Medical Licensing Research Articles

Mental health stigma in the medical profession: Where do we go from here?

Mental health conditions are highly prevalent among physicians with high rates of depression, anxiety, stress-related disorders, suicidal ideation and burnout reported among medical practitioners at all levels of training and practice. This phenomenon is in part contributed by a highly stressful clinical environment with an often suboptimal support system for doctors. Concerningly, there is hitherto a striking reluctance amongst medical trainees/practitioners to seek treatment/help for mental health-related conditions due to fear of associated stigma and negative career repercussions. In this article, we sought to raise awareness of the mental health stigma that has long been prevailing in the medical community, and review the key drivers of such stigma at the individual, community and organisational level. In general, drivers of mental health stigma in the medical profession include self-stigmatisation predisposed by physician personality and character traits, societal stereotypes about mental illness permeating through the medical community, and systemic constructs such as mandatory mental health declarations for medical licensure that perpetuate the unfortunate perception that mental illness appears synonymous with job impairment or incompetency. To destigmatise mental health issues in the medical profession, we herein propose multi-pronged strategies which can practically be implemented: 1) normalisation of mental health issues through open dialogue and sharing, 2) creating a supportive, “psychologically friendly” work environment through increased accessibility to workplace mental health support services, peer support systems, and reduction of psychiatric “name-calling” practices, and 3) reviewing systemic practices, in particular the mandatory mental health declarations for medical registration, that perpetuate mental health stigma.

The States' Hodgepodge of Physician Licensure Regulations.

The end of the federal COVID-19 public health emergency (PHE) on May 11, 2023, marked a pivotal shift in the landscape of telehealth regulation in the US. Kwan, Jolin, and Shachar analyze the implications of this transition by exposing inconsistencies in access to care. We agree that we now face a "convoluted patchwork of permanent and temporary changes to telehealth law and policy."1.

The States' Hodgepodge of Physician Licensure Regulations

The States' Hodgepodge of Physician Licensure Regulations

Understanding the USMLE journey of Brazilian medical students and graduates: a narrative review.

Over the past decades, advances in medical technology and research have promoted the globalization of medicine, drawing medical students and physicians worldwide into seeking for better career and financial prospects, as well as enhanced clinical and research training abroad. The medical landscape in Brazil has been marked by significant challenges over the past years, particularly within the Unified Health System (SUS). Despite the expansion of medical schools and initiatives such as the Mais Médicos program arising as efforts to improve healthcare access and decrease regional inequalities in the country, such problems as under-resourced working environments, inadequate infrastructure, and unattractive financial compensation for both physician and research careers, have risen as persisting challenges. In the pursuit of improved conditions, the United States Medical Licensing Examination (USMLE) has emerged as an increasingly common pathway for those seeking to overcome these challenges. This review explores the motivations behind Brazilians pursuing the USMLE while examining structural and financial challenges within the country's medical education and SUS landscape.

Impact of USMLE Pass/Fail Step 1 Scoring on Current Medical Students.

The aim of this study was to examine students' perceptions of pass/fail scoring for Step 1, particularly when applying to competitive specialties. This study also investigated whether this transition increased anxiety among medical students and elicited student perspectives on the most critical components of residency applications. The United States Medical Licensing Exam (USMLE) Step 1 Board Exam transitioned from a traditional numeric score to pass/fail for exams taken on or after January 26, 2022. One justification for this change, according to a statement from USMLE, was to ease the transition between undergraduate and graduate medical education. Although many factors are considered when selecting candidates to interview for residency, Step 1 scores were historically used as an important metric. Few studies specifically evaluated medical student's attitudes toward the change in Step 1 grading. An online survey was distributed to Penn State College of Medicine medical students in the 2024 to 2027 classes. Statistical analysis was conducted on responses to research plans, attitudes regarding Step 1 pass/fail, match rates, and importance of application components. There was a 21.3% response rate (127/596) to the survey with 30.7% pursuing a competitive specialty. Students applying into competitive specialties felt their chances of matching changed significantly compared to students applying to other specialties (either increased (25.6% vs 10.2%, P < .001), or decreased (28.2% vs10.3%, P < .001). Majority of students (73.2%) agreed with the decision to make Step 1 pass/fail, however, students reported increased anxiety surrounding Step 2 scores. There was a significant association between which aspects students believed were most important for matching (letters of recommendation and performance on Acting Internships) and those they believed program directors would weigh heaviest (P < .001). Medical students report increased anxiety with Step 2 after Step 1 became pass/fail, although students overwhelmingly agreed with this change. To address this, medical schools should offer further guidance and resources to students, focusing on mentorship and methods to optimize residency applications for their chosen specialties.

