Applying the master adaptive learner framework to just-in-time training of procedures.

Background: Just-in-time training (JITT) occurs in the clinical context when learners need immediate guidance for procedures due to a lack of proficiency or the need for knowledge refreshment. The master adaptive learner (MAL) framework presents a comprehensive model of transforming learners into adaptive experts, proficient not only in their current tasks but also in the ongoing development of lifelong skills. With the evolving landscape of procedural competence in emergency medicine (EM), trainees must develop the capacity to acquire and master new techniques consistently. This concept paper will discuss using JITT to support the development of MALs in the emergency department. Methods: In May 2023, an expert panel from the Society for Academic Emergency Medicine (SAEM) Medical Educator's Boot Camp delivered a comprehensive half-day preconference session entitled "Be the Best Teacher" at the society's annual meeting. A subgroup within this panel focused on applying the MAL framework to JITT. This subgroup collaboratively developed a practical guide that underwent iterative review and refinement. Results: The MAL-JITT framework integrates the learner's past experiences with the educator's proficiency, allowing the educational experience to address the unique requirements of each case. We outline a structured five-step process for applying JITT, utilizing the lumbar puncture procedure as an example of integrating the MAL stages of planning, learning, assessing, and adjusting. This innovative approach facilitates prompt procedural competence and cultivates a positive learning environment that fosters acquiring adaptable learning skills with enduring benefits throughout the learner's career trajectory. Conclusions: JITT for procedures holds the potential to cultivate a dynamic learning environment conducive to nurturing the development of MALs in EM.

Correction: Clinical reasoning education in the clerkship years: A cross-disciplinary national needs assessment.

[This corrects the article DOI: 10.1371/journal.pone.0273250.].

IFEM model curriculum: emergency medicine learning outcomes for undergraduate medical education.

BACKGROUND: The International Federation for Emergency Medicine (IFEM) published its model curriculum for medical student education in emergency medicine in 2009. Because of the evolving principles of emergency medicine and medical education, driven by societal, professional, and educational developments, there was a need for an update on IFEM recommendations. The main objective of the update process was creating Intended Learning Outcomes (ILOs) and providing tier-based recommendations. METHOD: A consensus methodology combining nominal group and modified Delphi methods was used. The nominal group had 15 members representing eight countries in six regions. The process began with a review of the 2009 curriculum by IFEM Core Curriculum and Education Committee (CCEC) members, followed by a three-phase update process involving survey creation [The final survey document included 55 items in 4 sections, namely, participant & context information (16 items), intended learning outcomes (6 items), principles unique to emergency medicine (20 items), and content unique to emergency medicine (13 items)], participant selection from IFEM member countries and survey implementation, and data analysis to create the recommendations. RESULTS: Out of 112 invitees (CCEC members and IFEM member country nominees), 57 (50.9%) participants from 27 countries participated. Eighteen (31.6%) participants were from LMICs, while 39 (68.4%) were from HICs. Forty-four (77.2%) participants have been involved with medical students' emergency medicine training for more than five years in their careers, and 56 (98.2%) have been involved with medical students' training in the last five years. Thirty-five (61.4%) participants have completed a form of training in medical education. The exercise resulted in the formulation of tiered ILO recommendations. Tier 1 ILOs are recommended for all medical schools, Tier 2 ILOs are recommended for medical schools based on perceived local healthcare system needs and/or adequate resources, and Tier 3 ILOs should be considered for medical schools based on perceived local healthcare system needs and/or adequate resources. CONCLUSION: The updated IFEM ILO recommendations are designed to be applicable across diverse educational and healthcare settings. These recommendations aim to provide a clear framework for medical schools to prepare graduates with essential emergency care capabilities immediately after completing medical school. The successful distribution and implementation of these recommendations hinge on support from faculty and administrators, ensuring that future healthcare professionals are well-prepared for emergency medical care.

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology.

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.