Despite numerous studies showing the benefits of integrating palliative care in both the early- and advanced-stage cancer settings,1 palliative care remains underutilized for most patients with cancer. A recent study by the American Cancer Society found that only 10% of Medicare beneficiaries with late-stage cancer received early palliative care in 2019. In addition, the study found that differences between provider and institutional-level factors accounted for between 30% and 50% of the variation in early palliative care billing, suggesting that where patients receive care may be important drivers in determining whether they receive guideline-recommended palliative care.2
Both telemedicine palliative care and algorithm-based palliative care triage are scalable strategies to ensure that the highest-risk patients are seen the soonest.— RAVI B. PARIKH, MD, MPP, FACP
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To provide oncology clinicians with evidence-based recommendations on when to integrate palliative care into standard oncology care, ASCO updated its palliative care guidelines in 2024. The revised guidelines call for early referral of patients with advanced solid tumors and hematologic malignancies to specialized interdisciplinary palliative care teams in tandem with active cancer treatment.3
Embedding an algorithm-driven referral system into electronic health records appears to be an effective and scalable strategy to increase palliative care access for patients with advanced cancer, and it may be a solution to decreasing intensive end-of-life systemic care, according to the results of the BE-a-PAL randomized clinical trial. The study was conducted by Ravi B. Parikh, MD, MPP, FACP, Associate Professor in the Department of Hematology and Medical Oncology at Emory University School of Medicine and Medical Director of the Winship Data and Technology Applications Shared Resource at Winship Cancer Institute of Emory University, and colleagues. They investigated whether algorithm-based default specialty palliative care orders with an opt out feature and accountable justification, which requires clinicians to document their reasons for deviating from standard clinical practice, increase palliative care consultations and improve outcomes in patients with cancer in the outpatient setting.
The two-arm randomized clinical trial compared usual care with an algorithm-based default palliative care intervention among 562 adults with stage III or IV small cell or non–small cell lung cancer and non-colorectal gastrointestinal malignant tumors. The study was conducted across 15 community clinics in the Tennessee Oncology network.
The results from the study showed a substantial increase in palliative care access among patients in the intervention arm, from 8.3% in typical care settings to 43.9% with the algorithm-based referral system. In addition, the intervention decreased end-of-life systemic chemotherapy in the last 14 days of life from 16.1% to 6.5% compared with usual care. However, there were no differences in improvements in patients’ quality of life, their feeling heard and understood, or late hospice referral between the intervention and control groups.4
The ASCO Post talked with Dr. Parikh about how an algorithm-based palliative care referral system may help to broaden access to specialized palliative care services for patients with cancer and how federal budget cuts to Medicaid and other health-care programs are likely to impede access to palliative care as well as to other types of cancer care.
Improving Access to Palliative Care
What conclusion did you draw from the results of your study, that patients will accept palliative care services if they are offered?
Yes, that is among the big takeaways. The context for this study is that specialty palliative care for patients with advanced cancer, particularly advanced gastrointestinal and lung cancers, is an evidence-based practice recommended by national guidelines.
Ideally, in a world with unlimited resources, everyone with those cancers would get palliative care. And, ideally, they would receive that care early in their diagnosis, because early palliative care has been associated with a wide range of improved outcomes, including improved quality of life, improved moods, improved patient-reported experiences and symptoms, reductions in hospitalizations near the end of life, and potentially improved survival. This is not new information provided by our study.
However, the problem we are trying to solve is that in real-world practice, particularly in the oncology community setting, we are faced with capacity constraints with palliative care services and the persistent perception that palliative care is appropriate only near the end of life, despite the fact that studies show that’s not true.
The innovation of our study was multifactorial. First, it was a behavioral economic intervention. We were trying to counter implicit cognitive biases among oncologists by flagging the appropriateness of instituting specialty palliative care early in the disease course. Second, the clinical trial was an effectiveness study, not an efficacy study. It was mainly intended to test the effectiveness of an implementation strategy of an algorithm-driven identification of high-risk patients and then a default referral to palliative care in a community-based setting. And the results showed a four- to five-time increase in the rates of palliative care referrals, so we achieved our primary success metric in getting patients to palliative care earlier in their disease course.
Understanding the Benefits of Palliative Care
Why do you think the patients in the intervention arm did not benefit more from palliative care services, especially in improved quality of life?
I regard this finding as more of an issue with the measurement and response rate as opposed to a true finding. It’s been well established through previous highly controlled studies that early specialty palliative care improves reported quality of life through a variety of metrics, including the ones we used as part of this study.
The challenge of running large, pragmatic clinical trials of palliative care interventions in real-world settings is that you often don’t get enough high response rates to survey, and you don’t see as big a quality-of-life benefit, because patients are receiving different levels of palliative care at different time points in their disease course. We measured quality of life as an exploratory outcome in our study, not expecting to see a huge effect, because there are a variety of confounders in the real world that might explain why a quality-of-life benefit was not manifested in our study.
The finding does show that when we deploy interventions tested in highly controlled randomized trials, we need to explore ways of dosing palliative care or integrating it earlier vs later in the disease course to recapture the quality-of-life benefit that we have seen in other trials.