Performance of ChatGPT on Stage 1 of the Taiwanese medical licensing exam.

Since its release by OpenAI in November 2022, numerous studies have subjected ChatGPT to various tests to evaluate its performance in medical exams. The objective of this study is to evaluate ChatGPT's accuracy and logical reasoning across all 10 subjects featured in Stage 1 of Senior Professional and Technical Examinations for Medical Doctors (SPTEMD) in Taiwan, with questions that encompass both Chinese and English. In this study, we tested ChatGPT-4 to complete SPTEMD Stage 1. The model was presented with multiple-choice questions extracted from three separate tests conducted in February 2022, July 2022, and February 2023. These questions encompass 10 subjects, namely biochemistry and molecular biology, anatomy, embryology and developmental biology, histology, physiology, microbiology and immunology, parasitology, pharmacology, pathology, and public health. Subsequently, we analyzed the model's accuracy for each subject. In all three tests, ChatGPT achieved scores surpassing the 60% passing threshold, resulting in an overall average score of 87.8%. Notably, its best performance was in biochemistry, where it garnered an average score of 93.8%. Conversely, the performance of the generative pre-trained transformer (GPT)-4 assistant on anatomy, parasitology, and embryology was not as good. In addition, its scores were highly variable in embryology and parasitology. ChatGPT has the potential to facilitate not only exam preparation but also improve the accessibility of medical education and support continuous education for medical professionals. In conclusion, this study has demonstrated ChatGPT's potential competence across various subjects within the SPTEMD Stage 1 and suggests that it could be a helpful tool for learning and exam preparation for medical students and professionals.

Are Medical School Curricula Adapting With Their Students? A Survey on How Medical Students Study and How it Relates to USMLE Step 1 Scores.

Over the years, medical schools have evolved their curricula in response to the medical field, faculty, and students. The current study aims to examine how medical students study, what resources they most commonly use, and how it relates to United States Medical Licensing Examination (USMLE) Step 1 scores. A cross-sectional survey study of United States medical students was distributed via social media, GroupMe school chats, and school listservs from September 8, 2020, to December 12, 2020. The survey gathered data including, demographic and school information, resources students, time spent using each resource, and USMLE Step 1 scores. The survey was completed by 560 students from 102 different United States medical schools. Study guides as online resources (83.2%) were mostly used, lecture (82.5%), Anki (spaced repetition flashcards, 68.3%), and school-organized sessions (workshops, labs, and small groups) (60.7%). Of the students surveyed, 90% attended schools with a recorded lecture option. Only 54% of these students watched their lectures live. When watched online, the average watching speed was 1.75 ± 0.4× with a mode of 2× speed. In examining different medical school styles, schools with a 1.5-year preclinical curriculum had higher USMLE Step 1 scores (244.5 ± 15.6 vs 236.9 ± 16.2, P-score = .024) compared to schools that did not (1- or 2-year preclinical curriculum). Medical students seem to be using third-party resources in addition to their medical school curriculums. Because students are already broadly using these, medical schools could allow their curriculums to reference, require, or complement third-party online resources. Additionally, because of their increased cost, medical schools could consider ways to alleviate the cost on medical students as a means of equitable support. Lastly, Step 1 scores significantly correlated with schools with a 1.5-year preclinical curriculum, although the exact reasoning for this remains uncertain.

Underrepresented Applicants Post-USMLE Pass/Fail: A National Survey of Competitive Residency Directors.

