  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.
