An Introduction to Healthcare Informatics: Building Data-Driven Tools

An Introduction to Healthcare Informatics: Building Data-Driven Tools bridges the gap between the current healthcare IT landscape and cutting edge technologies in data science, cloud infrastructure, application development and even artificial intelligence. Information technology encompasses several rapidly evolving areas, however healthcare as a field suffers from a relatively archaic technology landscape and a lack of curriculum to effectively train its millions of practitioners in the skills they need to utilize data and related tools. The book discusses topics such as data access, data analysis, big data current landscape and application architecture. Additionally, it encompasses a discussion on the future developments in the field. This book provides physicians, nurses and health scientists with the concepts and skills necessary to work with analysts and IT professionals and even perform analysis and application architecture themselves. Presents case-based learning relevant to healthcare, bringing each concept accompanied by an example which becomes critical when explaining the function of SQL, databases, basic models etc.Provides a roadmap for implementing modern technologies and design patters in a healthcare setting, helping the reader to understand both the archaic enterprise systems that often exist in hospitals as well as emerging tools and how they can be used togetherExplains healthcare-specific stakeholders and the management of analytical projects within healthcare, allowing healthcare practitioners to successfully navigate the political and bureaucratic challenges to implementationBrings diagrams for each example and technology describing how they operate individually as well as how they fit into a larger reference architecture built upon throughout the book INDICE: Section 1: Accessing Data 1. The Healthcare IT Landscape 2. Example Project Part 1: Defining Need and Conceptualizing 3. Relational Databases 4. SQL 5. Non-Relational Databases 6. MUMPS 7. Connecting to and Querying a Database 8. Example Project Part 2: Gathering Data 9. Regulatory Considerations: HIPAA and Privacy Section 2: Introduction to Data Analysis 10. The Role of Data Analysis 11. Introduction to Python 12. Introduction to R 13. Connecting to Databases with Python and R 14. Data Science: Assessing Data Quality and Attributes 15. Data Science: Basic Modeling with Regression, Classification and Clustering 16. Interactive Computing with Jupyter and Zeppelin 17. Example Project Part 3: Hypothesis Testing and Modeling 18. Specialized Techniques: Introduction to Machine Learning, Deep Learning and Artificial Intelligence Section 3: The Big Data Landscape 19. Big Data vs Small Data 20. Overview of Big Data Tools: Hadoop, HDFS and MapReduce 21. Overview of Big Data Tools: SQL on Hadoop with Impala and Hive 22. Overview of Big Data Tools: Spark 23. Example Project Part 4: Using Big Data 24. Overview of Big Data Tools: Kafka, Flink and Spark Streaming 25. Example Project Part 5: Integrating Streaming and IoT Data Section 4: Basics of Application Architecture 26. Infrastructure: Servers and Networking 27. Infrastructure: Virtualization, Containerization and Microservices 28. Infrastructure: Hybrid and Full Cloud 29. Example Project Part 6: Implementing in the Cloud 30. Infrastructure: Introduction to Serverless Computing Section 5: Where to Go Next 31. Specialization: Healthcare Informatics Data Scientist 32. Specialization: Healthcare Informatics Data Architect 33. Specialization: Healthcare Informatics Data Developer 34. Being a Healthcare Innovator

• ISBN: 978-0-12-814915-7
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 350
• Fecha Publicación: 01/09/2020
• Nº Volúmenes: 1
• Idioma: Inglés