Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions

TL;DR

This paper introduces an interpretable Bayesian survival analysis method to accurately estimate the effects of postdischarge interventions on preventing readmissions, correcting for survivor bias and heterogeneity.

Contribution

It presents a novel bias-corrected survival analysis approach that is interpretable and accounts for heterogeneity in treatment effects, improving causal inference in healthcare.

Findings

Case management services significantly reduce readmissions.

The method corrects for survivor bias in treatment effect estimation.

Interpretable model facilitates understanding of heterogeneous effects.

Abstract

We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death. Our approach avoids a specific pitfall of applying machine learning to this problem, which is an inflated estimate of the effect of interventions, due to survivors bias -- where the magnitude of inflation may be conditional on heterogeneous confounders in the population. This bias arises simply because in order to receive an intervention after discharge, a person must not have been readmitted in the intervening period. After deriving an expression for this phantom effect, we controlled for this and other biases within an inherently interpretable Bayesian survival framework. We identified case management services as being the most impactful for reducing readmissions overall.