To report the selection criteria important to residency program directors (PDs) and whether they believe pass/fail scoring will impact underrepresented in medicine (URM), International Medical Graduate (IMG), or osteopathic (DO) residency applicants after the United States Medical Licensing Examination (USMLE) changed Step 1 score reporting to pass/fail in January 2022. A Qualtrics survey was sent between August 2022 and January 2023 to 1141 US PDs from specialties with traditionally low residency selection rates: dermatology, ophthalmology, otolaryngology, orthopedic surgery, neurosurgery, interventional radiology, diagnostic radiology, radiation oncology, thoracic surgery, vascular surgery, plastic surgery, and urology. Contact information was obtained from AMA, FRIEDA, or program websites. We received 433 responses (38%). When asked to anticipate the difficulty student groups will face matching into their specialty, PDs reported: for URM, 24.0% increased, 46.0% unchanged, and 30.0% decreased; for DO, 49.19% increased, 44.58% unchanged, and 6.23% decreased and for IMG, 56.35% increased, 39.72% unchanged, and 3.93% decreased. When asked to rank the most important selection factors, the top two responses were Step 2 CK score and away rotation participation at their site. PDs overwhelmingly believed residency selection difficulty would either increase or remain unchanged for DO (93.77%) and IMG (96.07%). In contrast, 76.0% reported difficulty for URM students would either decrease or remain unchanged. PDs ranked Step 2 CK score and away rotation participation as the most important selection factors. Despite PDs' belief that the Step 1 pass/fail scoring system may mitigate one barrier for URM students, emphasis on Step 2 CK and away rotations place additional barriers.

The Impact of Signaling on the 2022 to 2023 Orthopaedic Residency Application Cycle: A Survey of Incoming Residents.

During the 2022 to 2023 orthopaedic surgery residency application cycle, "signaling" was added, allowing applicants to communicate strong interest to 30 programs of their choosing. This study's purpose was to evaluate signaling's impact on the 2022 to 2023 application cycle. A cross-sectional survey was distributed to applicants who applied to a single residency program. We evaluated how many interviews the average applicant received, what proportion of interviews came from programs they had signaled, and what percentage of applicants matched at a program they had signaled. We stratified data by American Orthopaedic Association status, United States Medical Licensing Examination (USMLE) Step 1 and Step 2 scores, research items, sex, and race. This survey was distributed to 611 applicants, with 124 (20.3%) responding to the survey. Ninety-five respondents (76.6%) matched. The percentage of applicant interviews that came from signaled programs was 78.7%. Ninety-one percent of matched applicants matched at a program they signaled. Sixty-three percent of matched applicants performed an away rotation at their matched programs. Forty-five percent of applicants felt that signaling incentivized reducing the programs they applied to, and applicants gave signaling a high favorability rating of 4/5. Applicants with Alpha Omega Alpha status received more interviews per application (0.18 ± 0.11 vs. 0.10 ± 0.10, p < 0.001) and more interviews from programs they did not signal (74% of interviews from signaled programs vs. 90% of interviews from signaled programs, p < 0.001). Higher Step 1 and Step 2 scores were associated with more interviews per application (Step 1: 0.16 ± 0.12 vs. 0.12 ± 0.08, p = 0.032) (Step 2: 0.16 ± 0.11 vs. 0.12 ± 0.09, p = 0.032). Orthopaedic residency applicants received most of their interviews from programs that they signaled, with an overwhelming majority matching at signaled program. Alpha Omega Alpha status and high USMLE scores were associated with more interviews granted per application, regardless of signaling status. Signaling seems to be a favorable option for orthopaedic applicants. Data from future application cycles will help further evaluate signaling's impact on the orthopaedic match.

Palliative care education and knowledge transfer into practice - a multicenter survey among medical students and resident physicians in Germany using a mixed-methods design.

In 2009, Palliative care was incorporated into the medical curriculum as Cross-Sectional Subject 13 (QB13) by means of the revision of the Medical Licensing Regulations for Physicians. The aim of this study was to determine the strengths and deficits of QB13 student education for palliative care in clinical practice in a multi-centre setting and to identify potential for improvement. Online questionnaires filled out by medical students during their Practical Year (PY) and resident physicians from the university hospitals in Aachen, Düsseldorf, and Cologne were descriptively analyzed using SPSS; free-text responses were categorized and quantified. Semi-structured interviews with the resident physicians (using a mixed-methods design) were analyzed through content analysis. Emerging categories were quantified. Analysis of 130 fully completed questionnaires and 23 interviews revealed that participants particularly benefited from patient- and practice-oriented small-group sessions for their clinical work. Despite some university-specific differences, the PY students identified a need for training in end-of-life-care, while resident physicians saw a need for training primarily in dealing with patients and their relatives. They also reported deficits in transferability. QB13 should be organised in cross-university curricula and provide sufficient resources for practical-oriented small-group teaching. Based on the "unit of care", besides caring for palliative patients, dealing with patients' families should also be an education focus. To improve transferability into clinical practice, students should be actively involved in the care of palliative patients.

