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

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  • 1Duke University School of Medicine, Durham, North Carolina.

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Summary

A machine learning tool significantly increased documented advance care planning (ACP) conversations for cancer patients. However, this quality improvement intervention did not change end-of-life (EOL) care outcomes.

Keywords:
Advance care planningcancerend-of-lifemachine learningquality improvement

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Area of Science:

  • Oncology
  • Health Informatics
  • Quality Improvement

Background:

  • Prognostication is challenging for cancer patients nearing end-of-life (EOL), delaying crucial advance care planning (ACP).
  • Effective strategies are needed to improve ACP documentation and EOL care for this vulnerable population.

Purpose of the Study:

  • To evaluate the impact of a machine learning-driven mortality prediction algorithm intervention on ACP documentation and EOL care in cancer patients.
  • To assess changes in clinician behavior regarding ACP following the intervention.

Main Methods:

  • Implemented a validated machine learning mortality risk prediction model for solid malignancy patients admitted to a specialized unit.
  • Clinicians received email notifications for high-risk patients, and ACP documentation/EOL outcomes were compared pre- and post-intervention.
  • Statistical analyses included chi-square, Fisher's exact, Wilcoxon rank sum, and Cochran-Mantel-Haenszel tests.

Main Results:

  • Documented ACP conversations increased dramatically from 2.3% to 80.5% (P<0.001) post-intervention.
  • Significant increases in ACP documentation were observed when palliative care specialists or oncologists were notified.
  • No significant differences were found in length of stay, hospice referral, code status, ICU utilization, readmissions, or mortality.

Conclusions:

  • Machine learning-based identification of high-risk cancer patients substantially improved ACP documentation rates.
  • The intervention demonstrated potential in modifying clinician behavior regarding ACP discussions.
  • Further integration of this predictive model into clinical practice is warranted to sustain improvements in EOL care.