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OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

America is facing a primary care provider shortage. Enhanced primary care RNs (EPC‐RNs), registered nurses working at the top of their practice scope, can be an important part of community‐based primary care provision. Typically, community health nursing education has not included primary care‐based clinical experiences, and there is a clinical site shortage. Teaching students about EPC‐RN roles can be an important domain of community/public health nursing education. Simulation is a creative pedagogical approach to learning about the EPC‐RN role. The purpose of this manuscript is to 1) describe EPC‐RN Telehealth Simulation development; and 2) describe student simulation feedback. Nursing faculty and simulation experts designed five simulations for community health students to teach about the EPC‐RN role, incorporating recommendations from the Quad Council Coalition of Public Health Nursing Organizations community/public health nursing competencies and the National Competencies for Registered Nurses in Primary Care. Retrospective, descriptive quantitative, and qualitative student feedback data (n = 519) was collected through QuestionPro over multiple semesters. Student feedback data demonstrated support of the use of the simulations in community/public health nursing education. The EPC‐RN Telehealth Simulations prepared pre‐licensure nursing students to practice in community‐based primary care settings. Innovative, sustainable experiences such as this can improve community/public health nursing education. [ABSTRACT FROM AUTHOR]

Introduction: In the past year, the use of large language models (LLMs) has generated significant interest and excitement because of their potential to revolutionise various fields, including medical education for aspiring physicians. Although medical students undergo a demanding educational process to become competent health care professionals, the emergence of LLMs presents a promising solution to challenges like information overload, time constraints and pressure on clinical educators. However, integrating LLMs into medical education raises critical concerns and challenges for educators, professionals and students. This systematic review aims to explore LLM applications in medical education, specifically their impact on medical students' learning experiences. Methods: A systematic search was performed in PubMed, Web of Science and Embase for articles discussing the applications of LLMs in medical education using selected keywords related to LLMs and medical education, from the time of ChatGPT's debut until February 2024. Only articles available in full text or English were reviewed. The credibility of each study was critically appraised by two independent reviewers. Results: The systematic review identified 166 studies, of which 40 were found by review to be relevant to the study. Among the 40 relevant studies, key themes included LLM capabilities, benefits such as personalised learning and challenges regarding content accuracy. Importantly, 42.5% of these studies specifically evaluated LLMs in a novel way, including ChatGPT, in contexts such as medical exams and clinical/biomedical information, highlighting their potential in replicating human‐level performance in medical knowledge. The remaining studies broadly discussed the prospective role of LLMs in medical education, reflecting a keen interest in their future potential despite current constraints. Conclusions: The responsible implementation of LLMs in medical education offers a promising opportunity to enhance learning experiences. However, ensuring information accuracy, emphasising skill‐building and maintaining ethical safeguards are crucial. Continuous critical evaluation and interdisciplinary collaboration are essential for the appropriate integration of LLMs in medical education. Addressing the hype around incorporating large language models and ChatGPT into medical education, Robinson et al. outline how to do it right and wrong. [ABSTRACT FROM AUTHOR]

Historically, community health nursing education has not encompassed clinical sites in primary care. Primary care can be an important domain of community health nursing education. However, student practicum opportunities are limited by the number of and underutilization of RNs practicing at the full scope of their licensure (including assessment, client education, care planning and evaluation of care interventions) who can serve as student preceptors, especially in rural areas. This article describes the creation and implementation of the Enhanced Primary Care Registered Nurse (EPCRN) role in rural primary care clinics, as well as evaluates student perceptions of the EPCRN-precepted clinical experience. One nursing school used a federal training award to create the role of Enhanced Primary Care Registered Nurses (EPCRNs) to practice in federally-designated Rural Health Clinics. The EPCRNs worked in the Rural Health Clinics performing patient care and also functioned as student preceptors. Student experiences were evaluated through quantitative and qualitative methods, namely the Clinical Learning Experience, Supervision, and Nurse Teacher (CLES+T) scale and focus groups. This pilot project demonstrated positive pre-licensure student experience feedback as well as role value and sustainability for the health system. This pilot served as an example of a process for EPCRN role design within a primary care clinic site. It also demonstrated the importance of innovative, sustainable academic-practice partnerships. [ABSTRACT FROM AUTHOR]

Objective Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients. Materials and Methods We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes. Results The Peds-Phecodes aggregate 15 533 ICD-9-CM codes and 82 949 ICD-10-CM codes into 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P  < .001). Discussion We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations. Conclusion Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes. [ABSTRACT FROM AUTHOR]