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Algorithm-Based Palliative Care in Patients With Cancer: A Cluster Randomized Clinical Trial.

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Algorithm-based electronic health record (EHR) defaults significantly increased palliative care (PC) consultations for advanced cancer patients. This strategy also reduced end-of-life systemic therapy, offering a scalable solution for community oncology.

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Area of Science:

  • Oncology
  • Palliative Care
  • Health Informatics

Background:

  • Guideline-recommended early specialty palliative care (PC) access is limited in community oncology settings for patients with advanced solid tumors.
  • Effective strategies are needed to integrate PC into routine cancer care.

Purpose of the Study:

  • To evaluate if algorithm-based defaults within the electronic health record (EHR), incorporating opt-out options and accountable justification, can increase completed PC visits.
  • To assess the impact of this intervention on patient-centered outcomes and end-of-life care.

Main Methods:

  • A 2-arm cluster randomized clinical trial involving 15 community oncology clinics in Tennessee.
  • Patients with advanced lung or gastrointestinal cancer were identified via EHR algorithm.
  • Intervention sites used EHR defaults for PC orders with accountable justification for opting out; control sites managed referrals at clinician discretion.

Main Results:

  • The intervention group showed a significantly higher rate of completed PC visits (43.9%) compared to the control group (8.3%) (adjusted odds ratio, 8.9).
  • Patients in the intervention group received less systemic therapy within 14 days of death (6.5% vs. 16.1%).
  • No significant differences were observed in quality of life or feeling heard and understood.

Conclusions:

  • Algorithm-based EHR defaults with accountable justification represent a scalable strategy to enhance PC referrals in community oncology.
  • This approach effectively increases PC consultations and may reduce intensive end-of-life treatment.