Subtyping of Performance Trajectory During Medical School, Medical Internship, and the First Year of Residency in Training Physicians: A Longitudinal Cohort Study.

Developmental trajectories of clinical skills in training physicians vary among tasks and show interindividual differences. This study examined the predictors of medical internship performance and residency entrance and found subtypes of performance trajectory in training physicians. This retrospective cohort study involved 888 training physicians who completed a medical internship between 2015 and 2019. After the internship, 627 physicians applied for residency training between 2016 and 2020. Finally, 160 of them completed their first-year residency in internal medicine, surgery, pediatrics, and psychiatry departments between 2016 and 2020. Pearson's correlation coefficients of internship performance and first year-residency performance (n = 160) were calculated. Latent profile analysis identified performance trajectory subtypes according to medical school grade point average (GPA), internship performance, English proficiency, and residency selection procedures. Multivariate logistic regression models of residency acceptance (n = 627) and performance in the top 30%/lower 10% in the first year of residency were also constructed. Medical internship performance showed a significant positive correlation with the medical school GPA (r = 0.194) and the written score for the medical licensing examination (r = 0.125). Higher scores in the interview (adjusted odds ratio [aOR], 2.57) and written examination (aOR, 1.45) of residency selection procedures and higher medical internship performance (aOR, 1.19) were associated with a higher chance of residency acceptance. The latent profile analyses identified three training physician subgroups: average performance, consistently high performance (top 30%), and adaptation to changes (lowest 10%). Higher scores in the interview for residency selection (aOR, 1.35) and lower scores for medical internship performance (aOR, 0.79) were associated with a higher chance of performing in the top 30% or lowest 10% in the first year of residency, respectively. Performance in the interview and medical internship predicted being among the top 30% and lowest 10% of performers in the first year of residency training, respectively. Individualized educational programs to enhance the prospect of trainees becoming high-functioning physicians are needed.

How Well Did ChatGPT Perform in Answering Questions on Different Topics in Gross Anatomy?

The burgeoning interest in leveraging ChatGPT within the medical field underscores the necessity for a comprehensive understanding of its capabilities and limitations, particularly in the context of medical assessments and examinations. The model possesses a unique aptitude for addressing queries related to medical student exams, thereby serving as an invaluable resource for academic support. Its advanced natural language processing capabilities empower it to comprehend the intricacies of medical terminology, enabling it to provide nuanced and contextually relevant responses. This study aimed to quantitatively evaluate ChatGPT performance in answering Multiple Choice Questions (MCQs) related to the different topics in Gross Anatomy course for medical students. The research conducted for this study was focused on a comprehensive examination of ChatGPT (GPT-3.5) capabilities in answering 325 MCQs designed in USMLE style, arranged in 7 different sets related to specific topics. These questions were selected from Gross Anatomy course exam database for medical students and reviewed by three independent experts. The results of 5 successive attempts to answer each set of questions by Chat-GPT were evaluated based on accuracy, relevance, and comprehensiveness. The ChatGPT provided accurate answers to 44.1% ± 8.2% of questions. Accordingly, to our data, ChatGPT is answering much better on MCQs from Back material (58.4%), following Head and Neck (48.8%) and Pelvis (45.6%), and performing not so well in questions of Thorax (37.6%) and Upper limb (36.4%). ChatGPT is struggling in answering questions about blood supply and innervation of the specific organs. ChatGPT stands out as a promising and interactive educational tool, particularly for students engaged in the study of anatomy. Its distinctive ability to not only provide informative responses but also engage students in a conversational manner is highly commendable. This quality has the potential to enhance student engagement and foster curiosity, creating a dynamic learning experience. However, it’s crucial to acknowledge that ChatGPT’s current level of comprehension and interpretative abilities may not meet the demanding standards required for practical applications in the medical education domain. Its performance in challenging examinations like medical college exams and health licensing exams might need to catch up to expectations.

ChatGPT-4 Performance on USMLE Step 1 Style Questions and Its Implications for Medical Education: A Comparative Study Across Systems and Disciplines.

