• Ann C Raldow, Naveen Raja, Chad W Villaflores, Samuel A Skootsky, Elizabeth A Jaureguy, Hanina L Rosenstein, Sarah D Meshkat, Sitaram S Vangala, and Catherine A Sarkisian.
• Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, 200 Medical Plaza, Ste B-265, Los Angeles, CA 90095. Email: araldow@mednet.ucla.edu.
• Am J Manag Care. 2024 Nov 1; 30 (11): 548554548-554.

ObjectivesWe assessed whether proactive care management for artificial intelligence (AI)-identified at-risk patients reduced preventable emergency department (ED) visits and hospital admissions (HAs).Study DesignStepped-wedge cluster randomized design.MethodsAdults receiving primary care at 48 UCLA Health clinics and determined to be at risk based on a homegrown AI model were included. We employed a stepped-wedge cluster randomized design, assigning groups of clinics (pods) to 1 of 4 single-cohort waves during which the proactive care intervention was implemented. The primary end points were potentially preventable HAs and ED visits; secondary end points were all HAs and ED visits. Within each wave, we used an interrupted time series and segmented regression analysis to compare utilization trends.ResultsIn the pooled analysis of high-risk and highest-risk patients (n = 3007), potentially preventable HAs showed a statistically significant level drop (-27% [95% CI, -44% to -6%]), without any corresponding change in trends. Potentially preventable ED visits did not show a substantial level drop in response to the intervention, although a nonsignificant differential change in trend was observed, with visit rates decelerating 7% faster in the intervention cohorts (95% CI, -13% to 0%). Nonsignificant drops were observed for all HAs (-19% [95% CI, -35% to 1%]; P = .06) and ED visits (-15% [95% CI, -28% to 1%]; P = .06).ConclusionsA care management intervention targeting AI-identified at-risk patients was followed by a onetime, significant, sizable reduction in preventable HA rates. Further exploration is needed to assess the potential of integrating AI and care management in preventing acute hospital encounters.

16 keywords...

hide…