Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Randomized Controlled Trial
. 2023 Jul;66(1):24-32.
doi: 10.1016/j.jpainsymman.2023.02.317. Epub 2023 Feb 24.

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

Patrick M Wilson  1 , Priya Ramar  2 , Lindsey M Philpot  2 , Jalal Soleimani  3 , Jon O Ebbert  4 , Curtis B Storlie  5 , Alisha A Morgan  6 , Gavin M Schaeferle  7 , Shusaku W Asai  8 , Vitaly Herasevich  3 , Brian W Pickering  3 , Ing C Tiong  9 , Emily A Olson  6 , Jordan C Karow  6 , Yuliya Pinevich  3 , Jacob Strand  6
Affiliations
Randomized Controlled Trial

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

Patrick M Wilson et al. J Pain Symptom Manage. 2023 Jul.

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.

PubMed Disclaimer

Publication types

LinkOut - more resources