We assessed the performance of OpenAI's ChatGPT-4 on United States Medical Licensing Exam STEP 1 style questions across the systems and disciplines appearing on the examination. ChatGPT-4 answered 86% of the 1300 questions accurately, exceeding the estimated passing score of 60% with no significant differences in performance across clinical domains. Findings demonstrated an improvement over earlier models as well as consistent performance in topics ranging from complex biological processes to ethical considerations in patient care. Its proficiency provides support for the use of artificial intelligence (AI) as an interactive learning tool and furthermore raises questions about how the technology can be used to educate students in the preclinical component of their medical education. The authors provide an example and discuss how students can leverage AI to receive real-time analogies and explanations tailored to their desired level of education. An appropriate application of this technology potentially enables enhancement of learning outcomes for medical students in the preclinical component of their education.

Application of artificial intelligence chatbots, including ChatGPT, in education, scholarly work, programming, and content generation and its prospects: a narrative review

This study aims to explore ChatGPT’s (GPT-3.5 version) functionalities, including reinforcement learning, diverse applications, and limitations. ChatGPT is an artificial intelligence (AI) chatbot powered by OpenAI’s Generative Pre-trained Transformer (GPT) model. The chatbot’s applications span education, programming, content generation, and more, demonstrating its versatility. ChatGPT can improve education by creating assignments and offering personalized feedback, as shown by its notable performance in medical exams and the United States Medical Licensing Exam. However, concerns include plagiarism, reliability, and educational disparities. It aids in various research tasks, from design to writing, and has shown proficiency in summarizing and suggesting titles. Its use in scientific writing and language translation is promising, but professional oversight is needed for accuracy and originality. It assists in programming tasks like writing code, debugging, and guiding installation and updates. It offers diverse applications, from cheering up individuals to generating creative content like essays, news articles, and business plans. Unlike search engines, ChatGPT provides interactive, generative responses and understands context, making it more akin to human conversation, in contrast to conventional search engines’ keyword-based, non-interactive nature. ChatGPT has limitations, such as potential bias, dependence on outdated data, and revenue generation challenges. Nonetheless, ChatGPT is considered to be a transformative AI tool poised to redefine the future of generative technology. In conclusion, advancements in AI, such as ChatGPT, are altering how knowledge is acquired and applied, marking a shift from search engines to creativity engines. This transformation highlights the increasing importance of AI literacy and the ability to effectively utilize AI in various domains of life.

Impact of USMLE Step 1 Transition to Pass/Fail Scoring System on Medical Students' Anxiety, Sleep Quality, and Burnout

BACKGROUND: Originally designed to evaluate the application of foundational scientific knowledge in clinical contexts, the United States Medical License Exam Step 1 evolved into a comparative tool for assessing candidates' educational foundations. This transition heightened the pressure on medical students to excel in the exam. In response, collaborative efforts involving the National Board of Medical Examiners prompted a significant change from reporting scores to a pass/fail system. However, the true impact of this shift remains insufficiently explored. This study aims to assess the emotional toll - encompassing burnout, anxiety, depression, and sleep quality - experienced prior to taking the Step 1 exam. Additionally, it aims to uncover potential gender-based disparities in perceived stress, anxiety, and depression. METHODS: The study encompasses the entirety of third-year medical students at Drexel University College of Medicine, who were invited to participate in a comprehensive survey. Drawing from retrospection, the survey relies on self-reported data regarding anxiety, depression, sleep quality index, and burnout levels. Data compilation was anonymized and executed through the Qualtrics platform. RESULTS: A total of 102 medical students completed the survey, with a 97% pass rate for the USMLE Step 1. Despite their excellent performance, 75% of students reported inadequate sleep quality, and 68% exhibited mild to moderate anxiety levels. Among them, a higher percentage of females (83%) experienced mild to moderate anxiety compared to their male counterparts (50%). Furthermore, 66% of students felt that their commitment to education exceeded what was reasonable for their well-being. CONCLUSION: The transition from traditional scoring to a pass/fail system was ostensibly intended to enhance the mental well-being of medical students. Nevertheless, our findings underscore that students continue to grapple with heightened levels of stress, anxiety, and burnout during the pivotal month leading up to the Step 1 examination.

