Transforming Healthcare Analytics. Michael N. Lewis
VS. RETURNS ON TECHNOLOGY AND INNOVATION APPLYING AUGMENTED INTELLIGENCE TO SUPPORT CLINICAL, OPERATIONAL, AND FINANCIAL OUTCOMES ENGAGEMENT ON THE HEALTHCARE DELIVERY MODEL TO UNDERSTAND EXPERIENCES AND VIEWPOINTS TO CREATE TRUST BETWEEN PROVIDERS AND PATIENTS CONCLUSION KEY TAKEAWAYS
12 Index
List of Tables
1 Chapter 2Table 2.1 Assets Within an OrganizationTable 2.2 Traditional Quantitative Business Analytics TechniquesTable 2.3 Machine Learning AlgorithmsTable 2.4 Typical Storytelling SkillsTable 2.5 General Business SkillsTable 2.6 Sample of Healthcare Business Acumen Questions
2 Chapter 4Table 4.1 Data Sources
3 Chapter 5Table 5.1 Surgical and E and M Forecast Model Performance
4 Chapter 6Table 6.1 IoT in Industries
List of Illustrations
1 Chapter 1Figure 1.1 Storage of Your Medical RecordsFigure 1.2 Sources of Health Data: From Paper to DigitalFigure 1.3 Major Advancements in HealthcareFigure 1.4 Uniqueness of Healthcare Data
2 Chapter 2Figure 2.1 What Is an Analyst?Figure 2.2 The Analytics Maturity CurveFigure 2.3 Four Capability Pillars to Support Your Analytics StrategyFigure 2.4 A3 Title: What Are You Talking About?Figure 2.5 Skills MapFigure 2.6 Basic Organizational Models
3 Chapter 3Figure 3.1 Process FrameworkFigure 3.2 Getting-Ready-for-Work ProcessFigure 3.3 Traditional Process WorkflowFigure 3.4 The Analytics Maturity CurveFigure 3.5 Evolving Process Workflow
4 Chapter 4Figure 4.1 Traditional Approach to Data and AnalyticsFigure 4.2 In-Database Processing ApproachFigure 4.3 In-Memory Processing ApproachFigure 4.4 Hadoop ArchitectureFigure 4.5 Data Analysis with Python and R
5 Chapter 5Figure 5.1 Traditional Work SilosFigure 5.2 The Work Is Converging
Guide
1 Cover
2 Table of Contents
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