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

doi:10.1016/j.jpainsymman.2023.02.317

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

J Pain Symptom Manage. 2023 Jul;66(1):24-32. doi: 10.1016/j.jpainsymman.2023.02.317. Epub 2023 Feb 24.

Authors

Patrick M Wilson¹, Priya Ramar², Lindsey M Philpot², Jalal Soleimani³, Jon O Ebbert⁴, Curtis B Storlie⁵, Alisha A Morgan⁶, Gavin M Schaeferle⁷, Shusaku W Asai⁸, Vitaly Herasevich³, Brian W Pickering³, Ing C Tiong⁹, Emily A Olson⁶, Jordan C Karow⁶, Yuliya Pinevich³, Jacob Strand⁶

Affiliations

¹ 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.
² Department of Medicine (P.R., L.M.P.), Mayo Clinic, Rochester, Minnesota USA.
³ Department of Anesthesiology (J.S., V.H., B.W.P., Y.P.), Mayo Clinic, Rochester, Minnesota USA.
⁴ 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; Division of Community Internal Medicine (J.O.E., A.A.M. E.A.O., J.C.K., J.S.), Geriatrics and Palliative Care Mayo Clinic, Rochester, Minnesota, USA.
⁵ 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; Department of Health Sciences Research (C.B.S.), Mayo Clinic, Rochester, Minnesota, USA.
⁶ Division of Community Internal Medicine (J.O.E., A.A.M. E.A.O., J.C.K., J.S.), Geriatrics and Palliative Care Mayo Clinic, Rochester, Minnesota, USA.
⁷ 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.
⁸ Health Analytics | Global Health and Wellbeing (S.W.A.), Delta Air Lines, Atlanta, Georgia, USA.
⁹ Department of Information Technology (I.C.T.), Mayo Clinic, Rochester, Minnesota, USA.

PMID: 36842541
DOI: 10.1016/j.jpainsymman.2023.02.317

Abstract

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.

Keywords: Artificial intelligence (AI); EHR; Inpatient palliative care; Machine learning (ML); Pragmatic clinical trials.

Publication types

Randomized Controlled Trial

MeSH terms

Artificial Intelligence*
Hospitalization
Humans
Palliative Care*
Patient Readmission
Referral and Consultation