Faculty Bibliography

The rapid evolution of generative artificial intelligence (AI) is poised to transform medicine and medical education. Large language models (LLMs) have begun to demonstrate capabilities in reasoning, diagnosis, documentation, and patient communication that can rival or exceed those of clinicians. In medical education, AI is reshaping how students learn and how faculty teach-offering individualized, context-sensitive guidance at scale. This article outlines the current state of AI integration in health care, examines how systems can responsibly implement it to enhance patient care and education, and raises critical questions about ethics and safety as we harness its transformative potential.

The rapid advancement of generative artificial intelligence (GAI) is poised to revolutionize medical education, clinical decision-making, and health care workflow. Despite considerable interest and a surfeit of newly available tools, medical educators largely lack both competencies and guidance on how to incorporate the new and rapidly evolving world of GAI into the core medical school curriculum and experiences of undergraduate medical education. This Scholarly Perspective highlights the need for medical schools to adapt to this new paradigm by implementing policies, governance, and curricula that address the ethical, technical, and pedagogical implications of GAI. The authors recommend creating policies for appropriate GAI use, designed to protect institutional and patient data, and provide students with clarity on the appropriate use of AI for education. The authors suggest that implementing GAI governance at institutions is crucial to create guiding principles on ethical and equitable GAI use and involving students as coinventors of local innovation. The authors argue that providing faculty and learners with tools and training for safe experimentation with GAI and defining competencies for students and faculty are essential. Curricula for GAI should focus on implications of clinical uses. The authors propose a set of new competencies for GAI that build on those already established for AI in general. Given how dynamic the world of GAI is and how quickly new innovations are changing longstanding practices of clinical medicine, it is imperative that the medical education community acts together to share best practices, gather data to assess the impact of GAI education, continuously update the expected competencies of medical students, and help students prepare for a career that will be continually changed by GAI.

PURPOSE/OBJECTIVE:For accelerated 3-year MD (3YMD) pathways to be fully adopted in medical education, a comprehensive analysis of outcome data is needed. This study includes 7 accelerated 3YMD graduating classes at NYU Grossman School of Medicine (NYUGSOM) and reports on outcomes from both medical school and internship compared to their 4-year MD (4YMD) counterparts. METHOD/METHODS:Outcomes across the undergraduate-graduate medical education continuum for the first 7 classes of NYUGSOM graduates (matriculated from 2013-2019) from the accelerated 3YMD (n = 136) and 4YMD pathways (n = 681) were compared. For the internship outcomes, 3YMD interns were compared with 4YMD interns who graduated from NYUGSOM and all 4YMD interns (4YMD graduates from NYUGSOM and any other medical school) at NYUGSOM residencies. RESULTS:Accelerated 3YMD students were approximately 5 months older at admission and had higher multiple mini-interview scores than 4YMD students. Overall, accelerated 3YMD students performed similarly to 4YMD students during medical school and internship. Significant differences included higher performance by 3YMD students on preclerkship exams and lower performance on Steps 1 and 2 (average: 5.6 and 8.3 fewer points, respectively) and the physical examination portion of the NYUGSOM Comprehensive Clinical Skills Exam. Internship data indicated comparable team assessments across all residencies, statistically significant higher performance on Step 3 when compared to all 4YMD interns, and, in internal medicine, comparable clinical reasoning between 3YMD and all 4YMD interns. When comparing 3YMD interns to all 4YMD interns in the internal medicine residency program, 3YMD interns had a statistically significantly higher performance on milestones. CONCLUSIONS:The outcomes from 7 years of graduating accelerated 3YMD students at NYUGSOM show similar performance in medical school and early residency to 4YMD graduates. Long-term study of accelerated 3YMD students from NYUGSOM and other medical schools is needed to further validate the success of this innovative medical education pathway.

BACKGROUND:Coaching has been proposed to support the transition to residency. Clarifying its impact will help define its value and best use. OBJECTIVE:To explore the experiences of residents working with coaches through the residency transition. DESIGN/METHODS:A cohort comparison survey compared experiences of a coached resident cohort with coaches to the prior, uncoached cohort. PARTICIPANTS/METHODS:Post-graduate year (PGY)-2 residents in internal medicine, obstetrics and gynecology, emergency medicine, and pathology at a single academic center. INTERVENTIONS/METHODS:Faculty trained as coaches had semi-structured meetings with graduating medical students and residents throughout the PGY-1 year. MAIN MEASURES/METHODS:An online anonymous survey assessed effects of coaching on measures of self-directed learning, professional development, program support and impact of coaching using existing scales (2-item Maslach Burnout Inventory, Brief Resilient Coping Scale, 2-item Connor-Davidson Resilience Scale, Stanford Professional Fulfillment Inventory), and novel measures adapted for this survey. Bivariate analyses (t-tests and chi-square tests) compared cohort responses. MANOVA assessed the effects of coaching, burnout and their interactions on the survey domains. KEY RESULTS/RESULTS:Of 156 PGY2 residents, 86 (55%) completed the survey. More residents in the "un-coached" cohort reported burnout (69%) than the "coached" cohort (51%). Burnout was significantly and negatively associated (F = 3.97 (df 7, 75); p < .001) with the learning and professional development outcomes, while being coached was significantly and positively associated with those outcomes (F = 5.54 (df 9, 75); p < .001). Significant interaction effects were found for goal-setting attitudes, professional fulfillment, and perceived program career support such that the positive differences in these outcomes between coached and un-coached residents were greater among burned out residents. Coached residents reported a positive impact of coaching across many domains. CONCLUSIONS:Residents experiencing coaching reported better professional fulfillment and development outcomes, with more pronounced differences in trainees experiencing burnout. Coaching is a promising tool to support a fraught professional transition.

