Spend :01 of your time each Monday morning as Twelve:01 delivers timely tools, trends, strategies, and/or compliance insights for the CME/CE enterprise.
The U.S. Department of Labor’s Employment and Training Administration has released a voluntary AI Literacy Framework to guide nationwide workforce and education efforts in building foundational AI literacy skills. It outlines five core content areas, from understanding AI principles to evaluating and using AI responsibly, and seven delivery principles to shape effective training programs. Designed for flexibility across industries and roles, it supports workers, students, educators, and employers in navigating an increasingly AI-driven economy. For CME/CE professionals, this initiative aims to prepare healthcare professionals and other learners to apply, evaluate, and oversee AI tools safely and effectively in clinical and administrative settings.
Effective learning objectives are the backbone of accredited CME/CE, translating identified practice gaps into clear statements of what clinicians should be able to do after an activity. In a recent Alliance Almanac article, Anatomy of a Learning Objective: Writing Clear, Measurable Objectives for Continuing Education, the author underscores that effective CE begins with well-constructed learning objectives anchored in identified practice gaps. Strong objectives use precise, action-oriented verbs to describe observable behaviors that align with desired changes in knowledge, competence, performance, or patient outcomes. Writing one measurable action per objective to ensure clarity, assess ability, and alignment with outcomes. Thoughtfully crafted objectives are foundational to accredited education that demonstrates measurable impact in clinical practice.
AHRQ has launched a new Diagnostic Excellence hub, positioning diagnostic safety and accuracy as an explicit quality domain within its Quality Indicators (QI) program. The centralized resource connects health systems to updated QI v2025 software, technical documentation, and risk-adjusted methods to calculate and benchmark diagnostic-related indicators using administrative data. It also highlights Measure Dx, which helps organizations detect and learn from diagnostic safety events, and Calibrate Dx, a clinician-focused tool that strengthens diagnostic decision-making through structured case review and reflection. Together, these resources support both system-level learning and individual clinician improvement.
