Faculty Bibliography

Discrimination toward trainees is a pervasive problem, with surveys showing it is often perpetrated by patients. For several years, residents and faculty in an internal medicine residency have participated in a workshop offering a framework for responding to discriminatory behavior by patients. As part of a larger effort to reinforce this teaching and promote an inclusive environment, the authors pursued a multi-pronged simulation curriculum that could be incorporated into graduate medical education programs across their institution. First, the authors conducted trainee and faculty focus groups to better understand their experiences. Qualitative data was collected, including recommendations for responding to discrimination, characteristics of the most common experiences, and trainees' own ideas for worthwhile simulation scenarios. Trainees and faculty were then brought together in a collaborative process to co-develop simulation cases that were later implemented in curricula across multiple learner levels, specialties, and contexts. Participants in these simulations reported improved comfort in responding to discrimination in the moment and/or in the wake of such incidents. Through trainee-faculty collaboration, the project yielded authentic and impactful simulation experiences for learners, while also giving trainees an opportunity to turn previous trauma into constructive learning opportunities that promote an inclusive environment.

INTRODUCTION/UNASSIGNED:Addressing systemic bias in medical school assessment is an urgent task for medical education. This paper outlines recommendations on topic areas for further research on systemic bias, developed from a workshop discussion at the 2023 annual meeting of the Society of Directors of Research in Medical Education. MATERIALS AND METHODS/UNASSIGNED:During the workshop, directors engaged in small-group discussions on guidelines to address bias in assessment practices following a proposed categorization of 'Do's,' 'Don'ts,' and 'Don't knows' and listed their insights using anonymous sticky notes, which were shared and discussed with the larger group of participants. The authors performed a content analysis of the notes through deductive and inductive coding. We reviewed and discussed our analysis to reach consensus. RESULTS/UNASSIGNED:The workshop included 31 participants from 28 institutions across the US and Canada, generating 51 unique notes. Participants identified 23 research areas in need of further study. The inductive analysis of proposed research areas revealed four main topics: 1) The role of interventions, including pre-medical academic interventions, medical-education interventions, assessment approaches, and wellness interventions; 2) Professional development, including the definition and assessment of professionalism and professional identity formation; 3) Context, including patient care and systemic influences; and 4) Research approaches. DISCUSSION/UNASSIGNED:While limited to data from a single workshop, the results offered perspectives about areas for further research shared by a group of directors of medical education research units from diverse backgrounds. The workshop produced valuable insights into the need for more evidence-based interventions that promote more equitable assessment practices grounded in real-world situations and that attenuate the effects of bias.

Tools to briefly assess diet among US Spanish-speaking adults are needed to identify individuals at risk for cardiometabolic disease (CMD) related to diet. Two registered dietitian nutritionists (RDNs) recruited bilingual medical students to translate the validated Diet Risk Score (DRS) into Spanish (DRS-S). Participants were recruited from a federally qualified health center. Students administered the DRS-S and one 24-h recall (Automated Self-Administered 24-Hour (ASA24^®) Dietary Assessment Tool) on one day; a second recall was administered within 1 week. Recalls were scored using the Healthy Eating Index (HEI)-2015, a measure of adherence to the Dietary Guidelines for Americans. Spearman correlations, weighted kappa, and ANOVA were conducted using SAS 9.4 to assess the relative validity of the DRS-S. Thirty-one Spanish-speaking adults (female: n = 17, 53%; mean age: 58 (42-69)) completed assessments. The mean DRS-S was 9 (SD = 4.2) (max: 27; higher score = higher risk) and the mean HEI-2015 score was 65.7 (SD = 9.7) (max: 100; higher score = lower risk), with significant agreement between measures (r: -0.45 (p = 0.01)), weighted kappa: -0.3 (p = 0.03). The DRS-S can be used in resource-constrained settings to assess diet for intervention and referral to RDNs. The DRS-S should be tested in clinical care to assess the impact of dietary changes to reduce CMD risk.

BACKGROUND:This study describes how New York City (NYC) Health + Hospitals implemented a large-scale Community Health Worker (CHW) program in adult primary care clinics between January 2022 and December 2023 and established metrics to monitor program implementation. This study is timely as healthcare systems consider how to scale high-quality CHW programs. METHODS:We collected metrics in the following areas: (1) Workforce demographics, team structure, and training; (2) Enrolled patient demographics; (3) Patient-centered metrics, such as patient counts (e.g. patients outreached and enrolled) and engagement (e.g. median time in program, caseloads per CHW), and goals (e.g. median number of goals identified and completed). Metrics are based on standard data elements captured through CHW documentation in the electronic health record collected during program implementation. Data cleaning is completed using SQL queries and R scripts. RESULTS:In June 2023, there were a total of 97 CHW and 22 CHW Supervisor staff lines in adult primary care across 17 healthcare sites. There were 4.6 CHWs to 1 CHW supervisor on average though this ranged by facility from 1:1 to 1:6. Compared to the population that receives primary care at NYC H + H, CHWs served more African American/Black patients (40% vs. 32%) and an older patient population (35% older than 65 vs. 21% older than 65). From January 2022 to December 2023, 13,812 patients were outreached by CHWs. Of these, 9,069 (66%) were referred by clinicians, 7,331 (53%) were enrolled, and 5,044 (37%) successfully graduated. The median number of goals identified by patients was four, and the median number of goals completed with a CHW per patient was three. The top three goals were primary care engagement (47%), specialty care engagement (46%), and food insecurity (45%). CONCLUSION/CONCLUSIONS:Establishing clear implementation and process metrics helps to ensure that CHWs embedded in health systems can meaningfully engage adult patients in health care, address patient-centered goals, and connect patients to community and government services.

