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Randomized Controlled Trial
. 2024 Nov;30(11):548-554.
doi: 10.37765/ajmc.2024.89625.

Proactive care management of AI-identified at-risk patients decreases preventable admissions

Ann C Raldow  1 , Naveen Raja, Chad W Villaflores, Samuel A Skootsky, Elizabeth A Jaureguy, Hanina L Rosenstein, Sarah D Meshkat, Sitaram S Vangala, Catherine A Sarkisian
Affiliations
Randomized Controlled Trial

Proactive care management of AI-identified at-risk patients decreases preventable admissions

Ann C Raldow et al. Am J Manag Care. 2024 Nov.

Abstract

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.

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Conflict of interest statement

Author Disclosures: Dr Raldow reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Figures

Figure.
Figure.
ED Visits, HAs, Potentially Preventable ED Visits, and Potentially Preventable HA Rates by Cohort and Wave ED, emergency department; HA, hospital admission.
See this image and copyright information in PMC

References

    1. Liang L, Moore B, Soni A. National inpatient hospital costs: the most expensive conditions by payer, 2017. Agency for Healthcare Research and Quality statistical brief 261. July 2020. Accessed September 7, 2022. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb261-Most-Expensive-Hos... - PubMed
    1. McDermott KW, Jiang HJ. Characteristics and costs of potentially preventable inpatient stays, 2017. Agency for Healthcare Research and Quality statistical brief 259. June 2020. Accessed September 7, 2022. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb259-Potentially-Preven... - PubMed
    1. Moore BJ, Liang L. Costs of emergency department visits in the United States, 2017. Agency for Healthcare Research and Quality statistical brief 268. December 2020. Accessed September 7, 2022. https://hcup-us.ahrq.gov/reports/statbriefs/sb268-ED-Costs-2017.jsp - PubMed
    1. Cooper Z, Scott Morton F, Shekita N. Surprise! out-of-network billing for emergency care in the United States. J Polit Econ. 2020;128(9):3626–3677. doi:10.1086/708819 - DOI
    1. [Coffey C, Greenwald J, Budnitz T, Williams MV, et al. Project BOOST Implementation Guide. Society of Hospital Medicine; 2013. Accessed September 7, 2022. https://www.hospitalmedicine.org/globalassets/professional-development/p...

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