Faculty Bibliography

Graduate medical education (GME) and Clinical Competency Committees (CCC) have been evolving to monitor trainee progression using competency-based medical education principles and outcomes, though evidence suggests CCCs fall short of this goal. Challenges include that evaluation data are often incomplete, insufficient, poorly aligned with performance, conflicting or of unknown quality, and CCCs struggle to organize, analyze, visualize, and integrate data elements across sources, collection methods, contexts, and time-periods, which makes advancement decisions difficult. Learning analytics have significant potential to improve competence committee decision making, yet their use is not yet commonplace. Learning analytics (LA) is the interpretation of multiple data sources gathered on trainees to assess academic progress, predict future performance, and identify potential issues to be addressed with feedback and individualized learning plans. What distinguishes LA from other educational approaches is systematic data collection and advanced digital interpretation and visualization to inform educational systems. These data are necessary to: 1) fully understand educational contexts and guide improvements; 2) advance proficiency among stakeholders to make ethical and accurate summative decisions; and 3) clearly communicate methods, findings, and actionable recommendations for a range of educational stakeholders. The ACGME released the third edition CCC Guidebook for Programs in 2020 and the 2021 Milestones 2.0 supplement of the Journal of Graduate Medical Education (JGME Supplement) presented important papers that describe evaluation and implementation features of effective CCCs. Principles of LA underpin national GME outcomes data and training across specialties; however, little guidance currently exists on how GME programs can use LA to improve the CCC process. Here we outline recommendations for implementing learning analytics for supporting decision making on trainee progress in two areas: 1) Data Quality and Decision Making, and 2) Educator Development.

Background/Objective: A growing number of biomedical doctoral graduates are entering the biotechnology and industry workforce, though most lack training in business practice. Entrepreneurs can benefit from venture creation and commercialization training that is largely absent from standard biomedical educational curricula. The NYU Biomedical Entrepreneurship Educational Program (BEEP) seeks to fill this training gap to prepare and motivate biomedical entrepreneurs to develop an entrepreneurial skill set, thus accelerating the pace of innovation in technology and business ventures. Methods: The NYU BEEP Model was developed and implemented with funding from NIDDK and NCATS. The program consists of a core introductory course, topic-based interdisciplinary workshops, venture challenges, on-line modules, and mentorship from experts. Here, we evaluate the efficacy of the core, introductory course, Foundations of Biomedical Startups, through the use of pre/post-course surveys and free-response answers. Results: After 2 years, 153 participants (26% doctoral students, 23% post-doctoral PhDs, 20% faculty, 16% research staff, 15% other) have completed the course. Evaluation data show self-assessed knowledge gain in all domains. The percentage of students rating themselves as either competent or on the way to being an expert in all areas was significantly higher post-course (P < 0.05). In each content area, the percentages of participants rating themselves as very interested increased post-course. 95% of those surveyed reported the course met its objectives, and 95% reported a higher likelihood of pursuing commercialization of discoveries post-course. Conclusion: NYU BEEP can serve as a model to develop similar curricula/programs to enhance entrepreneurial activity of early-stage researchers.

PURPOSE/OBJECTIVE:To explore whether a machine-learning algorithm could accurately perform the initial screening of medical school applications. METHOD/METHODS:Using application data and faculty screening outcomes from the 2013 to 2017 application cycles (n = 14,555 applications), the authors created a virtual faculty screener algorithm. A retrospective validation using 2,910 applications from the 2013 to 2017 cycles and a prospective validation using 2,715 applications during the 2018 application cycle were performed. To test the validated algorithm, a randomized trial was performed in the 2019 cycle, with 1,827 eligible applications being reviewed by faculty and 1,873 by algorithm. RESULTS:The retrospective validation yielded area under the receiver operating characteristic (AUROC) values of 0.83, 0.64, and 0.83 and area under the precision-recall curve (AUPRC) values of 0.61, 0.54, and 0.65 for the invite for interview, hold for review, and reject groups, respectively. The prospective validation yielded AUROC values of 0.83, 0.62, and 0.82 and AUPRC values of 0.66, 0.47, and 0.65 for the invite for interview, hold for review, and reject groups, respectively. The randomized trial found no significant differences in overall interview recommendation rates according to faculty or algorithm and among female or underrepresented in medicine applicants. In underrepresented in medicine applicants, there were no significant differences in the rates at which the admissions committee offered an interview (70 of 71 in the faculty reviewer arm and 61 of 65 in the algorithm arm; P = .14). No difference in the rate of the committee agreeing with the recommended interview was found among female applicants (224 of 229 in the faculty reviewer arm and 220 of 227 in the algorithm arm; P = .55). CONCLUSIONS:The virtual faculty screener algorithm successfully replicated faculty screening of medical school applications and may aid in the consistent and reliable review of medical school applicants.

