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Problem: Recognition of the importance of clinical learning environments (CLEs) in health professions education has led to calls to evaluate and improve the quality of such learning environments. As CLEs sit at the crossroads of education and healthcare delivery, leadership from both entities should share the responsibility and accountability for this work. Current data collection about the experience and outcomes for learners, faculty, staff, and patients tends to occur in fragmented and siloed ways, and available tools to assess clinical learning environments are limited in scope. In addition, from an organizational perspective oversight of education and patient care is often done by separate entities, and not infrequently is there a sense of competing interests. Intervention: We aimed to design and pilot a holistic approach to assessment and review of CLEs and establish whether such a formative assessment process could be used to engage stakeholders from education, departmental, and health systems leadership in improvement of CLEs. Utilizing concepts of implementation science, we planned and executed a holistic assessment process for CLEs, monitored the impact of the assessment, and reflected on the process. We focused the assessment on four pillars characterizing exemplary learning environments: 1) Environment is inclusive, promotes diversity and collaboration; 2) Focus on continuous quality improvement; 3) Alignment between work and learning; and 4) Integration of education and healthcare mission. Context: At our institution, medical trainees rotate through several different health systems, but clinical and educational leadership converge at the departmental level. We therefore focused this proof-of-concept project on two large clinical departments at our institution, centering on medical learners from undergraduate and graduate medical education. For each department, a small team of champions helped create an assessment grid based on the four pillars and identified existing quantitative evaluation data sources. Champions subsequently collected qualitative data through observations, focus groups, and interviews to fill any gaps in available quantitative data. Impact: The project teams shared reports summarizing findings and recommendations with departmental, clinical, and educational leadership. Subsequent meetings with these stakeholders led to actionable plans for improvement as well as sustained structures for collaborative work between the different stakeholder groups. Lessons Learned: This project demonstrated the feasibility and effectiveness of collating, analyzing, and sharing data from various sources in engaging different stakeholder groups to initiate actionable improvement plans. Collating quantitative data from existing resources was a powerful way to demonstrate common issues in CLEs, and qualitative data provided further detail to inform improvement initiatives. Other institutions can adapt this approach to guide assessment and quality improvement of CLEs. As a next step, we are creating a comprehensive learning environment scorecard to allow for comparison of clinical learning environment quality across institutions and over time. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) in medical education has the potential to facilitate complicated tasks and improve efficiency. For example, AI could help automate assessment of written responses, or provide feedback on medical image interpretations with excellent reliability. While applications of AI in learning, instruction, and assessment are growing, further exploration is still required. There exist few conceptual or methodological guides for medical educators wishing to evaluate or engage in AI research. In this guide, we aim to: 1) describe practical considerations involved in reading and conducting studies in medical education using AI, 2) define basic terminology and 3) identify which medical education problems and data are ideally-suited for using AI. [ABSTRACT FROM AUTHOR]