This is a comprehensive, practical guide which looks at the advantages and limitations of new data analysis techniques being introduced across public health and administration services.
The Affordable Care Act (ACT) and free market reforms in healthcare are generating a rapid change of pace. The "electronification" of medical records from paper to digital, which is required to meet the meaningful use standards set forth by the Act, is advancing what and how information can be analyzed. Coupled with the advent of more computing power and big data analytics and techniques, practitioners now more than ever need to stay on top of these trends. This book presents a comprehensive look at healthcare analytics from population data to geospatial analysis using current case studies and data analysis examples in health.
This resource will appeal to undergraduate and graduate students in health administration and public health. It will benefit healthcare professionals and administrators in nursing and public health, as well as medical students who are interested in the future of data within healthcare.
Author(s): Ross M. Mullner, Edward M. Rafalski
Publisher: Routledge
Year: 2019
Language: English
Pages: 120
City: Abingdon
Cover
Title Page
Copyright Page
Contents
Foreword
Lisr of Contributors
Introduction
1. Managerial epidemiology: Creating care of health ecosystems
Introduction
What is epidemiology?
What is managerial epidemiology?
What is an ecosystem of care?
Social media and its application
Conclusion
References
2. Healthcare and population data: The building blocks
Background
Why risk stratification?
Overview of risk stratification methods
Risk stratification: Examples
Rising risk methodology: Follow-up on 38109 familiar faces
Use of data
References
3. Trend toward connected data architectures in healthcare
Balancing cost and performance
Risk management
Sharing and connecting data
Current state of databases in healthcare systems
Introduction of NoSQL databases
Summary
References
4. Knowledge management for health data analytics
Rise of the electronic health record
Data governance and infrastructure issues
Importance of data governance and infrastructure
Analytics beyond statistics
Feature selection
Data mining
Advantages
Potential issues with data mining
References
5. Geospatial analysis of patients with healthcare utilization for type 2 diabetes mellitus (T2DM): A use case approach
Introduction
Burden of the disease
Research question
Results
Discussion
References
6. Futuring: A brief overview of methods and tools
Statistical regression models
Delphi survey technique
Environmental scanning: SWOT analysis
Scenario analysis
Conclusion
References
7. Conclusion
References
Index
