Reducing readmissions in the safety net through AI and automation

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

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

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.

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.

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.