An Introduction to Healthcare Informatics

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 together
- Explains healthcare-specific stakeholders and the management of analytical projects within healthcare, allowing healthcare practitioners to successfully navigate the political and bureaucratic challenges to implementation
- Brings 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

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.

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