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Physician Characteristics Associated With Advance Care Planning After a Machine Learning-Based Nudge.

Mihir N Patel1, Yvonne Acker2, Noppon Setji3

  • 1Duke University School of Medicine, Durham, NC, USA.

The American Journal of Hospice & Palliative Care
|November 21, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can identify patients needing advance care planning (ACP). This study explores physician factors influencing ACP conversations post-notification, aiding targeted physician support.

Keywords:
advance care planningend-of-life careinternal medicinemachine learningpalliative careprognostication

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Area of Science:

  • Medical Informatics
  • Clinical Decision Support
  • Healthcare Delivery Research

Background:

  • Prognostic machine learning models show promise in identifying patients requiring advance care planning (ACP).
  • Physician comfort and prioritization of ACP conversations remain critical, even with predictive model assistance.
  • Understanding physician-specific factors is essential for optimizing the integration of these tools into clinical practice.

Purpose of the Study:

  • To explore the relationship between internal medicine physician characteristics and the likelihood of initiating advance care planning (ACP) conversations.
  • To identify which physician traits, such as training background and practice patterns, influence ACP conversation rates after receiving a machine learning-generated mortality risk notification.
  • To inform strategies for enhancing physician engagement with prognostic tools for advance care planning.

Main Methods:

  • Secondary analysis of a cluster randomized trial data.
  • Examination of internal medicine physician characteristics (training, practice patterns).
  • Assessment of ACP conversation likelihood following notification by a mortality risk prediction machine learning model.

Main Results:

  • Analysis focused on the association between physician attributes and ACP conversation initiation.
  • Investigated how different physician backgrounds and practices correlate with acting on machine learning-generated risk alerts.
  • Results aim to elucidate barriers or facilitators to ACP discussions driven by prognostic technology.

Conclusions:

  • Physician characteristics significantly influence the adoption and prioritization of advance care planning (ACP) conversations prompted by machine learning tools.
  • Identifying these factors can guide the development of more effective implementation strategies for prognostic models in clinical settings.
  • Further research is needed to tailor interventions that support physicians in utilizing predictive analytics for timely ACP discussions.