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, particularly for patients discharged to long term care facilities, with high resource utilization in the quarter preceding admission.

利用生存分析来量化术后评估和管理服务对于防止医院再入院率和死亡率的影响，并且确定了个案管理服务对于降低再入院率影响最大，尤其是对于被送往长期护理设施并且在入院前一季度资源利用率高的患者。

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

可解释的（而不仅是事后解释的）异质性生还者偏差修正治疗效应，用于指派出院后干预措施以预防再入院