Introduction aux Big Data et Décisions en Santé, Clair et Simple

Aperçu

Learn the fundamentals of big data, how to manage it and how to leverage it for making better clinical decisions. What you'll learn:

Differentiate between structured and unstructured dataHow to get databases to share informationForm a foundation to better understand machine learning and artificial intellegenceUnderstand the impact that real time data has on any Healthcare Information SystemMake clinical decisions based on Big Data WHY IS THIS COURSE IMPORTANT?Medical errors are now the third leading cause of death in America.

A system that can provide "Evidence Based" decision support to clinical staff in the diagnosis and treatment of patients has the potential of reducing many of these errors. It also has the potential of streamlining processes and improving patient experience.

Big Data can provide the solid foundation for these clinical decision support systems. But the volume is enormous and it is increasing each day.

This is complicated by the speed at which real time devices stream healthcare data. How do we link all of this data?

How do we make sure that it is accurate? How do we manage it?

How do we make it easily accessible? This course will begin to lay the foundation for addressing many of these issues.WHAT WILL YOU LEARN IN THIS COURSE?Fundamentals of Big DataWhat is Big Data?The 4Vs of Big DataStructured vs Unstructured DataThe Challenges of Big DataBig Data, The AssetData Accuracy, Stewards and GovernanceThe Power of Big DataThe Users of Big DataThe Dangers of Big DataInteroperability & DatabasesWhat is a Database?Data Islands and InteroperabilityBig Data, The Foundation for Making DecisionsFrom Big Data to InsightsDecision Making ProcessCausation and CorrelationMedical Error ExamplePharmacy ExampleRadiology ExampleThis course includes:

Video lectures walking you through the various aspects of Big Data & making clinical decisionsQuiz to test your retentionSignificant number of resources to do a deeper dive into his topic.WHO IS THE IDEAL STUDENT FOR THIS COURSE?You will learn a lot from this course if you areThinking about choosing healthcare as a careerLooking to advance your career in healthcareLooking to expand your knowledge of healthcare to better perform your current job and better understand how it fits into the ecosystem of patient care and better serve those in needCurious about everything and want to learn more about healthcare just for the sake of expanding your knowledge base.WHAT IS YOUR TEACHING STYLE?My teaching style is a very pragmatic one.

I assume you know nothing about this topic and start with the foundation and build from there. Some of these concepts could be challenging, so I sprinkle in as many examples as I can, both non healthcare and healthcare, to assure full understanding of the topic.

This is why I have appended "Plain and Simple" to all my courses.WHY ARE YOU QUALIFIED TO TEACH THIS COURSE?I spent 35 years in the designing and launching of medical imaging products and services. My career evolved from leading engineering teams, to becoming VP Marketing and then to president of a Healthcare IT firm.

It is also based on 15 years of university teaching.

Programme

• Introduction au Big Data dans le domaine de la santé
Définition et caractéristiques du Big Data
Importance du Big Data dans les décisions de santé
Vue d'ensemble des sources de données en santé
• Collecte et stockage des données
Méthodes de collecte de données
Entrepôts de données et lacs de données
Préoccupations en matière de confidentialité et de sécurité
• Traitement et gestion des données
Nettoyage et prétraitement des données
Outils pour la gestion des données dans le domaine de la santé
Qualité des données et gouvernance
• Analytique Big Data en santé
Analytique descriptive
Analytique prédictive
Analytique prescriptive
• Apprentissage automatique et IA dans le domaine de la santé
Concepts de base et applications
Études de cas de l'IA en santé
Défis et considérations éthiques
• Prise de décision avec le Big Data
Prise de décision basée sur des preuves
Impact sur les résultats des patients
Rentabilité et allocation des ressources
• Études de cas et applications
Médecine personnalisée
Gestion de la santé de la population
Opérations hospitalières et efficacité
• Tendances futures et défis
Technologies émergentes dans le Big Data
Avenir de la prise de décision dans le secteur de la santé
Implications politiques et éthiques
• Conclusion et révision du cours
Principales conclusions
Rôle du Big Data dans les systèmes de santé futurs

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Enseigné par

Thomas Giordano

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Matières

Data Science