Predicting preventable hospital readmissions with causal machine learning.

ABSTRACT:

Objective

To assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program).

Data sources

Electronic health records maintained by Kaiser Permanente Northern California (KPNC).

Study design

Retrospective causal forest analysis of postdischarge outcomes among KPNC inpatients. Using data from both before and after implementation, we apply causal forests to estimate individual-level treatment effects of the Transitions Program intervention on 30-day readmission. These estimates are used to characterize treatment effect heterogeneity and to assess the notional impacts of alternative targeting strategies

SUBMITTER: Marafino BJ 

PROVIDER: S-EPMC7704477 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature