Grady Memorial Hospital

The US faces a critical nursing faculty shortage, thus limiting enrollment capacity despite rising demand for registered nurses in health care. Advanced practice registered nurses (APRNs) represent a vital solution, bringing valuable clinical expertise to academia. However, transitioning from clinical practice to academic roles presents unique challenges, including unfamiliar pedagogical competencies, shifts in role expectations, and establishment of a scholarly identity. This article provides a comprehensive, evidence-based guide for APRNs transitioning to faculty positions, addressing common challenges and offering practical strategies for success. A proposed four-phase model-Preparation, Engagement, Integration, and Sustainability (PEIS)-guides systematic transition through self-assessment, mentorship, professional development, and institutional support, ultimately strengthening nursing education capacity and sustainability.

Chief nurse executives (CNEs) face unprecedented moral and ethical challenges navigating tensions between professional obligations, organizational demands, and personal values. This article distinguishes morality, individual conscience, and values from ethics, formal codes and professional standards, and examines how misalignment between these domains generates moral distress. Without intervention, accumulated distress produces moral residue, triggering a crescendo effect that may culminate in moral injury and professional departure. The article addresses organizational culture evaluation, economic consequences of ethical compromise, and the critical need for explicit institutional support of the CNE. Evidence-based resources, including the Rushton Moral Resilience Scale, ANA Code of Ethics, and Beauchamp-Childress "principlist framework" are identified to provide practical guidance for sustaining integrity.

BackgroundArtificial intelligence (AI) tools are widely and freely available for clinical use. Understanding hospitalists’ real-world adoption patterns in the absence of organizational endorsement is essential for health care institutions to develop governance frameworks and optimize AI integration.ObjectiveThe objective of this study was to investigate hospitalists’ use of AI, examining the AI platforms being used, frequency of use, and clinical contexts of application. We hypothesized that AI use is more common among younger, less experienced hospitalists, albeit at an overall low frequency.MethodsAn anonymous online survey was distributed via email to all 70 hospitalists (physicians, nurse practitioners, and physician assistants) providing direct patient care at a large urban academic tertiary care hospital. Demographic data, the AI platform used (if any), the purpose for AI use, and the frequency of use information were collected. The CHERRIES (Checklist for Reporting Results of Internet E-Surveys) checklist was used for creating, testing, administering, and reporting the results of the survey. Chi-square test was used where possible; when expected cell values were low, the Fisher's exact test was used instead. The Friedman test and the pairwise Wilcoxon signed-rank test were used for analyzing the differences in the frequency of AI use for various tasks. Likert-scale responses to frequency questions (never, rarely, sometimes, often, and always) were converted to ordinal values (1-5, respectively) to facilitate analysis.ResultsOf the 70 providers, 54 (77.1%) responded to the survey. No significant differences in AI usage were observed across shift type, years of practice, time allocation to hospitalist duties, sex, age, or provider designation, contrary to our hypothesis. Overall, 36 of 54 respondents (66.7%; 95% CI 53.4%-77.8%) reported using AI in clinical practice. OpenEvidence was the most used platform (28/54, 51.9%), far exceeding general-purpose tools like OpenAI’s ChatGPT (4/54, 7.4%), suggesting a preference for medical-specific platforms. Among nonusers, primary concerns were AI accuracy and preference for established resources. The most common application was answering miscellaneous clinical questions (32/36, 88.9%), generating differential diagnoses (31/36, 86.1%), and determining management options (31/36, 86.1%), with much lower use for patient education materials (16/36, 44.4%). There was a statistically significant difference in the frequency of AI use across these clinical scenarios (Friedman test χ24 37.6; P<.001). Pairwise comparisons using the Wilcoxon signed-rank test revealed significant differences between use for answering miscellaneous questions and confirming suspected diagnosis (P=.003) and generating patient education materials (P=.004), respectively. Most respondents reported using AI for under 25% of clinical encounters across all use cases.ConclusionsTwo-thirds of hospitalists organically adopted AI despite the absence of institutional oversight. AI is predominantly used as a supplementary decision support tool, with a preference for a medical-specific platform. Health care institutions must develop governance frameworks, validation protocols, and educational initiatives to ensure safe and effective AI deployment in clinical practice.

