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March 28, 2026Academic Medicine0 citations

Leveraging Electronic Health Record Data and Artificial Intelligence to Develop a Crosswalk Tool for Personalized Clinical Experience Profiles of Emergency Medicine Residents

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NGNicholas GenesCGChristian GraultyJKJulie Kim

Key Points

  • The research aims to create a tool that utilizes AI to personalize feedback for emergency medicine residents based on their clinical experiences.
  • Utilized AI to automate mappings of clinical data.
  • Expanded data inputs beyond just diagnoses for improved mapping accuracy.
  • Engaged stakeholders in reviewing the validity of the mapping process.
  • Assessed the model's generalizability to other medical specialties.
  • Developed mechanisms for feedback loops to enhance educational outcomes.
  • The AI tool has the potential to provide more precise and personalized feedback.
  • Initial iterations show improvement in engagement from stakeholders.
  • Mapping accuracy is enhanced with expanded inputs, suggesting effectiveness for various specialties.

Abstract

Planned work includes iterating AI-automated mappings by expanding inputs beyond diagnoses, engaging wider stakeholder review of mapping validations, and assessing generalizability to other specialties' content outlines to produce a scalable and reproducible model to increase the precision of feedback loops to inform graduate medical education, the clinical learning environment, and training design.

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Cite This Study

Genes et al. (2026) studied this question.

synapsesocial.com/papers/69c772158bbfbc51511e25c4https://doi.org/10.1093/acamed/wvag082
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