An Introduction to Healthcare Informatics
Building Data-Driven Tools
Published on 29. July 2020
Book
Paperback/Softback
339 pages
978-0-12-814915-7 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
graduate students, physicians, nurses, and several members of biomedical field
Dimensions
Height: 235 mm
Width: 191 mm
Weight
680 gr
ISBN-13
978-0-12-814915-7 (9780128149157)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
07/2020
Academic Press
€109.00
Available for download
Person
Peter McCaffrey, MD is a physician informaticist as well as Co-Founder and Chief Technology Officer at Hadera Technologies, a healthcare data science and cloud company. Peter attended medical school at The John Hopkins University School of Medicine, during which time he was the Founder and CEO of Accetia, Inc where his team developed a cloud analytics platform for next generation sequencing. Peter did his residency in Clinical Pathology and Laboratory Medicine at the Massachusetts General Hospital, where he also served as Chief Resident.
Together with John Monahan, Peter has developed several production healthcare applications including a clinical analytics dashboard that currently runs at the Massachusetts General Hospital. Peter has worked with hospitals in Massachusetts, Texas, California and overseas in project areas ranging from application development to analytics, application architecture and IT project management. Peter also is an Amazon Web Services Certified Solutions Architect. Peter and John have a passion for informatics and routinely lead medical informatics teaching sessions at the Massachusetts General Hospital to audiences consisting of residents, faculty and existing clinical informatics fellows.
Together with John Monahan, Peter has developed several production healthcare applications including a clinical analytics dashboard that currently runs at the Massachusetts General Hospital. Peter has worked with hospitals in Massachusetts, Texas, California and overseas in project areas ranging from application development to analytics, application architecture and IT project management. Peter also is an Amazon Web Services Certified Solutions Architect. Peter and John have a passion for informatics and routinely lead medical informatics teaching sessions at the Massachusetts General Hospital to audiences consisting of residents, faculty and existing clinical informatics fellows.
Content
Section 1: Storing and Accessing Data1. The Healthcare IT Landscape2. Relational Databases3. SQL
4. Example Project 1: Querying Data with SQL5. Non-Relational Databases6. M/MUMPS
Section 2: Understanding Healthcare Data7. How to Approach Healthcare Data Questions8. Clinical and Administrative Workflows: Encounters, Laboratory Testing, Clinical Notes, and Billing9. HL-7 and FHIR, and Clinical Document Architecture10. Ontologies, Terminology Mappings and Code Sets
Section 3: Analyzing Data11. A Selective Introduction to Python and Key Concepts12. Packages, Interactive Computing, and Analytical Documents13. Assessing Data Quality, Attributes, and Structure14. Introduction to Machine Learning: Regression, Classification, and Important Concepts15. Introduction to Machine Learning: Support Vector Machines, Tree-Based Models, Clustering, and Explainability16. Computational Phenotyping, and Clinical Natural Language Processing17. Example Project 2: Assessing and Modeling Data
18. Introduction to Deep Learning and Artificial Intelligence
Section 4: Designing Data Applications19. Analysis Best Practices20. Overview of Big Data Tools: Hadoop, Spark and Kafka21. Cloud Technologies
4. Example Project 1: Querying Data with SQL5. Non-Relational Databases6. M/MUMPS
Section 2: Understanding Healthcare Data7. How to Approach Healthcare Data Questions8. Clinical and Administrative Workflows: Encounters, Laboratory Testing, Clinical Notes, and Billing9. HL-7 and FHIR, and Clinical Document Architecture10. Ontologies, Terminology Mappings and Code Sets
Section 3: Analyzing Data11. A Selective Introduction to Python and Key Concepts12. Packages, Interactive Computing, and Analytical Documents13. Assessing Data Quality, Attributes, and Structure14. Introduction to Machine Learning: Regression, Classification, and Important Concepts15. Introduction to Machine Learning: Support Vector Machines, Tree-Based Models, Clustering, and Explainability16. Computational Phenotyping, and Clinical Natural Language Processing17. Example Project 2: Assessing and Modeling Data
18. Introduction to Deep Learning and Artificial Intelligence
Section 4: Designing Data Applications19. Analysis Best Practices20. Overview of Big Data Tools: Hadoop, Spark and Kafka21. Cloud Technologies