Expanding care coordination in an integrated health system through causal machine learning.

Abstract

Hospital readmission is a key quality metric, yet post-discharge interventions often yield variable results. In the first large-scale randomized evaluation of causal machine learning in a health system, we assessed whether a novel model (the Predicted Benefit Intervention (PBI) score) could identify lower-risk patients most likely to benefit from post-discharge care coordination within Kaiser Permanente Northern California (KPNC). From May to December 2022, 9959 low-risk patients at 19 KPNC hospitals were randomized to usual care or the Transitions Program, which included medication reconciliation, primary care follow-up scheduling, and weekly calls for 30 days. While 30-day readmissions declined in the intervention group (7.7% vs. 8.2%), the difference was not statistically significant. However, the observed-to-expected readmission ratio declined into randomization and remained low thereafter; this decline was statistically significant. This study demonstrates the feasibility of implementing causal machine learning at scale to improve targeting and resource allocation in care delivery.