Physician Characteristics Associated With Advance Care Planning After a Machine Learning-Based Nudge
Prognostic machine learning models can help identify patients in need of advance care planning (ACP) conversations. However, additional work is needed to understand which factors guide physicians' comfort with and prioritization of ACP conversations, even after such nudges. In this exploratory secondary analysis of a cluster randomized trial, we examine the relationship between characteristics of internal medicine physicians (eg, training background and practice patterns) and likelihood of ACP conversation following notification by a mortality risk prediction machine learning model.
Authors
- Suresh Balu (ORCID: https://orcid.org/0000-0003-4929-9130)
- Yvonne Acker
- Michael Gao (ORCID: https://orcid.org/0000-0003-0534-4403)
- Jessica Ma (ORCID: https://orcid.org/0000-0002-9448-0353)
- David Casarett
- Thomas W. LeBlanc (ORCID: https://orcid.org/0000-0002-0546-7895)
- Mark Sendak (ORCID: https://orcid.org/0000-0001-5828-4497)
- Mihir Patel (ORCID: https://orcid.org/0000-0003-4254-763X)
- Jonathan Walter
- Noppon Setji (ORCID: https://orcid.org/0000-0002-6016-0730)
Institutions
- Duke University (US)
- Geriatric Research Education and Clinical Center (US)
- Durham VA Health Care System (US)
- Duke Institute for Health Innovation (US)
- Duke University Health System (US)
Publication Details
- Journal
- American Journal of Hospice and Palliative Medicine®
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1177/10499091251401462
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00