Doubly-Robust Estimation of Effect of Imaging Resource Utilization on Discharge Decisions in Emergency Departments

Cluster analysis provides a data-driven multidimensional approach for identifying distinct subgroups of patients in a cohort. Each of the clusters represents a particular health condition with specific clinical trajectory and medical needs. Patients visiting emergency rooms do not share the same health condition, therefore discriminating between groups may have implications for diagnostic testing and resource utilization. We carried out this retrospective cohort study on 13825 patients who visited the emergency rooms in three Emory hospitals presenting with head trauma and non-stroke-like non-specific neurologic symptoms from January 2010 to September 2015. We utilized k-means clustering to find five distinct subgroups. Then, we investigated if getting an emergency head CT scan could have a statistically significant effect on getting discharged from the hospital. Adjusted effect estimation method was applied on each cluster to estimate the association between receiving a diagnostic test (e.g., head CT scan) on the disposition status. Out of five patient subgroups in the cohort, the chance of getting discharged for two clusters were significantly affected by getting a head CT scan. They both include comparatively older, African American or black patients who arrived in the ER with EMS, the latter suggesting critical health conditions.