The Effect of Medicaid Expansion on Wait Time in the Emergency Department

• 成果类型: Article
• 署名作者: Wang, Guihua
• 署名单位: University of Texas System; University of Texas Dallas
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4239
• 发表日期: 2022
• 页码: 6648-6665
• 关键词: MEDICAID EXPANSION; emergency department; Causal Inference; Machine Learning

摘要

We study the effect ofMedicaid expansion on wait time in the emergency department (ED), using a difference-in-differences approach, where the treatment group includes the states that expandedMedicaid at the beginning of 2014 and the control group includes the states that did not expandMedicaid before 2016. We first focus on the average treatment effect and find that Medicaid expansion increases ED wait time by 10.4% (or 3.5 minutes). We then develop a first-difference causal forest (FDCF) approach for heterogeneous treatment-effect analysis by incorporating the first-difference approach into the causal forest approach. Our comprehensive simulation studies show that the FDCF approach has smaller mean-squared errors than direct applications of the causal forest approach. Finally, we apply the FDCF approach to our empirical example and find that the effect of Medicaid expansion varies widely across different hospitals. Our results are useful to policymakers deciding whether to expand Medicaid and to hospital managers seeking to understand the effect of Medicaid expansion on their hospitals. Our results are also useful to researchers applying causal machine learning methods for heterogeneous treatment-effect analysis.