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Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment.

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A prognostic machine learning system can increase early palliative care (PC) access for advanced cancer patients, improving outcomes. This AI tool helps overcome capacity limitations in PC services, ensuring more patients receive timely support.

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Area of Science:

  • Oncology
  • Health Informatics
  • Palliative Care

Background:

  • Early palliative care (PC) improves quality of life, survival, and reduces costs for advanced cancer patients.
  • Existing capacity limitations hinder timely PC consultations for all eligible patients.
  • Prognostic tools are needed to optimize PC resource allocation.

Purpose of the Study:

  • To evaluate if a prognostic machine learning system can facilitate early palliative care (PC) consultations.
  • To assess the system's impact on PC access within existing healthcare capacity.

Main Methods:

  • A machine learning system was developed using population-level administrative data from Ontario, Canada.
  • The system predicted 1-year mortality risk for patients with incurable cancer receiving palliative-intent therapy.
  • The system's potential impact on PC was evaluated by comparing predicted outcomes with usual care.

Main Results:

  • The machine learning system demonstrated a positive predictive value of 69.7% and sensitivity of 74.9% for predicting 1-year mortality.
  • System-guided care could increase early PC by 8.5% overall and 15.3% for patients surviving 6 months post-treatment.
  • This increase was achieved without increasing total PC consultations or significantly impacting patients with prognoses over 2 years.

Conclusions:

  • Prognostic machine learning systems show promise in increasing early PC access despite resource constraints.
  • Real-time deployment and evaluation of these systems are crucial for improving patient care.
  • AI-driven prognostic tools can optimize palliative care delivery for advanced cancer patients.