TY - JOUR
T1 - Effect of an Artificial Intelligence Decision Support Tool on Palliative Care Referral in Hospitalized Patients
T2 - A Randomized Clinical Trial
AU - Wilson, Patrick M.
AU - Ramar, Priya
AU - Philpot, Lindsey M.
AU - Soleimani, Jalal
AU - Ebbert, Jon O.
AU - Storlie, Curtis B.
AU - Morgan, Alisha A.
AU - Schaeferle, Gavin M.
AU - Asai, Shusaku W.
AU - Herasevich, Vitaly
AU - Pickering, Brian W.
AU - Tiong, Ing C.
AU - Olson, Emily A.
AU - Karow, Jordan C.
AU - Pinevich, Yuliya
AU - Strand, Jacob
N1 - Funding Information:
This study is funded/sponsored by the Kern Center for the Science of Health Care Delivery and the Clinical Practice Committee (CPC) at Mayo Clinic. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Kern Center or the CPC.
Publisher Copyright:
© 2023 American Academy of Hospice and Palliative Medicine
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Artificial intelligence (AI)
KW - EHR
KW - Inpatient palliative care
KW - Machine learning (ML)
KW - Pragmatic clinical trials
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U2 - 10.1016/j.jpainsymman.2023.02.317
DO - 10.1016/j.jpainsymman.2023.02.317
M3 - Article
C2 - 36842541
AN - SCOPUS:85150772753
SN - 0885-3924
JO - Journal of Pain and Symptom Management
JF - Journal of Pain and Symptom Management
ER -