9 courses with the subject eCornell WCM, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
eCornell WCM101Telemedicine0
Telemedicine has emerged as a powerful tool in healthcare. During the COVID-19 pandemic, many clinicians and their patients were forced to explore and implement alternative and innovative forms of medical treatment. For many patients, telemedicine was their only link to care. But unlike in-person visits, it only takes a few seconds for a virtual visit to go poorly, exposing you and your institution to risk. This foundational course, authored by expert clinicians at Weill Cornell Medicine, offers you the chance to examine how to lead successful telemedicine encounters with your patients in a virtual space. More specifically, you will look at how to stage your telemedicine appointments effectively with patients. You will also explore how you can communicate effectively both verbally and nonverbally with patients in this arena. Given the many technical challenges inherent in conducting an effective telemedicine visit, you will examine techniques and tips to help you better prepare for your patients, creating a personal action plan to help you troubleshoot and overcome technical difficulties. In addition, you will investigate how to perform a basic physical exam virtually and have an opportunity to practice it with peers. Finally, you will study how to modify your medical decision-making process in the virtual space. All of the course materials are designed to help you adapt your in-person clinical skills to a virtual environment and ultimately form strong connections with your patients. The materials in this course correspond to the Association of American Medical Colleges (AAMC) Telehealth Competencies established in 2020.
eCornell WCM111Pharmacology of Cardiovascular Disease0
In this course, you will examine three common conditions of the cardiovascular system and explore their pharmacologic management. You ll determine how to leverage the pharmacodynamic and pharmacokinetic properties of different classes of therapeutic agents to make informed, evidence-based decisions tailored to diverse patient needs. These recommendations will be based on the latest research and society-published guidelines, promoting treatment strategies that are both current and effective. As part of developing treatment recommendations, you will balance comorbid conditions, contraindications, and possible drug interactions to guide patients to optimal outcomes, allowing you to understand key elements of cardiovascular medicine through practical, actionable insights.
In this course, you will examine common conditions affecting the pulmonary system and explore their pharmacologic management. You ll determine how to leverage the pharmacodynamic and pharmacokinetic properties of different classes of therapeutic agents to make informed, evidence-based recommendations tailored to diverse patient needs. These recommendations will be based on the latest research and society-published guidelines, promoting treatment strategies that are both current and effective. As part of developing treatment recommendations, you will balance comorbid conditions, contraindications, and possible drug interactions to guide patients to optimal outcomes, allowing you to understand pulmonary care through practical, actionable insights.
eCornell WCM113Pharmacology in the Management of Coagulation and Thrombosis0
In this course, you will examine thrombosis and explore the pharmacologic management of associated high-risk conditions. You ll discover how to leverage the pharmacodynamic and pharmacokinetic properties of anticoagulants, antiplatelet agents, thrombolytics and antifibrinolytics to make informed, evidence-based recommendations tailored to diverse patient needs. These recommendations will be based on the latest research and society-published guidelines, promoting treatment strategies that are both current and effective. As part of developing treatment recommendations, you will balance comorbid conditions, contraindications, and possible drug interactions to guide patients to optimal outcomes, allowing you to understand the use of these critical medications in your own patient population.
In this course, you will examine three distinct conditions that affect the brain and explore their pharmacologic management. You ll learn how to leverage the pharmacodynamic and pharmacokinetic properties of different classes of therapeutic agents to make informed, evidence-based recommendations tailored to diverse patient needs. These recommendations will be based on the latest research and society-published guidelines, promoting treatment strategies that are both current and effective. As part of developing treatment recommendations, you will balance comorbid conditions, contraindications, and possible drug interactions to guide patients to optimal outcomes, allowing you to understand care of conditions that affect the brain through practical, actionable insights.
In the healthcare sector, patient data is abundant. Machine learning can transform this data into a powerful tool for prediction and analysis. In this course, you will explore supervised and unsupervised learning, two key machine learning approaches that can help you maximize your data's potential. Before addressing healthcare challenges with machine learning, it's essential to begin with high-quality data. You'll examine and practice the key steps to clean and prepare raw data, ensuring it's ready for effective machine analysis. Once you've mastered these data preparation processes, you'll be ready to apply machine learning to healthcare analysis. You'll use supervised learning techniques to predict whether a patient is likely to experience sepsis. You'll also leverage unsupervised learning methods to identify similar subtypes within a large group of patients. By the end of the course, you'll realize how machine learning can improve efficiency for medical professionals and personalize patient care. Students must have intermediate proficiency in Python programming and machine learning to succeed in this course.
Data is critical to the diagnosis and treatment of patients. In this course, you will examine the efficient storage, management, and processing of healthcare data, with a focus on implementing relational data models that emphasize structure, integrity, and manipulation. You'll also explore programming languages essential for querying information from relational and non-relational databases, and you'll gain proficiency in leveraging these languages to extract valuable insights from healthcare datasets through practical exercises. You'll also develop the skills to adapt to diverse data management challenges within healthcare systems. By mastering these competencies, you will be well prepared to navigate the complex landscape of healthcare data management while ensuring compliance with regulatory standards and optimizing data-driven decision-making processes.
eCornell WCM123Natural Language Processing in Healthcare0
Clinical notes and patient records contain vast amounts of data, but this data is not always in a format machines can interpret. In this course, you will discover how natural language processing (NLP) can help you transform free text into structured data for extracting insights. You'll start by reviewing NLP methods to prepare raw text for machine analysis. Using the Python package spaCy, you'll perform NLP tasks like sentence splitting, tokenization, part-of-speech tagging, and parsing. You will then explore key NLP applications. Using the scikit-learn and scispaCy Python packages, you'll apply text classification and named entity recognition (NER) to gain insights from medical texts. Finally, you'll advance to deep learning models, examining their application for healthcare tasks such as the de-identification of patient data. You will also consider the ethical implications of using such models, focusing on patient security and privacy. By the end of this course, you'll gain hands-on experience using NLP techniques to extract insights from healthcare data while also considering how to apply these methods ethically and responsibly. Students must have intermediate proficiency in Python programming and machine learning to succeed in this course.
eCornell WCM124Designing Digital Healthcare Tools0
Artificial intelligence is influenced by various human factors. In this course, you will delve into the human elements that affect the design, implementation, and evaluation of tools in the healthcare industry. You'll examine how to apply user-centered and participatory design theory to potential solutions in healthcare, including the FAVES principles: fairness, appropriateness, validity, effectiveness, and safety. You'll discover how to create and assess a design based on a real-life problem, equipping you with the skills to translate theory into practice. You'll also explore how human factors impact different aspects of artificial intelligence and discover how to incorporate these considerations when designing a healthcare product or service related to digital health.
