Leveraging Electronic Health Record Data and Measuring Interdependence in the Era of Precision Education and Assessment.

Purpose: The era of precision education is increasingly leveraging electronic health record (EHR) data to assess residents' clinical performance. But precision in what the EHR-based resident performance metrics are truly assessing is not fully understood. For instance, there is limited understanding of how EHR-based measures account for the influence of the team on an individual's performance-or conversely how an individual contributes to team performances. This study aims to elaborate on how the theoretical understandings of supportive and collaborative interdependence are captured in residents' EHR-based metrics.
Method: Using a mixed methods study design, the authors conducted a secondary analysis of 5 existing quantitative and qualitative datasets used in previous EHR studies to investigate how aspects of interdependence shape the ways that team-based care is provided to patients.
Results: Quantitative analyses of 16 EHR-based metrics found variability in faculty and resident performance (both between and within resident). Qualitative analyses revealed that faculty lack awareness of their own EHR-based performance metrics, which limits their ability to act interdependently with residents in an evidence-informed fashion. The lens of interdependence elucidates how resident practice patterns develop across residency training, shifting from supportive to collaborative interdependence over time. Joint displays merging the quantitative and qualitative analyses showed that residents are aware of variability in faculty's practice patterns and that viewing resident EHR-based measures without accounting for the interdependence of residents with faculty is problematic, particularly within the framework of precision education.
Conclusions: To prepare for this new paradigm of precision education, educators need to develop and evaluate theoretically robust models that measure interdependence in EHR-based metrics, affording more nuanced interpretation of such metrics when assessing residents throughout training.
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