The Carolina hysterectomy cohort (CHC): a novel case series of reproductive-aged hysterectomy patients across 10 hospitals in the US south

BackgroundHysterectomy is a common surgery among reproductive-aged U.S. patients, with rates highest among Black patients in the South. There is limited insight on causes of these racial differences. In the U.S., electronic medical records (EMR) data can offer richer detail on factors driving surgical decision-making among reproductive-aged populations than insurance claims-based data. Our objective in this cohort profile paper is to describe the Carolina Hysterectomy Cohort (CHC), a large EMR-based case-series of premenopausal hysterectomy patients in the U.S. South, supplemented with census and surgeon licensing data. To demonstrate one strength of the data, we evaluate whether patient and surgeon characteristics differ by insurance payor type.MethodsWe used structured and abstracted EMR data to identify and characterize patients aged 18–44 years who received hysterectomies for non-cancerous conditions between 10/02/2014–12/31/2017 in a large health care system comprised of 10 hospitals in North Carolina. We used Chi-squared and Kruskal Wallis tests to compare whether patients’ socio-demographic and relevant clinical characteristics, and surgeon characteristics differed by patient insurance payor (public, private, uninsured).ResultsOf 1857 patients (including 55% non-Hispanic White, 30% non-Hispanic Black, 9% Hispanic), 75% were privately-insured, 17% were publicly-insured, and 7% were uninsured. Menorrhagia was more prevalent among the publicly-insured (74% vs 68% overall). Fibroids were more prevalent among the privately-insured (62%) and the uninsured (68%). Most privately insured patients were treated at non-academic hospitals (65%) whereas most publicly insured and uninsured patients were treated at academic centers (66 and 86%, respectively). Publicly insured and uninsured patients had higher median bleeding (public: 7.0, uninsured: 9.0, private: 5.0) and pain (public: 6.0, uninsured: 6.0, private: 3.0) symptom scores than the privately insured. There were no statistical differences in surgeon characteristics by payor groups.ConclusionThis novel study design, a large EMR-based case series of hysterectomies linked to physician licensing data and manually abstracted data from unstructured clinical notes, enabled identification and characterization of a diverse reproductive-aged patient population more comprehensively than claims data would allow. In subsequent phases of this research, the CHC will leverage these rich clinical data to investigate multilevel drivers of hysterectomy in an ethnoracially, economically, and clinically diverse series of hysterectomy patients.

ChatGPT's performance in German OB/GYN exams - paving the way for AI-enhanced medical education and clinical practice.

Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial learning and large language model tool developed by OpenAI in 2022. It utilizes deep learning algorithms to process natural language and generate responses, which renders it suitable for conversational interfaces. ChatGPT's potential to transform medical education and clinical practice is currently being explored, but its capabilities and limitations in this domain remain incompletely investigated. The present study aimed to assess ChatGPT's performance in medical knowledge competency for problem assessment in obstetrics and gynecology (OB/GYN). Two datasets were established for analysis: questions (1) from OB/GYN course exams at a German university hospital and (2) from the German medical state licensing exams. In order to assess ChatGPT's performance, questions were entered into the chat interface, and responses were documented. A quantitative analysis compared ChatGPT's accuracy with that of medical students for different levels of difficulty and types of questions. Additionally, a qualitative analysis assessed the quality of ChatGPT's responses regarding ease of understanding, conciseness, accuracy, completeness, and relevance. Non-obvious insights generated by ChatGPT were evaluated, and a density index of insights was established in order to quantify the tool's ability to provide students with relevant and concise medical knowledge. ChatGPT demonstrated consistent and comparable performance across both datasets. It provided correct responses at a rate comparable with that of medical students, thereby indicating its ability to handle a diverse spectrum of questions ranging from general knowledge to complex clinical case presentations. The tool's accuracy was partly affected by question difficulty in the medical state exam dataset. Our qualitative assessment revealed that ChatGPT provided mostly accurate, complete, and relevant answers. ChatGPT additionally provided many non-obvious insights, especially in correctly answered questions, which indicates its potential for enhancing autonomous medical learning. ChatGPT has promise as a supplementary tool in medical education and clinical practice. Its ability to provide accurate and insightful responses showcases its adaptability to complex clinical scenarios. As AI technologies continue to evolve, ChatGPT and similar tools may contribute to more efficient and personalized learning experiences and assistance for health care providers.

