Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
This addresses a critical bias in healthcare analytics for preventing hospital readmissions, though it is incremental by refining existing survival analysis methods.
The study tackled the problem of inflated treatment effect estimates in postdischarge interventions due to survivor bias, using a Bayesian survival framework to correct for this and other biases, finding that case management services were most impactful for reducing 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 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.