Emotional intelligence and LEGO-based communication assessments as indicators of peer evaluations.

Much attention and effort are devoted to general surgery residency applicant interview strategies and ranking. However, few interview strategies are positively associated with applicant communication performance. The purpose of this study was to assess the potential of a LEGO-based communication assessment and a standardized patient-based emotional intelligence assessment to serve as an indicator for communication and interpersonal skills among peers in residency. We examined general surgery residents who interviewed virtually at our institution in 2021-2022, then matriculated in 2022-2023. Residents' LEGO-based communication and emotional intelligence interview assessments were evaluated for associations with peer evaluations in intern year. A regression analysis was conducted testing the association between each assessment and peer-evaluation metrics, controlling for resident track (preliminary, categorical). Performance on LEGO-based communication assessments was significantly associated with peer evaluation of cross-cover performance (B = 0.09, standard error 0.044, 95% confidence interval 0.001-0.187, P = .048), whereas emotional intelligence scores had no significant association (P = .155). In contrast, performance on the LEGO-based communication assessments were associated with peer evaluations for desirability as a coworker (B = 0.098, standard error 0.038, 95% confidence interval 0.017-0.178, P = .021), whereas emotional intelligence scores were negatively associated with desirability as a coworker (B = -0.255, standard error 0.107, 95% confidence interval -0.482 to -0.029], P = .029). Although LEGO-based communication assessments before residency are associated with better resident peer evaluations 1 year into residency, our emotional intelligence applicant assessment during recruitment did not provide clear insight into resident performance. Future research should consider the ability of standardized assessments to predict performance.

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