• 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, and Jacob Strand.
• Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery (P.M.W, J.O.E., C.B.S., G.M.S.), Rochester, Minnesota, USA. Electronic address: wilson.patrick@mayo.edu.
• J Pain Symptom Manage. 2023 Jul 1; 66 (1): 243224-32.

ContextPalliative care services are commonly provided to hospitalized patients, but accurately predicting who needs them remains a challenge.ObjectivesTo 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.MethodsThe 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.ResultsA 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.ConclusionA 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.Copyright © 2023 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

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