Faculty Bibliography

BACKGROUND:Objective measures and large datasets are needed to determine aspects of the Clinical Learning Environment (CLE) impacting the essential skill of clinical reasoning documentation. Artificial Intelligence (AI) offers a solution. Here, the authors sought to determine what aspects of the CLE might be impacting resident clinical reasoning documentation quality assessed by AI. METHODS:In this observational, retrospective cross-sectional analysis of hospital admission notes from the Electronic Health Record (EHR), all categorical internal medicine (IM) residents who wrote at least one admission note during the study period July 1, 2018- June 30, 2023 at two sites of NYU Grossman School of Medicine's IM residency program were included. Clinical reasoning documentation quality of admission notes was determined to be low or high-quality using a supervised machine learning model. From note-level data, the shift (day or night) and note index within shift (if a note was first, second, etc. within shift) were calculated. These aspects of the CLE were included as potential markers of workload, which have been shown to have a strong relationship with resident performance. Patient data was also captured, including age, sex, Charlson Comorbidity Index, and primary diagnosis. The relationship between these variables and clinical reasoning documentation quality was analyzed using generalized estimating equations accounting for resident-level clustering. RESULTS:Across 37,750 notes authored by 474 residents, patients who were older, had more pre-existing comorbidities, and presented with certain primary diagnoses (e.g., infectious and pulmonary conditions) were associated with higher clinical reasoning documentation quality. When controlling for these and other patient factors, variables associated with clinical reasoning documentation quality included academic year (adjusted odds ratio, aOR, for high-quality: 1.10; 95% CI 1.06-1.15; P <.001), night shift (aOR 1.21; 95% CI 1.13-1.30; P <.001), and note index (aOR 0.93; 95% CI 0.90-0.95; P <.001). CONCLUSIONS:AI can be used to assess complex skills such as clinical reasoning in authentic clinical notes that can help elucidate the potential impact of the CLE on resident clinical reasoning documentation quality. Future work should explore residency program and systems interventions to optimize the CLE.

BACKGROUND:Clinical reasoning (CR) is an essential skill; yet, physicians often receive limited feedback. Artificial intelligence holds promise to fill this gap. OBJECTIVE:We report the development of named entity recognition (NER), logic-based and large language model (LLM)-based assessments of CR documentation in the electronic health record across 2 institutions (New York University Grossman School of Medicine [NYU] and University of Cincinnati College of Medicine [UC]). METHODS:-scores for the NER, logic-based model and area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) for the LLMs. RESULTS:-scores 0.80, 0.74, and 0.80 for D0, D1, D2, respectively. The GatorTron LLM performed best for EA2 scores AUROC/AUPRC 0.75/ 0.69. CONCLUSIONS:This is the first multi-institutional study to apply LLMs for assessing CR documentation in the electronic health record. Such tools can enhance feedback on CR. Lessons learned by implementing these models at distinct institutions support the generalizability of this approach.

BACKGROUND:Despite the central role of experiential learning in residency training, the actual clinical experiences residents participate in are not well characterized. A better understanding of the type, volume, and variation in residents' clinical experiences is essential to support precision medical education strategies. OBJECTIVE:We sought to characterize the entirety of the clinical experiences had by individual internal medicine residents throughout their time in training. METHOD/METHODS:We evaluated the clinical experiences of medicine residents (n = 51) who completed training at NYU Grossman School of Medicine's Brooklyn campus between 2020 and 2023. Residents' inpatient and outpatient experiences were identified using notes written, orders placed, and care team sign-ins; principal ICD-10 codes for each encounter were converted into medical content categories using a previously described crosswalk tool. RESULTS:Of 152,426 clinical encounters with available ICD-10 codes, 132,284 were mapped to medical content categories (94.5% capture). Residents' clinical experiences were particularly enriched in infectious and cardiovascular disease; most had very little exposure to allergy, dermatology, oncology, or rheumatology. Some trainees saw twice as many cases in a given content area as did others. There was little concordance between actual frequency of clinical experience and expected content frequency on the ABIM certification exam. CONCLUSIONS:Individual residents' clinical experiences in training vary widely, both in number and in type. Characterizing these experiences paves the way for exploration of the relationships between clinical exposure and educational outcomes, and for the implementation of precision education strategies that could fill residents' experiential gaps and complement strengths with targeted educational interventions.