Communicating the Benefits of Palliative Care
What are the challenges of providing palliative care for patients with late-stage disease? Might patients conflate palliative care with hospice care and, therefore, be hesitant to accept palliative care referrals?
Our study provides good evidence suggesting that when palliative care is presented to patients in a clear and supportive manner, they are more likely to agree to palliative care services. For example, part of the success of our intervention was due to the fact that a trained palliative care nurse coordinator introduced palliative care in a standardized way, messaging it as care meant to be an extra layer of support concurrent with their therapy, not as appropriate care for patients only at the end of life, as many oncologists often frame it.
When we presented palliative care in that framework, well over 60% of patients agreed to a palliative care consult. So, I don’t think the barrier is with patients; it’s with clinicians presenting palliative care too late in the disease course and sometimes underselling the true benefit of the service.
Reducing Disparities in Access to Palliative Care
Although palliative care use has increased over time in the United States, it remains low for Black, Asian or Pacific Islander, and Hispanic patients compared with White patients, according to a study of patients with metastatic breast cancer.5 How can algorithm-based palliative care referral interventions help broaden access to palliative care services to more patients, especially minority patients with both early- and late-stage cancers?
We’ve shown both in our study and in previous algorithm-based supportive care intervention studies that integrating algorithmic identification of patients with cancer within standard cancer care can reduce disparities in receiving palliative care or other supportive care interventions.
Guest Editor
Janet L. Abrahm, MD, FACP, FAAHPM, FASCO
Dr. Abrahm is Professor of Medicine at Harvard Medical School and former Chief of the Division of Adult Palliative Care, Department of Supportive Oncology, Dana-Farber Cancer Institute, and Division of Palliative Medicine, Brigham and Women’s Hospital. Palliative Care in Oncology addresses the evolving needs of cancer survivors at various stages of their illness.
As a medical community, we have a variety of cognitive biases, some unconscious, including which patients may be appropriate for palliative care intervention. For example, there is a perception that Black and Hispanic patients may have cultural-related barriers that may conflict with the goals of palliative care as part of their standard cancer care or that non–English speaking patients may not understand the concept of palliative care.
Using an algorithm-based referral system embedded into patients’ electronic health records eliminates those biases, because it identifies eligible patients for palliative care solely based on clinical need. Despite the fact that algorithm-based medical platforms are not perfect, they can oftentimes counter some of these built-in biases in the health-care community.
Overcoming Oncology Workforce Shortages
Was it difficult for patients in your study to be connected with a palliative care provider? How might technology-driven interventions help to overcome oncology workforce shortages?
In our study, most patients were seen by a specialty palliative care clinician within 2 weeks of being flagged by the algorithm for referral. The clinics in our study readily embraced telemedicine-based palliative care, so they weren’t constrained to seeing patients only in a clinic visit, which was convenient for the patients as well, and that helped to improve access to palliative care.
The other factor that helped improve access was that using algorithm-based referral strategies often helps to triage patients for care. For example, the highest-risk patients identified by our algorithm in our study were seen quickly by a palliative care specialist, even at the expense potentially of a lower-risk patient who may not have as urgent a need.
Both telemedicine palliative care and algorithm-based palliative care triage are scalable strategies to ensure that the highest-risk patients are seen the soonest. The telemedicine strategy may also help to reduce the need for patients to see palliative care physicians and oncologists in the clinic, saving patients travel time and money as well streamlining workflows to use clinicians’ time more efficiently.
Impeding Access to Care
How might the 2025 federal budget reconciliation bill recently signed into law, which includes major cuts to the Medicaid program,6 likely reduce patients’ ability to access palliative care as well as other cancer care services?
A lot of what we are proposing based on the results of our study is predicated on patients’ having adequate health-care coverage for palliative care services. Federal cuts to Medicaid and other health-care programs threaten patients’ ability to access that care as well as other types of cancer care.
DISCLOSURE: Funding for this clinical trial was provided by the Emerson Collective. Dr. Parikh reported no conflicts of interest.
REFERENCES
1. Temel JS, Greer JA, Muzikansky A, et al: Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med 363:733-742, 2010.
2. Hu X, Kwon Y, Jiang C, et al: Trend and provider- and organizational-level factors associated with early palliative care billing among patients diagnosed with distant-stage cancers in 2010–2019 in the United States. J Clin Oncol 43:1789-1799, 2025.
3. Sanders JJ, Temin S, Ghoshal A, et al: Palliative care for patients with cancer: ASCO guideline update. J Clin Oncol 42:2336-2357, 2024.
4. Parikh RB, Ferrell WJ, Li Y, et al: Algorithm-based palliative care in patients with cancer: A cluster randomized clinical trial. JAMA Netw Open 8:e2458576, 2025.
5. Freeman JQ, Omoleye OJ, Zhao F, et al: Trends and racial/ethnic disparities in palliative care use among patients with de novo metastatic breast cancer—National Cancer Database, 2004–2020. 2023 AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved. Abstract B011. Presented September 29, 2023.
6. KFF: Health provisions in the 2025 Federal Budget Reconciliation Law. August 22, 2025. Available at www.kff.org/medicaid/health-provisions-in-the-2025-federal-budget-reconciliation-law/#:~:text=Health%20Savings%20Accounts-,Overview,House%20and%20Senate%20passed%20bills. Accessed September 24, 2025.
