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Background: Rapid changes in the health care marketplace are driving health care systems to modify operations by which the advanced practice registered nurse (APRN) and physician assistant (PA) clinicians serve patients. By identifying more effective and efficient utilization workflows, organizations can meet these demands resulting in high-functioning clinical teams.
Local Problem: With the growing number of APRNs and PAs within a large academic medical center, there was a recognized need to establish effective and efficient utilization practices for these health care providers.
Methods: Directors of the Advanced Practice Provider Best Practice Center developed an internal nurse practitioner (NP)/certified nurse midwife (CNM)/clinical nurse specialist (CNS)/PA assessment service in which evaluations were conducted to optimize APRN and PA practice at the clinical/department level. This assessment excluded certified registered nurse anesthetists.
Interventions: Thirty-two clinical areas were evaluated between September 2016 and May 2019. This included an NP/CNM/CNS/PA survey and over 200 individual NP/CNM/CNS and PA provider interviews. Assessments addressed utilization, billing practices, professional development, and communication among team members.
Results: Qualitative and quantitative reports were compiled. Many common themes were identified. These were broken down into three major categories: productivity, clinical operations, and professional development/support.
Conclusions: Several recommendations were presented to department leaders regarding NP/CNM/CNS/PA practice. Those departments who implemented several of the recommendations showed positive outcomes. This was evidenced by increased financial gain (increased relative value units, increase in revenue generated), increased patient access (increased clinic densities), and overall NP/CNM/CNS/PA satisfaction.
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http://dx.doi.org/10.1097/JXX.0000000000000415 | DOI Listing |
Interv Neuroradiol
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Department of Neurosurgery, Shinshu University School of Medicine, Matsumoto, Japan.
BackgroundA stable guiding system is essential for successful carotid artery stenting (CAS), particularly when navigating tortuous aortic or supra-aortic anatomy. However, data on the mechanical behavior of stent delivery systems remain scarce.ObjectiveTo assess and compare the bending stiffness and trackability of five commercially available carotid stent delivery systems using bench-top experiments.
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CIISA - Centre for Interdisciplinary Research in Animal Health, Faculty of Veterinary Medicine, University of Lisbon, Lisbon, Portugal.
African Swine Fever (ASF), caused by the highly contagious African swine fever virus (ASFV), poses a significant threat to domestic and wild pigs worldwide. Despite its limited host range and lack of zoonotic potential, ASF has severe socio-economic and environmental consequences. Current control strategies primarily rely on early detection and culling of infected animals, but these measures are insufficient given the rapid spread of the disease.
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Key Laboratory of Bio-based Material Science and Technology of Ministry of Education, Northeast Forestry University, Harbin, P. R. China.
Rapid advancement of flexible electronics has generated a demand for sustainable materials. Cellulose, a renewable biopolymer, exhibits exceptional mechanical strength, customizable properties, biodegradability, and biocompatibility. These attributes are largely due to its hierarchical nanostructures and modifiable surface chemistry.
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School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Biobanking in China has seen rapid development, placing the country as a key player globally. However, significant ethical challenges arise, particularly around donor autonomy in informed consent for collecting and using biological materials and personal data. This study examines how Chinese biobanks inform donors about their participation and the ethical content of consent documents.
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September 2025
Department of Computer Science, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging.
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