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

Importance Among patients with advanced solid malignant tumors, early specialty palliative care (PC) is guideline recommended, but strategies to increase PC access and effectiveness in community oncology are lacking. Objective To test whether algorithm-based defaults with opting out and accountable justification embedded in the electronic health record (EHR) increase completed PC visits. Design, Setting, and Participants This 2-arm cluster randomized clinical trial was conducted from November 1, 2022, to December 31, 2023. Eligible patients from 15 urban or rural clinics within a large community oncology network in Tennessee had advanced lung or noncolorectal gastrointestinal cancer and were identified by an automated EHR algorithm adapted from national guidelines. Data were analyzed between November 1, 2023, and March 4, 2024. Intervention At sites randomized to control, clinicians received weekly reports detailing PC referral rates compared with peer clinicians (peer comparison) and referred patients to PC at their discretion. At sites randomized to intervention, clinicians also received default PC orders using the EHR. Clinicians who opted out of PC consultation were asked to provide justification (accountable justification). If clinicians did not opt out, a study coordinator contacted patients to introduce and schedule PC visits using a standardized, predefined script. Main Outcomes and Measures The primary outcome was a completed PC consultation within 12 weeks of enrollment. Exploratory outcomes included quality of life, feeling heard and understood, and intensive end-of-life care. Outcomes were analyzed using clustered generalized linear and logistic regression models. Results The trial enrolled 562 patients (mean [SD] age, 68.5 [10.1] years; 288 male [51.2%]), of whom 433 (77.0%) had lung cancer. There were 130 of 296 patients (43.9%) randomized to the intervention group and 22 of 266 (8.3%) randomized to the control group who completed PC visits (adjusted odds ratio, 8.9 [95% CI, 5.5-14.6]; P < .001). Among 179 patients who died at the 24-week follow-up, 6 of 92 (6.5%) in the intervention group compared with 14 of 87 (16.1%) in the control group received systemic therapy within 14 days of death (adjusted odds ratio, 0.3 [95% CI, 0.1-0.7]; P = .05). There were no differences in quality of life, feeling heard and understood, or late hospice referral. Conclusions and Relevance In this randomized clinical trial of algorithm-based EHR defaults, the intervention increased PC consultations and decreased end-of-life systemic therapy. The intervention provides a scalable implementation strategy to increase specialty PC referrals in the community oncology setting. Trial Registration ClinicalTrials.gov Identifier: NCT05590962