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

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 common pitfall when applying machine learning to this problem: inflated treatment effect estimates 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 the phantom effect due to survivors bias, we controlled for this and other biases within an inherently interpretable model that quilts together linear functions using Bayesian multilevel modeling. We identified case management services as being the most impactful for reducing readmissions overall.