BACKGROUND/UNASSIGNED:A lack of educational continuity creates disorienting friction at the onset of residency. Few programs have harnessed the benefits of coaching, which can facilitate self-directed learning, competency development, and professional identity formation, to help ease this transition. OBJECTIVE/UNASSIGNED:To describe the process of training faculty Bridge Coaches for the Transition to Residency Advantage (TRA) program for interns. METHODS/UNASSIGNED:Nineteen graduate faculty educators participated in a coaching training course with formative skills assessment as part of a faculty development program starting in January 2020. Surveys (n = 15; 79%) and a focus group (n = 7; 37%) were conducted to explore the perceived impact of the training course on coaching skills, perceptions of coaching, and further program needs during the pilot year of the TRA program. RESULTS/UNASSIGNED:Faculty had strong skills around establishing trust, authentic listening, and supporting goal-setting. They required more practice around guiding self-discovery and following a coachee-led agenda. Faculty found the training course to be helpful for developing coaching skills. Faculty embraced their new roles as coaches and appreciated having a community of practice with other coaches. Suggestions for improvement included more opportunities to practice and receive feedback on skills and additional structures to further support TRA program encounters with coaches. CONCLUSIONS/UNASSIGNED:The faculty development program was feasible and had good acceptance among participants. Faculty were well-suited to serve as coaches and valued the coaching mindset. Adequate skills reinforcement and program structure were identified as needs to facilitate a coaching program in graduate medical education.

BACKGROUND:Although efforts are underway to address social determinants of health (SDOH), little is known about physicians' SDOH practices despite evidence that failing to fully elicit and respond to social needs can compromise patient safety and undermine both the quality and effectiveness of treatment. In particular, interventions designed to enhance response to social needs have not been assessed using actual practice behaviour. In this study, we evaluate the degree to which providing primary care physicians with feedback on their SDOH practice behaviours is associated with increased rates of eliciting and responding to housing and social isolation needs. METHODS:Unannounced standardised patients (USPs), actors trained to consistently portray clinical scenarios, were sent, incognito, to all five primary care teams in an urban, safety-net healthcare system. Scenarios involved common primary care conditions and each included an underlying housing (eg, mould in the apartment, crowding) and social isolation issue and USPs assessed whether the physician fully elicited these needs and if so, whether or not they addressed them. The intervention consisted of providing physicians with audit/feedback reports of their SDOH practices, along with brief written educational material. A prepost comparison group design was used to evaluate the intervention; four teams received the intervention and one team served as a 'proxy' comparison (no intervention). Preintervention (February 2017 to December 2017) rates of screening for and response to the scripted housing and social needs were compared with intervention period (January 2018 to March 2019) rates for both intervention and comparison teams. RESULTS:108 visits were completed preintervention and 183 during the intervention period. Overall, social needs were not elicited half of the time and fully addressed even less frequently. Rates of identifying the housing issue increased for teams that received audit/feedback reports (46%-60%; p=0.045) and declined for the proxy comparison (61%-42%; p=0.174). Rates of responding to housing needs increased significantly for intervention teams (15%-41%; p=0.004) but not for the comparison team (21%-29%; p=0.663). Social isolation was identified more frequently postintervention (53%) compared with baseline (39%; p=0.041) among the intervention teams but remained unchanged for the comparison team (39% vs 32%; p=0.601). Full exploration of social isolation remained low for both intervention and comparison teams. CONCLUSIONS:Results suggest that physicians may not be consistently screening for or responding to social needs but that receiving feedback on those practices, along with brief targeted education, can improve rates of SDOH screening and response.

INTRODUCTION/BACKGROUND:COVID-19 forced health systems to rapidly implement telehealth for routine practice, often without sufficient training or standards. We conducted a longitudinal survey of physicians to explore changes in their perceptions of the challenges and benefits of telehealth and identify recommendations for future practice. METHODS:An anonymous online survey was distributed to a cohort of internal medicine physicians in May to June 2020 and March to June 2021. Changes in responses between 2020 and 2021 and by site (private vs. public) were described. These findings, along with those of a thematic analysis of open-ended responses to questions on telehealth experiences, informed a set of recommendations. RESULTS: = 0.027). Physicians' open-ended responses identified recommendations for further improving the design and use of telehealth. DISCUSSION/CONCLUSIONS:Results suggest that physician experience with telehealth improved but opportunities for training and improved integration remain. Longitudinal assessment can deepen understanding of the evolution of telehealth care.