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 - Publisher Copyright:
© 2023 American Academy of Hospice and Palliative Medicine
PY - 2023/7
Y1 - 2023/7
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
VL - 66
SP - 24
EP - 32
JO - Journal of pain and symptom management
JF - Journal of pain and symptom management
IS - 1
ER -