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.

翻译：我们采用生存分析量化了出院后评估与管理服务在预防医院再入院或死亡中的效果。我们的方法避免了将机器学习应用于该问题时的特定陷阱——即因幸存者偏差导致的对干预效果的膨胀估计，且这种膨胀幅度可能取决于人群中的异质性混杂因素。该偏差的产生原因很简单：患者必须在出院后的随访期间内未发生再入院，才有机会接受干预措施。在推导出这种虚假效应的数学表达式后，我们将其与其他偏差纳入一个本质可解释的贝叶斯生存分析框架中进行控制。最终，我们识别出个案管理服务在整体上对降低再入院率最为有效。