Mistake Based Learning In Cardiology

Medical errors are one of the leading causes of death. Deliver the highest quality care to your patients by recognizing and minimizing common mistakes. Providing quality care free of clinical errors isn't just a matter of knowing what to do in any given situation-it's about actively knowing what not to do. Mistake-Based Learning in Cardiology: Avoiding Medical Errors provides healthcare professionals with a summary of the common ways to inadvertently cause medical errors for each major clinical action. This resource also provides valuable information on why the mistake may be made and openly discusses medical errors to facilitate growth, learning, and psychological safety in today's workplace. Identifies the most common errors associated with each disease and clinical action. Dissects each mistake into potential reasoning errors and pinpoints the major clinical principles related to the error. Helps you understand why the mistake was made and how to avoid similar mistakes, empowering you with pre-emptive thoughts that act as an excellent first-line defense against medical mistakes. Supports you with timely, point-of-care solutions if the medical error were to occur. Uses a concise, templated format for quick reference and review. Helps prepare you for clinical rotations and future practice, as well as for the medicine and cardiology board exams. An eBook version is included with purchase. The eBook allows you to access all of the text, figures and references, with the ability to search, customize your content, make notes and highlights, and have content read aloud.

Medical errors are one of the leading causes of death. Deliver the highest quality care to your patients by recognizing and minimizing common mistakes. Providing quality care free of clinical errors isn’t just a matter of knowing what to do in any given situation—it’s about actively knowing what not to do. Mistake-Based Learning in Cardiology: Avoiding Medical Errors provides healthcare professionals with a summary of the common ways to inadvertently cause medical errors for each major clinical action. This resource also provides valuable information on why the mistake may be made and openly discusses medical errors to facilitate growth, learning, and psychological safety in today’s workplace. Identifies the most common errors associated with each disease and clinical action. Dissects each mistake into potential reasoning errors and pinpoints the major clinical principles related to the error. Helps you understand why the mistake was made and how to avoid similar mistakes, empowering you with pre-emptive thoughts that act as an excellent first-line defense against medical mistakes. Supports you with timely, point-of-care solutions if the medical error were to occur. Uses a concise, templated format for quick reference and review. Helps prepare you for clinical rotations and future practice, as well as for the medicine and cardiology board exams. An eBook version is included with purchase. The eBook allows you to access all of the text, figures and references, with the ability to search, customize your content, make notes and highlights, and have content read aloud.

Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides a comprehensive survey of artificial intelligence concepts and methodologies with real-life applications in cardiovascular medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and data science domains. The book's content consists of basic concepts of artificial intelligence and human cognition applications in cardiology and cardiac surgery. This portfolio ranges from big data, machine and deep learning, cognitive computing and natural language processing in cardiac disease states such as heart failure, hypertension and pediatric heart care. The book narrows the knowledge and expertise chasm between the data scientists, cardiologists and cardiac surgeons, inspiring clinicians to embrace artificial intelligence methodologies, educate data scientists about the medical ecosystem, and create a transformational paradigm for healthcare and medicine. Covers a wide range of relevant topics from real-world data, large language models, and supervised machine learning to deep reinforcement and federated learning Presents artificial intelligence concepts and their applications in many areas in an easy-to-understand format accessible to clinicians and data scientists Discusses using artificial intelligence and related technologies with cardiology and cardiac surgery in a myriad of venues and situations Delineates the necessary elements for successfully implementing artificial intelligence in cardiovascular medicine for improved patient outcomes Presents the regulatory, ethical, legal, and financial issues embedded in artificial intelligence applications in cardiology

Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Be ready with specific evidence when you present patient plans during medical rounds! Concise and easy to use, Introduction to Evidence-Based Medicine: Key Summaries for Common Medical Practices simplifies the complexity of clinical studies using key landmark trials in the core medicine specialties. Ideal for those early in their medical education and career, this portable guide helps you make the most of your limited time by introducing and explaining classic studies, preparing you to seek out and quickly digest future evidence-based medicine information. Highlights the landmark trials that have driven the evolution in medical practice, focusing on the critical information necessary to know about the study. Organizes evidence by disease and further by diagnostic or therapeutic intervention. Includes key takeaways and important notes from trials. Indicates which studies are new or controversial to help you develop an informed perspective.

Avoiding Common Prehospital Errors, will help you develop the deep understanding of common patient presentations necessary to prevent diagnostic and treatment errors and to improve outcomes. Providing effective emergency care in the field is among the most challenging tasks in medicine. You must be able to make clinically vital decisions quickly, and perform a wide range of procedures, often under volatile conditions.Written specifically for the prehospital emergency team, this essential volume in the Avoiding Common Errors Series combines evidence-based practice with well-earned experience and best practices opinion to help you avoid common errors of prehospital care.Look inside and discover...* Concise descriptions of each error are followed by insightful analysis of the "hows" and "whys" underlying the mistake, and clear descriptions of ways to avoid such errors in the future.* "Pearls" highlighted in the text offer quick vital tips on error avoidance based on years of clinical and field experience.* Focused content emphasizes "high impact" areas of prehospital medicine, including airway management, cardiac arrest, and respiratory and traumatic emergencies.

This resource offers practical advice from a seasoned cardiology physician assistant on how to be an efficient, competent member of the cardiology team. It also provides the basics of how to care for the most common cardiac conditions encountered in clinical practice. Written in an easy-to-read format, this book allows the PA/NP or student to read the book and immediately feel at home in the world of cardiology.