- Ann C Raldow: 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.
OBJECTIVES: We assessed whether proactive care management for artificial intelligence (AI)-identified at-risk patients reduced preventable emergency department (ED) visits and hospital admissions (HAs).
STUDY DESIGN: Stepped-wedge cluster randomized design.
METHODS: Adults 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.
RESULTS: In 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).
CONCLUSIONS: A 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.