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Faculty development (FD) programs are critical for providing the knowledge and skills necessary to drive positive change in health professions education, but they take many forms to attain the program goals. The Macy Faculty Scholars Program (MFSP), created by the Josiah Macy Jr. Foundation (JMJF) in 2010, intends to develop participants as leaders, scholars, teachers, and mentors. After a decade of implementation, an external review committee conducted a program evaluation to determine how well the program met its intended goals and defined options for ongoing improvement.The committee selected Stufflebeam's CIPP (context, input, process, products) framework to guide the program evaluation. Context and input components were derived from the MFSP description and demographic data, respectively. Process and product components were obtained through a mixed-methods approach, utilizing both quantitative and qualitative data obtained from participant survey responses, and curriculum vitae (CV).The evaluation found participants responded favorably to the program and demonstrated an overall increase in academic productivity, most pronounced during the two years of the program. Mentorship, community of practice, and protected time were cited as major strengths. Areas for improvement included: enhancing the diversity of program participants, program leaders and mentors across multiple sociodemographic domains; leveraging technology to strengthen the MFSP community of practice; and improving flexibility of the program.The program evaluation results provide evidence supporting ongoing investment in faculty educators and summarizes key strengths and areas for improvement to inform future FD efforts for both the MFSP and other FD programs.

Background: The integration of artificial intelligence (AI) into clinical practice is transforming both clinical practice and medical education. AI-based systems aim to improve the efficacy of clinical tasks, enhancing diagnostic accuracy and tailoring treatment delivery. As it becomes increasingly prevalent in health care for high-quality patient care, it is critical for health care providers to use the systems responsibly to mitigate bias, ensure effective outcomes, and provide safe clinical practices. In this study, the clinical task is the identification of heart failure (HF) prior to surgery with the intention of enhancing clinical decision-making skills. HF is a common and severe disease, but detection remains challenging due to its subtle manifestation, often concurrent with other medical conditions, and the absence of a simple and effective diagnostic test. While advanced HF algorithms have been developed, the use of these AI-based systems to enhance clinical decision-making in medical education remains understudied., Objective: This research protocol is to demonstrate our study design, systematic procedures for selecting surgical cases from electronic health records, and interventions. The primary objective of this study is to measure the effectiveness of interventions aimed at improving HF recognition before surgery, the second objective is to evaluate the impact of inaccurate AI recommendations, and the third objective is to explore the relationship between the inclination to accept AI recommendations and their accuracy., Methods: Our study used a 3 × 2 factorial design (intervention type × order of prepost sets) for this randomized trial with medical students. The student participants are asked to complete a 30-minute e-learning module that includes key information about the intervention and a 5-question quiz, and a 60-minute review of 20 surgical cases to determine the presence of HF. To mitigate selection bias in the pre- and posttests, we adopted a feature-based systematic sampling procedure. From a pool of 703 expert-reviewed surgical cases, 20 were selected based on features such as case complexity, model performance, and positive and negative labels. This study comprises three interventions: (1) a direct AI-based recommendation with a predicted HF score, (2) an indirect AI-based recommendation gauged through the area under the curve metric, and (3) an HF guideline-based intervention., Results: As of July 2023, 62 of the enrolled medical students have fulfilled this study's participation, including the completion of a short quiz and the review of 20 surgical cases. The subject enrollment commenced in August 2022 and will end in December 2023, with the goal of recruiting 75 medical students in years 3 and 4 with clinical experience., Conclusions: We demonstrated a study protocol for the randomized trial, measuring the effectiveness of interventions using AI and HF guidelines among medical students to enhance HF recognition in preoperative care with electronic health record data., International Registered Report Identifier (irrid): DERR1-10.2196/49842., (©Hyeon Joo, Michael R Mathis, Marty Tam, Cornelius James, Peijin Han, Rajesh S Mangrulkar, Charles P Friedman, VG Vinod Vydiswaran. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 24.10.2023.)

Background: Aortic regurgitation (AR) is common and detrimental in patients with left ventricular assist devices (LVADs). Off-label use of transcatheter aortic valve replacement (TAVR) has emerged as a potential treatment option. Further data are required regarding the feasibility and outcomes of TAVR to treat AR in LVAD recipients., Methods: A retrospective review of all patients with LVADs who underwent TAVR for the treatment of AR at a single center was performed. All echocardiograms were independently reviewed to ensure accuracy., Results: Eleven patients with continuous-flow LVADs underwent TAVR for AR. All patients had moderate or severe AR with New York Heart Association (NYHA) class III and IV symptoms. Implantation of more than 1 valve was required in 4 (36.3%) patients; 1 patient died during the procedure because of valve migration into the left ventricle and 1 patient died in-hospital after TAVR. Of 9 (81.8%) patients discharged alive, 8 (72.7%) were alive at 12 months and all survivors had improvement in AR severity, natriuretic peptide levels, left ventricle end-diastolic diameter, and NYHA class. Five (62.5%) survivors had a large improvement (>20 points) in the Kansas City Cardiomyopathy Questionnaire score at 1 year. One survivor experienced heart failure, requiring hospitalization, within 1 year., Conclusions: In this single-center series, TAVR for the treatment of AR in patients with LVADs is technically challenging but feasible in select patients and may produce durable improvements in AR severity, functional status, and quality of life.