Data Analytics in Healthcare Research: Tools and Strategies

Add a touch of data analytics to your healthcare systems and get insightful outcomesKey FeaturesPerform healthcare analytics with Python and SQLBuild predictive models on real healthcare data with pandas and scikit-learnUse analytics to improve healthcare performanceBook DescriptionIn recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes.This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed.By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples.What you will learnGain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processesUse SQL and Python to analyze dataMeasure healthcare quality and provider performanceIdentify features and attributes to build successful healthcare models Build predictive models using real-world healthcare dataBecome an expert in predictive modeling with structured clinical dataSee what lies ahead for healthcare analyticsWho this book is forHealthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.Table of ContentsIntroduction to Healthcare AnalyticsHealthcare FoundationsMachine Learning FoundationsComputing Foundations – DatabasesComputing Foundations – Introduction to PythonMeasuring Healthcare QualityMaking Predictive Models in HealthcareHealthcare Predictive Models – A Review The Future – Healthcare and Emerging Technologies

Instructor Resources: Test bank, PowerPoint slides, suggested class activities and individual student assignments, and a transition guide to the new edition. The way a supervisor handles the managerial aspects of the job makes the difference between running the department and being run by the department. Now in its tenth edition, Dunn and Haimann’s Healthcare Management continues to provide readers with the information and tools they need to understand and keep pace with the vast changes occurring in healthcare management today. Although the text builds its framework around the core functions of management—planning, organizing, staffing, influencing, and controlling—key topics relating to the Affordable Care Act (ACA), new technologies for managers and medical staffs, and a savvier customer base are also addressed. This updated edition includes new glossary terms reflecting the ACA and other recent industry changes, as well as: A discussion of social media as an internal and external communications tool, with guidelines for setting appropriate boundaries on social media use Enhanced coverage of planning processes, including benchmarking and maintaining a data repository with input from various applications and systems Expanded treatment of budgeting tools, including the rolling and grow budget techniques A discussion of capital expenditure requests, incorporating return on investment (ROI) and the payback period as a capital ROI budgeting approach Information on Peter Senge’s entrepreneurial management theory and an expanded discussion of contingency theory Updated information on personnel-related regulations, including the Fair Labor Standards Act, Family and Medical Leave Act rules, and Health Insurance Portability and Accountability Act penalties