Over the last decade there has been tremendous growth in the development of accelerated MD pathways that allow medical students to graduate in three years. Developing an accelerated pathway program requires commitment from students and faculty with intensive re-thinking and altering of the curriculum to ensure adequate content to achieve competency in an accelerated timeline. A re-visioning of assessment and advising must follow and the application of AI and new technologies can be added to support teaching and learning. We describe the curricular revision to an accelerated pathway at NYU Grossman School of Medicine highlighting our thought process, conceptual framework, assessment methods and outcomes over the last ten years.

Precision education (PE) uses personalized educational interventions to empower trainees and improve learning outcomes. While PE has the potential to represent a paradigm shift in medical education, a theoretical foundation to guide the effective implementation of PE strategies has not yet been described. Here, the authors introduce a theoretical foundation for the implementation of PE, integrating key learning theories with the digital tools that allow them to be operationalized. Specifically, the authors describe how the master adaptive learner (MAL) model, transformative learning theory, and self-determination theory can be harnessed in conjunction with nudge strategies and audit and feedback dashboards to drive learning and meaningful behavior change. The authors also provide practical examples of these theories and tools in action by describing precision interventions already in use at one academic medical center, concretizing PE's potential in the current clinical environment. These examples illustrate how a firm theoretical grounding allows educators to most effectively tailor PE interventions to fit individual learners' needs and goals, facilitating efficient learning and, ultimately, improving patient and health system outcomes.

The goal of medical education is to produce a physician workforce capable of delivering high-quality equitable care to diverse patient populations and communities. To achieve this aim amidst explosive growth in medical knowledge and increasingly complex medical care, a system of personalized and continuous learning, assessment, and feedback for trainees and practicing physicians is urgently needed. In this perspective, the authors build on prior work to advance a conceptual framework for such a system: precision education (PE).PE is a system that uses data and technology to transform lifelong learning by improving personalization, efficiency, and agency at the individual, program, and organization levels. PE "cycles" start with data inputs proactively gathered from new and existing sources, including assessments, educational activities, electronic medical records, patient care outcomes, and clinical practice patterns. Through technology-enabled analytics, insights are generated to drive precision interventions. At the individual level, such interventions include personalized just-in-time educational programming. Coaching is essential to provide feedback and increase learner participation and personalization. Outcomes are measured using assessment and evaluation of interventions at the individual, program, and organizational level, with ongoing adjustment for repeated cycles of improvement. PE is rooted in patient, health system, and population data; promotes value-based care and health equity; and generates an adaptive learning culture.The authors suggest fundamental principles for PE, including promoting equity in structures and processes, learner agency, and integration with workflow (harmonization). Finally, the authors explore the immediate need to develop consensus-driven standards: rules of engagement between people, products, and entities that interact in these systems to ensure interoperability, data sharing, replicability, and scale of PE innovations.

PURPOSE/OBJECTIVE:This study explores coaching during transition from medical school to residency through the perspectives of residents and faculty coaches participating in a coaching program from residency match through the first year of residency. METHOD/METHODS:From January to September 2020, 15 faculty coaches in internal medicine, obstetrics and gynecology, emergency medicine, orthopedics, and pathology participated in a synchronous, in-person coaching training course. All 94 postgraduate year 1 residents in these 5 training programs participated. Between November 2021 and March 2022, focus groups were held with interns from all residency programs participating in the program. Interviews were conducted with faculty coaches in February 2022. Faculty and residents discussed their experiences with and perceptions of coaching. De-identified transcripts were coded, and researchers organized these codes into broader categories, generated cross-cutting themes from the concepts described in both cohorts, and proposed a model for the potential of coaching to support the transition to residency. Descriptive themes were constructed and analytic themes developed by identifying concepts that crossed the data sets. RESULTS:Seven focus groups were held with 39 residents (42%). Residents discussed the goals of a coaching program, coach attributes, program factors, resident attributes, and the role of the coach. Coaches focused on productivity of coaching, coaching skills and approach, professional development, and scaffolding the coaching experience. Three analytic themes were created: (1) coaching as creating an explicit curriculum for growth through the transition to residency, (2) factors contributing to successful coaching, and (3) ways in which these factors confront graduate medical education norms. CONCLUSIONS:Learner and faculty perspectives on coaching through the transition to residency reveal the potential for coaching to make an explicit and modifiable curriculum for professional growth and development. Creating structures for coaching in graduate medical education may allow for individualized professional development, improved mindset, self-awareness, and self-directed learning.

Competency-based medical education (CBME) has produced large collections of data, which can provide valuable information about trainees and medical education systems. Many organizations continue to struggle with accessing, collecting, governing, analyzing, and visualizing their clinical and/or educational data. This hinders data sharing efforts within and across organizations, which are foundational in supporting system-wide improvements. Challenges to data sharing within medical education include variability in legislation, existing data policies, heterogeneity of data, inadequate data infrastructure, and various intended purposes or uses. In this eye opener, the authors describe four case studies to illustrate some of the aforementioned challenges and characterize the complexity of data sharing within medical education along two dimensions: organizational (single vs. multiple) and data type (clinical and/or educational). With the goal of better supporting data sharing initiatives, the authors introduce an action-oriented blueprint that includes a three-stage process (i.e., preparation, execution, and iteration) to highlight crucial aspects of data sharing. This evidence-informed model incorporates current best practices and aims to support data sharing initiatives within their own organizations and across multiple organizations. Finally, organizations can use this model to conceptually guide and track their progression throughout the data sharing process.