Machine Learning for Targeted Advance Care Planning in Cancer Patients: A Quality Improvement Study.

ABSTRACT:

Context

Prognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL).

Objectives

Examine a quality improvement mortality prediction algorithm intervention's impact on ACP documentation and EOL care.

Methods

We implemented a validated mortality risk prediction machine learning model for solid malignancy patients admitted from the emergency department (ED) to a dedicated solid malignancy unit at Duke University Hospital. Clinicians received an email when a patient was identified as high-risk. We compared ACP documentation and EOL care outcomes before and after the notification intervention. We excluded patients with intensive care unit (ICU) admission in the first 24 hours. Comparisons involved chi-sq

SUBMITTER: Patel MN 

PROVIDER: S-EPMC11536198 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature