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Default EHR Orders to Increase Palliative Care Referrals In a Large Community Oncology Network
- Augmented & Artificial Intelligence
This project leverages algorithm-based triggers to increase early outpatient palliative care referrals for patients with advanced cancer. Researchers hypothesize that palliative care referrals will increase, which can lead to improved quality of life through pain relief, symptom management, and psychosocial support.
For patients with advanced cancer, palliative care can holistically improve their quality of life through pain relief, symptom management, and psychosocial support. Palliative care also provides support to families and caregivers. Despite the known benefits of early palliative care, nearly two-thirds of patients with advanced cancers do not receive it. Instead, 40% of patients with advanced cancers receive aggressive end-of-life care, such as chemotherapy within the last few days of life.
Why are referrals so low when the evidence demonstrates that early palliative care paired with cancer-directed treatment improves patients’ quality of life and reduces symptom burden? Unfortunately, oncologists tend to overestimate prognosis for patients with advanced cancer at an alarming 70%, delaying or even forgoing palliative care. Furthermore, specialty palliative care is not yet widely available at community oncology practices, where the majority of patients receive their primary oncologic care.
This project leverages an innovative partnership between PC3I and Tennessee Oncology, one of the largest U.S. community-based practices and a member of OneOncology, a network of community practices nationwide. The partnership brings together multidisciplinary expertise across PC3I and Tennessee Oncology. Building on advances in electronic health record (EHR) infrastructure and predictive analytics, the study leverages algorithm-based triggers to increase early outpatient palliative care referrals for patients with advanced cancer. At four Tennessee Oncology practices, a behavioral intervention built into the EHR will detect patients with high need and create a default order to palliative care, which clinicians have the option to remove.
This study evaluates a scalable and adaptable approach to increasing early palliative care utilization at a time when demand for palliative care in the U.S. is only rising while the supply at community oncology practices remains limited. The team hypothesizes that the intervention will increase palliative care visits by 10% and decrease aggressive end-of-life utilization by 15% as compared to usual practice.
Project Leads
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Ravi Parikh
MD, MPP, FACP
Associate Director, PC3I & Director, Program in Augmented and Artificial Intelligence, PC3I
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Project Team
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Stephen Schliecher
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Sandhya K. Mudumbi