Effect of Socioeconomic Status Bias on Medical Student–Patient Interactions Using an Emergency Medicine Simulation

Abstract

Objectives Implicit bias in clinical decision making has been shown to contribute to healthcare disparities and results in negative patient outcomes. Our objective was to develop a high‐fidelity simulation model for assessing the effect of socioeconomic status (SES) on medical student (MS) patient care. Methods Teams of MSs were randomly assigned to participate in a high‐fidelity simulation of acute coronary syndrome. Cases were identical with the exception of patient SES, which alternated between a low‐SES homeless man and a high‐SES executive. Students were blinded to study objectives. Cases were recorded and scored by blinded independent raters using 24 dichotomous items in the following domains: 13 communication, six information gathering, and five clinical care. In addition, quantitative data were obtained on the number of times students performed the following patient actions: acknowledged patient by name, asked about pain, generally conversed, and touching the patient. Fisher's exact test was used to test for differences between dichotomous items. For continuous measures, group differences were tested using a mixed‐effects model with a random effect for case to account for multiple observations per case. Results Fifty‐eight teams participated in an equal number of high‐ and low‐SES cases. MSs asked about pain control more often (p = 0.04) in patients of high SES. MSs touched the low‐SES patient more frequently (p = 0.01). There were no statistically significant differences in clinical care or information gathering measures. Conclusions This study demonstrates more attention to pain control in patients with higher SES as well as a trend toward better communication. Despite the differences in interpersonal behavior, quantifiable differences in clinical care were not seen. These results may be limited by sample size, and larger cohorts will be required to identify the factors that contribute to SES bias.