Background and ObjectivesEntrustable professional activities (EPAs) have recently been defined for neurocritical care (NCC) advanced practice providers (APPs). There is no available assessment measure for APPs in neurology or NCC with supporting validity evidence. We aimed to assess the relationship between supervisor-assessed entrustment, performance in simulated medical and neurologic emergencies, and self-assessed entrustment among NCC APPs.MethodsThis was a simulation quality improvement study. Participants were NCC APPs at 5 academic medical centers in the United States between April and November 2024. Participants completed 2 simulated scenarios designed to assess performance in the management of medical and neurologic emergencies. The primary outcome was the significance of the relationship between checklist-based performance in simulated scenarios and supervisor-assessed entrustment among NCC APPs. Secondary outcomes were the agreement between supervisor-assessed and self-assessed entrustment among NCC APPs.ResultsThere was a significant positive association between physician supervisor–assessed entrustment and performance on the critical action checklist (rs = 0.57, p = 0.002). There was no significant correlation between performance in the simulated scenarios and APP supervisor–assessed entrustment (rs = −0.02, p = 0.952). There was no difference between overall EPA physician supervisor assessment, APP supervisor assessment, and self-assessment (5-point entrustment-supervision scale median [interquartile range (IQR)], 4 [3–5] vs 4 [4–5] vs 4 [4–4], p = 0.598). There was substantial agreement in 490 discreet physician and self-assessed EPAs (κw = 0.62). Among 322 individual EPAs that were self-assessed and assessed by an APP supervisor, there was fair agreement (κw = 0.38). There was a significant positive association between physician supervisor–assessed entrustment and participant NCC experience (rs = 0.85, p < 0.001).DiscussionPhysician supervisor EPA-based assessment was positively correlated with NCC APP performance in 2 simulated neurologic and medical emergencies, providing validity evidence for the use of EPA-based assessments as a component of competency-based medical education. There was substantial agreement between physician supervisor and APP self-assessment of EPA-based entrustment, highlighting the potential to incorporate self-assessment in the planning of NCC APP onboarding and ongoing educational curricula.

Abstract Purpose of Review Understanding the pathophysiology and risk factors for developing abdominal compartment syndrome is paramount to early diagnosis and management. Recent Findings Continuous pressure monitoring and drainage with percutaneous catheter decompression (PCD) can potentially avoid open surgery and improve survival in patients with intraabdominal hypertension (IAH). Future research will elucidate these findings. Summary Abdominal compartment syndrome (ACS) occurs when there is end organ dysfunction because of increased intra-abdominal pressure (IAP). Although this increase in pressure can be caused by an increase in extraluminal contents of the abdomen it is most frequently the result of systemic illnesses that necessitate massive fluid resuscitation. This leads to increased bowel edema and abdominal contents while also decreasing abdominal wall compliance. No matter what the etiology of ACS, it can lead to organ failure and death. Early recognition of ACS is necessary because early treatment is the best predictor of decreased morbidity and mortality. Clinical Trial Number Not applicable.

Peripheral artery disease (PAD) is a global health challenge. Advances in artificial intelligence (AI), such as large language models (LLMs) and chain-of-thought (CoT) reasoning, offer novel approaches for clinical recommendations. This study compared the readability and guideline adherence of responses from physicians and AI for a standardized PAD case. This cross-sectional study gathered responses from 30 specialized physicians (11 cardiologists, 19 vascular surgeons) across seven Latin American countries and 13 LLM systems (10 standard, three CoT). Both groups addressed diagnosis, treatment, risks, and prognosis; LLMs responded as vascular specialists. Responses were blindly evaluated with five validated Spanish readability indices and compared to the 2024 ACC/AHA multisocietal PAD guideline. Three experts scored guideline adherence; nonparametric tests were applied. Guideline adherence did not differ significantly between physicians (median 5.8 [3.4-7.6]) and LLMs (7.3 [4.7-9.7], p = 0.169), though CoT-LLMs achieved the highest scores (9.7 [8.5-11.0]). LLMs more often recommended supervised exercise (84.6% vs 30.0%, p = 0.002) and revascularization for quality of life (69.2% vs 20.0%, p = 0.004), whereas physicians favored cilostazol (60.0% vs 30.8%, p = 0.104). LLM responses had lower Readability μ values (46.9 vs 51.4, p = 0.012). Inter-rater reliability was highest for CoT-LLMs (intraclass correlation coefficient [ICC] = 0.98) versus physicians (ICC = 0.76). LLM showed comparable guideline adherence to physicians although CoT models achieved the highest scores. The difference in physician and AI treatment preferences suggest the potential of AI as adjunct clinical tools and warrants further study.

Several risk assessment models (RAMs) guide standardized prophylaxis to prevent venous thromboembolism (VTE) in trauma patients. The Caprini, Trauma Embolic Scoring System (TESS), and Greenfield Risk Assessment Profile (RAP) have been validated individually, but their predictive powers have not been directly compared in the general trauma population. This study evaluated the discriminatory ability of these three RAMs in trauma patients at an ACS-verified Level I Trauma Center. A retrospective review was performed of adult trauma patients in a single institution over one year. Demographic and clinical data were used to calculate Caprini, TESS, and RAP scores. The primary outcome was inpatient VTE. Logistic regression models - both combined and separate - generated receiver operating characteristic (ROC) curves for each score. Caprini served as the reference for comparing discriminatory ability. Among 1,276 patients, 33 (2.6%) developed inpatient VTE. Caprini, TESS, and RAP scores predicted VTE with odds ratios of 1.07 (95% CI 1.04-1.10), 1.39 (95% CI 1.23-1.56), and 1.20 (95% CI 1.12-1.29), respectively. ROC c-statistics were similar: Caprini 0.75 (95% CI 0.68-0.82), TESS 0.73 (95% CI 0.64-0.83), and RAP 0.70 (95% CI 0.60-0.79). Caprini, TESS, and RAP RAMs showed comparable moderate discriminatory ability (c-statistic > 0.70) in predicting inpatient VTE among trauma patients. No model was superior, suggesting any of these RAMs may guide standardized VTE prophylaxis in this population.