Background

The adoption of predictive algorithms in healthcare comes with the potential for algorithmic bias, which could exacerbate existing disparities. Fairness metrics were proposed to measure algorithmic bias, but the application to real-world tasks is limited.

Objective

This study aims to evaluate the algorithmic bias between racial and income groups associated with the application of common 30-day hospital readmission models and assesses the usefulness and interpretability of selected fairness metrics.

 

 

Citation

Wang HE, Weiner JP, Saria S, Kharrazi H. Algorithmic bias evaluation in 30-day hospital readmission models: A retrospective analysis of hospital discharges. Journal of Medical Internet Research. 2024 Feb. DOI: 10.2196/47125. PMID: 38422347.