BACKGROUND:This study aimed to examine the prevalence of inappropriate tight glycemic control in older adults with type 2 diabetes and other chronic conditions in New York City, and to identify factors associated with this practice. METHODS:We conducted a retrospective cohort study using the INSIGHT Clinical Research Network. The study population included 11,728 and 15,196 older adults in New York City (age ≥ 75 years) with a diagnosis of type 2 diabetes, and at least one other chronic medical condition, in 2017 and 2022, respectively. The main outcome of interest was inappropriate tight glycemic control, defined as HbA1c <7.0% (<53 mmol/mol) with prescription of at least one high-risk agent (insulin or insulin secretagogue). RESULTS:The proportion of older adults with inappropriate tight glycemic control decreased by nearly 19% over a five-year period (19.4% in 2017 to 15.8% in 2022). There was a significant decrease in insulin (27.8% in 2017; 24.3% in 2022) and sulfonylurea (29.4% in 2017; 21.7% in 2022) medication prescription, and increase in use of GLP-1 agonists (1.8% in 2017; 11.4% in 2022) and SGLT-2 inhibitors (5.8% in 2017; 25.1% in 2022), among the total population. Factors associated with inappropriate tight glycemic control in 2022 included history of heart failure (adjusted odds ratio [aOR] 1.38), chronic kidney disease ([aOR] 1.93), colorectal cancer ([aOR] 1.38), acute myocardial infarction ([aOR] 1.28), "other" ([aOR] 0.72) or "unknown" ([aOR] 0.72) race, and a point increase in BMI ([aOR] 0.98). CONCLUSIONS:We found an encouraging trend toward less use of high-risk medication strategies for older adults with type 2 diabetes and multiple chronic conditions. However, one in six patients in 2022 still had inappropriate tight glycemic control, indicating a need for continued efforts to optimize diabetes management in this population.

BACKGROUND:Healthcare crowdsourcing events (e.g. hackathons) facilitate interdisciplinary collaboration and encourage innovation. Peer-reviewed research has not yet considered a healthcare crowdsourcing event focusing on generative artificial intelligence (GenAI), which generates text in response to detailed prompts and has vast potential for improving the efficiency of healthcare organizations. Our event, the New York University Langone Health (NYULH) Prompt-a-thon, primarily sought to inspire and build AI fluency within our diverse NYULH community, and foster collaboration and innovation. Secondarily, we sought to analyze how participants' experience was influenced by their prior GenAI exposure and whether they received sample prompts during the workshop. METHODS:Executing the event required the assembly of an expert planning committee, who recruited diverse participants, anticipated technological challenges, and prepared the event. The event was composed of didactics and workshop sessions, which educated and allowed participants to experiment with using GenAI on real healthcare data. Participants were given novel "project cards" associated with each dataset that illuminated the tasks GenAI could perform and, for a random set of teams, sample prompts to help them achieve each task (the public repository of project cards can be found at https://github.com/smallw03/NYULH-Generative-AI-Prompt-a-thon-Project-Cards). Afterwards, participants were asked to fill out a survey with 7-point Likert-style questions. RESULTS:Our event was successful in educating and inspiring hundreds of enthusiastic in-person and virtual participants across our organization on the responsible use of GenAI in a low-cost and technologically feasible manner. All participants responded positively, on average, to each of the survey questions (e.g., confidence in their ability to use and trust GenAI). Critically, participants reported a self-perceived increase in their likelihood of using and promoting colleagues' use of GenAI for their daily work. No significant differences were seen in the surveys of those who received sample prompts with their project task descriptions. CONCLUSION/CONCLUSIONS:The first healthcare Prompt-a-thon was an overwhelming success, with minimal technological failures, positive responses from diverse participants and staff, and evidence of post-event engagement. These findings will be integral to planning future events at our institution, and to others looking to engage their workforce in utilizing GenAI.

PURPOSE:The era of precision education is increasingly leveraging electronic health record (EHR) data to assess residents' clinical performance. But precision in what the EHR-based resident performance metrics are truly assessing is not fully understood. For instance, there is limited understanding of how EHR-based measures account for the influence of the team on an individual's performance-or conversely how an individual contributes to team performances. This study aims to elaborate on how the theoretical understandings of supportive and collaborative interdependence are captured in residents' EHR-based metrics. METHOD:Using a mixed methods study design, the authors conducted a secondary analysis of 5 existing quantitative and qualitative datasets used in previous EHR studies to investigate how aspects of interdependence shape the ways that team-based care is provided to patients. RESULTS:Quantitative analyses of 16 EHR-based metrics found variability in faculty and resident performance (both between and within resident). Qualitative analyses revealed that faculty lack awareness of their own EHR-based performance metrics, which limits their ability to act interdependently with residents in an evidence-informed fashion. The lens of interdependence elucidates how resident practice patterns develop across residency training, shifting from supportive to collaborative interdependence over time. Joint displays merging the quantitative and qualitative analyses showed that residents are aware of variability in faculty's practice patterns and that viewing resident EHR-based measures without accounting for the interdependence of residents with faculty is problematic, particularly within the framework of precision education. CONCLUSIONS:To prepare for this new paradigm of precision education, educators need to develop and evaluate theoretically robust models that measure interdependence in EHR-based metrics, affording more nuanced interpretation of such metrics when assessing residents throughout training.