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Predicting preventable hospital readmissions with causal machine learning.

Ben J Marafino¹, Alejandro Schuler², Vincent X Liu^2,3

¹Biomedical Informatics Training Program, Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, USA.

Health Services Research

|October 30, 2020

Summary

This summary is machine-generated.

Causal machine learning accurately predicts preventable hospital readmissions. Targeting patients with the largest predicted effects, not just highest risk, can prevent thousands of readmissions annually.

Keywords:

clinical decision rules machine learning patient readmission risk assessment

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Area of Science:

Health Informatics
Machine Learning
Health Services Research

Background:

Hospital readmissions pose a significant burden on healthcare systems.
Predictive models are crucial for developing effective readmission prevention strategies.
The Transitions Program is an intervention aimed at reducing hospital readmissions.

Purpose of the Study:

To evaluate the feasibility and impact of using causal machine learning to predict preventable hospital readmissions.
To assess the effectiveness of the Transitions Program intervention using causal inference methods.
To explore treatment effect heterogeneity and optimize targeting strategies for readmission prevention.

Main Methods:

Retrospective analysis of electronic health records from Kaiser Permanente Northern California (KPNC).
Application of causal forest analysis to estimate individual-level treatment effects of the Transitions Program on 30-day readmissions.
Analysis of 1,539,285 hospitalizations between June 2010 and December 2018.

Main Results:

Substantial heterogeneity was observed in patient responses to the intervention (p = 2.23 × 10⁻⁷).
Predicted treatment effects were more pronounced in patients with lower predicted risk, contrary to expectations.
The model accurately estimated prevented readmissions (1246) compared to formal evaluation (1210).
Targeting patients with the largest predicted effects could prevent an estimated 4458 readmissions annually, reducing the number needed to treat.

Conclusions:

Causal machine learning is a viable tool for identifying preventable hospital readmissions when interventional data are available.
A mismatch exists between patient risk levels and their response to readmission prevention interventions.
Optimizing intervention targeting based on predicted treatment effects, rather than solely on risk, can enhance prevention outcomes.