Advances in Chronic Kidney Disease

An integral part of a physician's practice includes being a leader, especially as there is a strong need for skilled leaders to advocate and navigate patient-centered and organizational outcomes. Nephrologists undertake multiple leadership roles, but dedicated leadership training is lacking in medical and postgraduate education. Given the growing need for physician leaders, practitioners in nephrology and beyond must become better equipped in understanding the role of leadership skills in medical practice. Nephrology and the medical community as a whole should focus on intentional and dedicated leadership in medical education training to better groom physicians for leadership roles...

There is no consistent educational model to introduce the physician assistant and/or nurse practitioner to nephrology. The job descriptions of the nephrology physician assistant/nurse practitioner may be similar, but the training, state and federal licensing, background, and recertification are different for the 2 professions adding a level of complexity to the training of the physician assistant/nurse practitioner new to nephrology. On-the-job training is the most common modality, but formats, content, mentors, and practices vary from organization to organization and even within organizations...

Evaluations serve as the backbone of any educational program and can be broadly divided into formative and summative evaluations. Formative evaluations are "just in time" evaluations focused on informing the learning process, whereas summative evaluations compare fellows to a preset standard to determine their readiness for unsupervised practice. In the nephrology fellowship programs, evaluations assess competence in the framework of ACGME Milestones 2.0. A variety of learning venues, evaluators, and tools should be incorporated into the measurement process...

Kidney pathology education is a critical component in training of nephrology fellows, as well as for continuing medical education for practicing nephrologists. Kidney pathology images are included on nephrology fellow board exams, and clinicopathologic correlation of kidney biopsy findings is critical in everyday clinical practice. Nephropathology training is a requirement by the American College of Graduate Medical Education within nephrology fellowship curricula. However, greater than one-third of fellowship program directors believe that nephropathology training for their fellows is not sufficient...

Nephrologists have a significant role in educating and mentoring trainees. They are considered role models and a major reason for fellows to be attracted to the specialty. Nephrology training programs not only support fellows in their teaching endeavors but also provide them with the necessary knowledge and skills required for advancing their careers as clinician educators. However, such career development tracks are limited in number and most focus on early career faculty. Here we present an overview of the various teaching opportunities for fellows at the University of North Carolina (UNC) Nephrology fellowship program and the development of a fellow-oriented clinician educator track...

Nephrology education has changed significantly since the first nephrology fellowship programs were established in the United States in the 1950s and 1960s. The past several years have seen increased opportunities for subspecialization in areas such as interventional nephrology, onconephrology, and glomerular disease. Notable trends in fellowship curricula include the expansion of education in home dialysis and palliative care, driven by policy changes and focus on patient-centered care. In addition, point-of-care ultrasound has garnered significant interest due to its potential to provide diagnostic information that improves patient care...

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Dwindling interest in nephrology amid a growing prevalence of kidney disease has inspired nephrologists to create educational initiatives for trainees. Engagement at all levels is crucial to fostering interest in the field, and experience for internal medicine residents has been a significant area for growth. In this article, we review the literature on available educational programs at the residency level. These interventions were largely met with high trainee satisfaction and positive feedback, particularly when designed with the goal to create superb internists rather than future nephrologists...

Addressing persistent racial health disparities in cases of kidney disease will first require significant investment in examining how structural racism has influenced our clinical practice and medical education. Improving how we understand and articulate race is critical for achieving this goal. This work begins with ensuring that race's mention within nephrology literature and curricular materials for medical trainees is thoroughly rooted in evidence-based rationale-not to serve as a proxy for polygenic contributions, social determinants of health, or systemic health care barriers...

Although medical schools across the United States have updated their curricula to incorporate active learning techniques, there has been little discussion on the nature of the content presented to students. Here, we share detailed examples of our experience in using original experiments to lay the groundwork for foundational concepts in renal physiology and pathophysiology. We believe that this approach offers distinct advantages over standard case-based teaching by (1) starting with simple concepts, (2) analyzing memorable visuals, (3) increasing graphical literacy, (4) translating observations to "rules," (5) encouraging critical thinking, and (6) providing historical perspective to the study of medicine...

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We reviewed some of the latest advancements in the use of mathematical models in nephrology. We looked over 2 distinct categories of mathematical models that are widely used in biological research and pointed out some of their strengths and weaknesses when applied to health care, especially in the context of nephrology. A mechanistic dynamical system allows the representation of causal relations among the system variables but with a more complex and longer development/implementation phase. Artificial intelligence/machine learning provides predictive tools that allow identifying correlative patterns in large data sets, but they are usually harder-to-interpret black boxes...

Unstructured data in the electronic health records contain essential patient information. Natural language processing (NLP), teaching a computer to read, allows us to tap into these data without needing the time and effort of manual chart abstraction. The core first step for all NLP algorithms is preprocessing the text to identify the core words that differentiate the text while filtering out the noise. Traditional NLP uses a rule-based approach, applying grammatical rules to infer meaning from the text. Newer NLP approaches use machine learning/deep learning which can infer meaning without explicitly being programmed...

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The use of artificial intelligence (AI) in nephrology and its associated clinical research is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques...

Continuous renal replacement therapy (CRRT) is widely utilized to support critically ill patients with acute kidney injury. Artificial intelligence (AI) has the potential to enhance CRRT delivery, but evidence is limited. We reviewed existing literature on the utilization of AI in CRRT with the objective of identifying current gaps in evidence and research considerations. We conducted a scoping review focusing on the development or use of AI-based tools in patients receiving CRRT. Ten papers were identified; 6 of 10 (60%) published in 2021, and 6 of 10 (60%) focused on machine learning models to augment CRRT delivery...

Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains...

Detecting protected health information in electronic health record systems is often an early step in health care analytics, and it is a nontrivial problem. Specific challenges include finding clinician names and diseases, which lack a fixed format and are often context-dependent. The general problem of finding entities, termed named-entity recognition, has received a substantial amount of attention in the natural language processing and deep learning communities. This paper begins by outlining recent methods for finding protected health information, and it then introduces a hybrid system which combines regular expressions with a natural language processing framework called FLAIR...

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