Effect of an artificial intelligence decision support tool on palliative care referral in hospitalized patients : a randomized clinical trial

Article indépendant

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

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.

http://dx.doi.org/10.1016/j.jpainsymman.2023.02.317

Voir la revue «JOURNAL OF PAIN AND SYMPTOM MANAGEMENT, 66»

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