Background: There is no consensus regarding values important for medical resident success, and current methods for selecting residents correlate poorly with success in residency.
Objective: We developed and validated a set of values demonstrated by exemplary residents in the Internal Medicine-Pediatrics program at the University of Utah and used them to inform our resident selection process.
Design: We utilized a modified Delphi method to identify and internally validate values of successful residents. We implemented these values into the interview evaluation rubric.
Participants: Four members of the Internal Medicine-Pediatrics residency program leadership and eleven current residents aided in value generation. Nine faculty from leadership positions in the residency programs of Internal Medicine-Pediatrics, Internal Medicine, and Pediatrics formed a local expert panel for validation.
Approach: We performed a literature review and engaged local stakeholders in a semi-structured group interview to generate 107 values. After consolidation based on redundancy, two iterative cycles of expert review using a modified Delphi approach, and alignment with the Accreditation Council for Graduate Medical Education core competencies, eleven values achieved expert agreement and were integrated into an interview rubric to aid in resident selection.
Key Results: We identified eleven values important for resident success: academic strength, intellectual curiosity, compassion, communication, work ethic, teamwork, leadership, self-awareness, DEI (diversity, equity, and inclusion), professionalism, and adaptability. The rank list from 2021 was found to correlate with a score based on values, but not Step 2 score, as it did in 2017.
Conclusions: We applied a modified Delphi method to generate eleven observable values present in the ideal Internal Medicine-Pediatric resident at one academic health center in the Intermountain West. Higher Step 2 scores no longer correlated with higher ranking when we used these values to inform our rank list.
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http://dx.doi.org/10.1007/s11606-022-07857-y | DOI Listing |
Circ Cardiovasc Qual Outcomes
January 2025
Department of Internal Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas (S.G., Nimesh Patel, M.K., M.S.S.).
JACC Adv
December 2024
Division of Pediatric Cardiology, Department of Pediatrics, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA.
Background: Early clinical outcomes data for adjunctive systemic sirolimus therapy (SST) for moderate to severe pediatric pulmonary vein stenosis (PVS) are promising but limited.
Objectives: The authors aimed to characterize a cohort of patients treated with SST to determine if SST was associated with a reduction in frequency of PVS interventions.
Methods: Medical records of 45 patients with PVS treated with SST for ≥1 month from 2015 to 2022 were retrospectively reviewed.
JACC Adv
December 2024
Alliance for Medical Research in Africa, Dakar, Senegal.
This proposed scientific statement is focused on providing new insights regarding challenges and opportunities for cardiovascular health (CVH) promotion in Africa. The statement includes an overview of the current state of CVH in Africa, with a particular interest in the cardiometabolic risk factors and their evaluation through metrics. The statement also explains the main principles of primordial prevention, its relevance in reducing noncommunicable disease and the different strategies that have been effective worldwide.
View Article and Find Full Text PDFKidney Res Clin Pract
January 2025
Department of Internal Medicine, St. Vincent Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
The impact of age on the relationship between body mass index (BMI) and all-cause mortality in hemodialysis (HD) patients is not clearly understood. We analyzed the association between BMI and all-cause mortality, stratified by age, in patients undergoing HD using data from the Korean Renal Data System (KORDS). We analyzed 66,129 HD patients from the 2023 KORDS database, with data collected between 2001 and 2022.
View Article and Find Full Text PDFKidney Res Clin Pract
January 2025
Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Background: Acute kidney injury (AKI) is a critical clinical condition that requires immediate intervention. We developed an artificial intelligence (AI) model called PRIME Solution to predict AKI and evaluated its ability to enhance clinicians' predictions.
Methods: The PRIME Solution was developed using convolutional neural networks with residual blocks on 183,221 inpatient admissions from a tertiary hospital (2013-2017) and externally validated with 4,501 admissions at another tertiary hospital (2020-2021).
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