Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment

TL;DR

This paper uses causal machine learning to reveal heterogeneous effects of Medicaid on emergency department use from the Oregon experiment, identifying key groups with significant increases and drivers of utilization.

Contribution

It introduces a novel application of causal machine learning to uncover individual-level heterogeneity in Medicaid's impact on ED visits.

Findings

Heterogeneous effects range from negative to positive.

A small subgroup (14%) drives most of the increase.

Priority groups with significant ED use increases are identified.

Abstract

We provide new insights regarding the headline result that Medicaid increased emergency department (ED) use from the Oregon experiment. We find meaningful heterogeneous impacts of Medicaid on ED use using causal machine learning methods. The individualized treatment effect distribution includes a wide range of negative and positive values, suggesting the average effect masks substantial heterogeneity. A small group-about 14% of participants-in the right tail of the distribution drives the overall effect. We identify priority groups with economically significant increases in ED usage based on demographics and previous utilization. Intensive margin effects are an important driver of increases in ED utilization.