In Journal of pain and symptom management ; h5-index 51.0
CONTEXT : Palliative care services are commonly provided to hospitalized patients, but accurately predicting who needs them remains a clinical challenge.
OBJECTIVE : 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 nursing unit.
RESULTS : A total of 3183 patient hospitalizations were enrolled in the trial. 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 decision support tool 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.
CONCLUSIONS : A clinical decision support tool integrated into a palliative care practice and leveraging an AI/ML algorithm demonstrated an increase in the rate of speciality palliative care consultation among hospitalized patients and reductions in hospitalizations.
Wilson Patrick M, Ramar Priya, Philpot Lindsey M, Soleimani Jalal, Ebbert Jon O, Storlie Curtis B, Morgan Alisha A, Schaeferle Gavin M, Asai Shusaku W, Herasevich Vitaly, Pickering Brian W, Tiong Ing C, Olson Emily A, Karow Jordan C, Pinevich Yuliya, Strand Jacob
2023-Feb-24
Artificial Intelligence (AI), EHR, Inpatient Palliative care, Machine Learning (ML), Pragmatic Clinical Trials