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 transitional period between hospital discharge and primary care follow-up is a vulnerable time for patients that can result in adverse health outcomes and preventable hospital readmissions. This is especially true for patients of safety-net hospitals (SNHs) who often struggle to secure primary care access when leaving the hospital due to social, economic and cultural barriers. In this study, we describe a resident-led postdischarge clinic that serves patients discharged from NYU Langone Hospital-Brooklyn, an urban safety-net academic hospital. In our multivariable analysis, there was no statistical difference in the readmission rate between those who completed the transitional care management and those who did not (OR 1.32 (0.75-2.36), p=0.336), but there was a statistically significant increase in primary care provider (PCP) engagement (OR 0.53 (0.45-0.62), p<0.001). Overall, this study describes a postdischarge clinic model embedded in a resident clinic in an urban SNH that is associated with increased PCP engagement, but no reduction in 30-day hospital readmissions.

BACKGROUND:Experiential learning through patient care is fundamental to graduate medical education. Despite this, the actual content to which trainees are exposed in clinical practice is difficult to quantify and is poorly characterized. There remains an unmet need to define precisely how residents' patient care activities inform their educational experience.  METHODS: Using a recently-described crosswalk tool, we mapped principal ICD-10 discharge diagnosis codes to American Board of Internal Medicine (ABIM) content at four training hospitals of a single Internal Medicine (IM) Residency Program over one academic year to characterize and compare residents' clinical educational experiences. Frequencies of broad content categories and more specific condition categories were compared across sites to profile residents' aggregate inpatient clinical experiences and drive curricular change. RESULTS:There were 18,604 discharges from inpatient resident teams during the study period. The crosswalk captured > 95% of discharges at each site. Infectious Disease (ranging 17.4 to 39.5% of total discharges) and Cardiovascular Disease (15.8 to 38.2%) represented the most common content categories at each site. Several content areas (Allergy/Immunology, Dermatology, Obstetrics/Gynecology, Ophthalmology, Otolaryngology/Dental Medicine) were notably underrepresented (≤ 1% at each site). There were significant differences in the frequencies of conditions within most content categories, suggesting that residents experience distinct site-specific clinical content during their inpatient training. CONCLUSIONS:There were substantial differences in the clinical content experienced by our residents across hospital sites, prompting several important programmatic and curricular changes to enrich our residents' hospital-based educational experiences.

PROBLEM/OBJECTIVE:Internal medicine training programs operate under the assumption that the three-year residency training period is sufficient for trainees to achieve the depth and breadth of clinical experience necessary for independent practice; however, the medical conditions to which residents are exposed in clinical practice are not easily measured. As a result, residents' clinical educational experiences are poorly understood. APPROACH/METHODS:A crosswalk tool (a repository of international classification of diseases [ICD]-10 codes linked to medical content areas) was developed to query routinely collected inpatient principal diagnosis codes and translate them into an educationally meaningful taxonomy. This tool provides a robust characterization of residents' inpatient clinical experiences. OUTCOMES/RESULTS:This pilot study has provided proof of principle that the crosswalk tool can effectively map one year of resident-attributed diagnosis codes to both the broad content category level (for example "Cardiovascular Disease") and to the more specific condition category level (for example "Myocardial Disease"). The authors uncovered content areas in their training program that are overrepresented and some that are underrepresented relative to material on the American Board of Internal Medicine (ABIM) Certification Exam. NEXT STEPS/UNASSIGNED:The crosswalk tool introduced here translated residents' patient care activities into discrete, measurable educational content and enabled one internal medicine residency training program to characterize residents' inpatient educational experience with a high degree of resolution. Leaders of other programs seeking to profile the clinical exposure of their trainees may adopt this strategy. Such clinical content mapping drives innovation in the experiential curriculum, enables comparison across practice sites, and lays the groundwork to test associations between individual clinical exposure and competency-based outcomes, which, in turn, will allow medical educators to draw conclusions regarding how clinical experience reflects clinical competency.

Introduction/UNASSIGNED:Virtual urgent care (VUC) provides real-time evaluation, triage, and treatment of low-acuity medical problems; however, VUC physicians have varying levels of telemedicine training. We created a workplace-based experiential onboarding program that deployed standardized patients (SPs) into a VUC clinic to evaluate and deliver feedback to independently practicing physicians, providing quality assurance and identifying areas for improvement. Methods/UNASSIGNED:SPs evaluated communication, disease-specific, and telemedicine skills by observing behaviors. We surveyed participants to evaluate the program. Results/UNASSIGNED:= 34%) well done-highlighting specific behaviors most ripe for improvement. All queried participants indicated that this simulation improved communication and telemedicine skills. Discussion/UNASSIGNED:This workplace-based experiential onboarding program uncovered knowledge gaps within telemedicine skills and patient education domains. Identification of these gaps can help drive new virtual care curricula.