Effect of an Artificial Intelligence Decision Support Tool on Palliative Care Referral in Hospitalized Patients: A Randomized Clinical Trial

Patrick M. Wilson, Priya Ramar, Lindsey M. Philpot, Jalal Soleimani, Jon O. Ebbert, Curtis B. Storlie, Alisha A. Morgan, Gavin M. Schaeferle, Shusaku W. Asai, Vitaly Herasevich, Brian W. Pickering, Ing C. Tiong, Emily A. Olson, Jordan C. Karow, Yuliya Pinevich, Jacob Strand

Research output: Contribution to journalArticlepeer-review


Context: Palliative care services are commonly provided to hospitalized patients, but accurately predicting who needs them remains a challenge. Objectives: To assess the effectiveness on clinical outcomes of an artificial intelligence (AI)/machine learning (ML) decision support tool for predicting patient need for palliative care services in the hospital. Methods: The study design was a pragmatic, cluster-randomized, stepped-wedge clinical trial in 12 nursing units at two hospitals over a 15-month period between August 19, 2019, and November 17, 2020. Eligible patients were randomly assigned to either a medical service consultation recommendation triggered by an AI/ML tool predicting the need for palliative care services or usual care. The primary outcome was palliative care consultation note. Secondary outcomes included: hospital readmissions, length of stay, transfer to intensive care and palliative care consultation note by unit. Results: A total of 3183 patient hospitalizations were enrolled. Of eligible patients, A total of 2544 patients were randomized to the decision support tool (1212; 48%) and usual care (1332; 52%). Of these, 1717 patients (67%) were retained for analyses. Patients randomized to the intervention had a statistically significant higher incidence rate of palliative care consultation compared to the control group (IRR, 1.44 [95% CI, 1.11–1.92]). Exploratory evidence suggested that the decision support tool group reduced 60-day and 90-day hospital readmissions (OR, 0.75 [95% CI, 0.57, 0.97]) and (OR, 0.72 [95% CI, 0.55–0.93]) respectively. Conclusion: A decision support tool integrated into palliative care practice and leveraging AI/ML demonstrated an increased palliative care consultation rate among hospitalized patients and reductions in hospitalizations.

Original languageEnglish (US)
Pages (from-to)24-32
Number of pages9
JournalJournal of pain and symptom management
Issue number1
StatePublished - Jul 2023


  • Artificial intelligence (AI)
  • EHR
  • Inpatient palliative care
  • Machine learning (ML)
  • Pragmatic clinical trials

ASJC Scopus subject areas

  • General Nursing
  • Clinical Neurology
  • Anesthesiology and Pain Medicine


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