Intervention Increases Palliative Cancer Care Consultations/Lowers EOL Care

An algorithm-based palliative care intervention provides a scalable implementation strategy to increase referrals in the community oncology setting.

A palliative care (PC) intervention combining algorithm-based automated identification of patients eligible for PC led to an increase in PC visits and a decrease in end-of-life systemic therapy among patients with cancer, according to a randomized clinical study (NCT05590962) published in JAMA Network Open.1

Efficacy data from the trial revealed that unadjusted rates of PC visits in the modified intention-to-treat population intervention arm (n = 296) were 43.9% vs 8.3% of patients in the control arm (n = 266). Adjusted analyses showed the intervention was associated with a statistically significant increase in completed PC visits (adjusted odds ratio [AOR], 8.9; 95% CI, 5.5-14.6; P <.001).

In those undergoing PC visits during the study period, the median number of PC visits in the intervention arm was 2.9 (range, 1.0-6.0) vs 3.4 (range, 1.0-7.0) in the control arm. Additionally, the percentage of patients who completed more than 1 PC visit in respective arms was 84.2% vs 89.3%. Furthermore, heterogeneity analyses revealed a disproportionate effect observed in patients with lung vs non-colorectal gastrointestinal (GI) malignant tumors with the intervention (AOR, 14.1; 95% CI, 7.1-27.9 vs 4.2; 95% CI, 1.8-9.9; P =.03).

Of 562 patients on trial, 179 died (31.9%) as of the data cutoff date of March 4, 2024, including 92 (31.1%) in the intervention group and 87 (32.7%) in the control group. Additionally, systemic therapy rates within 2 weeks of death were 6.5% and 16.1% of the respective groups (AOR, 0.3; 95% CI, 0.1-0.7; P =.05). Furthermore, no significant difference in late hospice referrals were observed between groups; 9.8% vs 13.8%, respectively (AOR, 0.6; 95% CI, 0.3-1.1; P =.36).

“In this cluster randomized clinical trial among patients with advanced solid malignant tumors in a community oncology network, algorithm-based default PC referrals with accountable justification increased completed PC visits from 8.3% to 43.9% and decreased end-of-life systemic therapy from 16.1% to 6.5%,” Ravi B. Parikh, MD, MPP, FACP, associate professor in the department of Hematology and Medical Oncology at the Emory University School of Medicine and medical director of the Winship Data and Technology Applications Shared Resource at Winship Cancer Institute of Emory University, wrote in the publication with study coinvestigators.1 “No changes in late hospice referral, quality of life [QOL], or feeling heard and understood were observed. Prior efficacy trials in oncology have tested early PC in controlled, primarily academic settings.2 To our knowledge, this is the first effectiveness randomized clinical trial of algorithm-driven default specialty PC in community oncology.”

Investigators of the randomized cluster trial compared an algorithm-based default PC intervention with standard of care among enrolled patients with advanced lung and non-colorectal GI tumors between November 1, 2022 and December 31, 2023. The trial was conducted across 15 community medical oncology clinics using the same electronic health records (EHR) with access to an on-site or virtual specialty PC clinician.

Patients eligible for enrollment were 18 years and older, actively receiving or had received care at an eligible clinic, and were eligible for PC as determined by an automated EHR algorithm. The EHR algorithm assigned scores to each patient based on the number of PC risk factors they met. It then assigned a weighted score that reflected an individual’s need for PC consultation.

In the intervention arm, oncologists and advance practice clinicians received automated notifications in their EHR inbox explaining the rationale for PC referral and offering an opportunity to opt out. If the clinician did not opt out after 48 hours, a nurse PC coordinator phoned the patient and offered a PC visit using a predefined script. If the patient agreed, they were scheduled, and the urgency of the consultation was triaged based on algorithm score.

Patients on trial had a mean age of 68.5 years (SD, 10.1), 48.8% were female, and 79.5% were White. A total of 77.0% of patients had lung cancer; the median risk score was 2.0 (range, 1.0-20.0) and 2.0 (range, 1.0-18.0) in the intervention and control arms, respectively; and 89.5% (n = 265) were not opted out of PC by their physician. Of the 265 who were not opted out, 166 (62.6%) agreed to PC.

The primary end point of the study was PC visit completion. Exploratory end points included patient QOL and markers of intensive end-of-life care.

References

1. Parikh RB, Ferrell WJ, Li Y, et al. Algorithm-based palliative care in patients with cancer: a cluster randomized clinical trial. JAMA Netw Open. 2025;8(2):e2458576. doi:10.1001/jamanetworkopen.2024.58576
2. Temel JS, Greer JA, Muzikansky A, et al. Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med. 2010;363(8):733-742. doi:10.1056/NEJMoa1000678