrid
type 1 pragmatic, cluster randomized trial among nearly 7,000 patients across 6 diverse hospitals to study the
intervention's effectiveness on hospital-free days and numerous other patient-centered, clinical, and economic
outcomes. We will also conduct an embedded mixed methods study to understand clinician and hospital
contextual factors that influence the intervention's uptake. Finally, we will evaluate for treatment effect
heterogeneity among patients with ADRD and other pre-specified subgroups to determine which types of
patients derive the greatest benefit from a systematic approach to nudge palliative care. This study will provide
high-quality evidence regarding the effectiveness of a scalable and sustainable approach to promote
collaborative primary and specialist palliative care among a large and diverse patient cohort, will advance the
science ...

## Key facts

- **NIH application ID:** 10442225
- **Project number:** 1R01AG073384-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Katherine Rinaldi Courtright
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $663,869
- **Award type:** 1
- **Project period:** 2022-06-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10442225

## Citation

> US National Institutes of Health, RePORTER application 10442225, Leveraging the electronic health record and behavioral nudges to promote primary and specialist palliative care for inpatients with serious illness: A pragmatic trial (1R01AG073384-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10442225. Licensed CC0.

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