Pediatric residency milestone performance is not predicted by the United States Medical Licensing Examination Step 2 Clinical Knowledge

Objectives: This study aims to show whether correlation exists between pediatric residency applicants’ quantitative scores on the United States Medical Licensing Exam Step 2 Clinical Knowledge examination and their subsequent performance in residency training based on the Accreditation Council for Graduate Medical Education Milestones, which are competency-based assessments that aim to determine residents’ ability to work unsupervised after postgraduate training. No previous literature has correlated Step 2 Clinical Knowledge scores with pediatric residency performance assessed by Milestones. Methods: In this retrospective cohort study, the United States Medical Licensing Exam Step 2 Clinical Knowledge Scores and Milestones data were collected from all 188 residents enrolled in a single categorical pediatric residency program from 2012 - 2017. Pearson correlation coefficients were calculated amongst available test and milestone data points to determine correlation between test scores and clinical performance. Results: Using Pearson correlation coefficients, no significant correlation was found between quantitative scores on the Step 2 Clinical Knowledge exam and average Milestones ratings (r = -0.1 for post-graduate year 1 residents and r = 0.25 for post-graduate year 3 residents). Conclusions: These results demonstrate that Step 2 scores have no correlation to success in residency training as measured by progression along competency-based Milestones. This information should limit the importance residency programs place on quantitative Step 2 scores in their ranking of residency applicants. Future studies should include multiple residency programs across multiple specialties to help make these findings more generalizable.

Child Neurology and Neurodevelopmental Disabilities Program Directors’ Opinions on Preference Signaling in the 2023-2024 NRMP Match: A Survey

Changes in residency recruitment have significantly altered how programs and applicants evaluate each other including virtual interviews, discontinuation of the United States Medical Licensing Exam Step 2 Clinical Skills exam, and transition of United States Medical Licensing Exam Step 1 to pass-fail scoring. To improve program-applicant fit, the Electronic Residency Application Service introduced supplemental application features including geographic preference, program signaling, and the opportunity to highlight impactful and meaningful experiences. We sought to evaluate child neurology (CN) and neurodevelopmental disabilities (NDD) program director's (PD) opinions regarding these changes. A 10-question anonymous survey was sent to CN (n=75) and NDD (n=8) PDs. The questions centered on PDs' opinions regarding components of the supplemental application, having a standard application review period and in-person recruitment activities. Answer choices to the questions were all close-ended. Respondents could select questions to complete. Thirty-eight CN residency PDs (49%) and 4 NDD residency PDs (50%) responded to the survey. Among CN PDs, there was strong support for use of the supplemental application questions and for the use of 3 program signals per applicant. Most PDs supported a standardized application review period prior to programs sending interview offers; however, there was no consensus on the appropriate length of time. Nearly half agreed with virtual-only interviews, and 62% agreed with the option of in-person second-look visits. CN PDs generally support many of the recent or proposed changes to residency recruitment. The impact of these changes on recruitment will be a topic of future investigation.

Patient Perspectives on Selecting an Academic Aesthetic Surgeon: A Qualitative Analysis.

Growth of the aesthetic surgery marketplace has increased patient choice in provider selection. This study aimed to characterize how patients choose an aesthetic surgeon, identify knowledge gaps in this decision-making process, and understand why patients select academic aesthetic surgeons. A qualitative interview study of aesthetic surgery patients from an academic center was conducted. Purposive sampling maximized representation regarding surgeon, surgery type, and patient demographics. An interview guide was developed in collaboration with content and methodology experts, then refined through pilot testing. Emergent themes were identified using a codebook constructed by grounded theory. Thematic saturation was achieved with 24 patients. When selecting a surgeon, participants valued bedside manner (24 of 24) and past patients' satisfaction (18 of 24). Most participants (16 of 24) ascribed low importance to board certification. Reasons given for choosing an academic practice included the institution's reputation (13 of 24) and the availability of medical records and other specialties if complications arise (8 of 24). Participants demonstrated knowledge gaps regarding medical training and licensure. No participant (0 of 24) was aware that any licensed physician can offer aesthetic surgery, and nearly all participants (23 of 24) expressed discomfort with this. Patients prioritize subjective elements when selecting an aesthetic surgeon, relying less on objective and meaningful qualifications like board certification and training background. Academic aesthetic practice is valued because of reputation and ability to function as a medical home. Given the lack of public understanding regarding physician training, initiatives promoting transparency are needed to ensure that patients can make safe, informed decisions.