Reducing readmissions in the safety net through AI and automation.

Abstract

Objectives: To implement a technology-based, systemwide readmission reduction initiative in a safety-net health system and evaluate clinical, care equity, and financial outcomes.

Study design: Retrospective interrupted time series analysis between October 2015 and January 2023.

Methods: The readmission reduction initiative standardized inpatient care for patients through a novel, electronic health record-integrated, digitally automated point-of-care decision-support tool. A predictive artificial intelligence algorithm was utilized to identify patients at the highest risk of readmission in both the inpatient and outpatient settings, allowing a population health team to perform proactive outpatient management in medical and social domains to avoid readmission.

Results: Readmission rates declined from 27.9% in the preimplementation period to 23.9% in the postimplementation period ( P  < .004) by the end of 2023. A significant gap in readmission rates between Black/African American patients and the general population was eliminated over the course of the evaluation period. Survival analysis demonstrated a reduction in all-cause mortality in the postimplementation period (HR, 0.82; 95% CI, 0.68-0.99; P  = .037). Improvement in readmission rates allowed the health system to retain $7.2 million of at-risk pay-for-performance funding.

Conclusions: This technology-based readmission reduction initiative demonstrated efficacy in reducing readmission rates, closing equity gaps, improving survival, and leading to a positive financial impact in a safety-net health system. This approach could be an effective model of technology-based, value-based care for other resource-limited health systems to meet pay-for-performance metrics and retain at-risk funding while improving clinical and equity outcomes.