TrialsgrambetaRCTs are beautiful

Parikh algorithm-based palliative care

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

Patient / Population Intervention / Exposure Comparison Outcome

In 562 patients with advanced lung or gastrointestinal cancer, default palliative care orders with opt-out increased completed palliative care consultations within 12 weeks compared with peer-comparison reports with referral at clinician discretion.

N
562patients
Design
Two-arm cluster RCT, 15 community oncology clinics, Tennessee, USA
Endpoint
Completed palliative care consultation within 12 weeks of enrolment
Relevance
2Important — one of several pillars.
ResultCompleted PC visit 130/296 (43.9%) vs 22/266 (8.3%) (aOR 8.9, 95% CI 5.5–14.6; P<0.001). Among 179 decedents, systemic therapy within 14 days of death 6.5% vs 16.1% (aOR 0.3, 95% CI 0.1–0.7; P=0.05). No differences in quality of life, feeling heard and understood, or late hospice referral.
Parikh RB, et al. JAMA Netw Open. 2025;8(2):e2458576. 10.1001/jamanetworkopen.2024.58576
Discussion & critique

A scalable implementation strategy for guideline-recommended early palliative care in community oncology, where access lags: defaults with accountable justification lifted consultation rates from 8.3% to 43.9% and cut systemic therapy near death. The 'algorithm' is a guideline-derived EHR rule rather than a learned model, quality of life and hospice timing did not change, and the trial sits within a single oncology network. Shares its behavioural-economics lineage with Manz ML nudges (SIC).