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Population Health AnalyticsAuthors:
As the focus of the health care delivery system continues to move toward a coordinated and accountable system, there is an increasing need for a single resource that focuses on analytics for population health. Coming this summer, Population Health Analytics addresses that need by providing detailed information and a “how to” guide for achieving population health analytics.
Comprehensive, current, and practical, this logically organized text builds from understanding data sources, to contextualizing data, modeling data, and gleaning insights from that data, which is a natural progression for organizations in progressing to higher levels of analytic capabilities. Furthermore, these frameworks for the population health process and analytics are grounded in an evidence base that is also aligned with theories and processes used in healthcare disciplines.
Features and Benefits
- Real world scenarios using case studies, use cases, critical thinking and discussion questions encourage readers to apply data and analytics to improve health.
- Readers will learn to discern the intricacies and nuances of creating, preparing, contextualizing, tracking, and reporting data to support clinical quality and healthcare cost improvement.
- Guiding principles and processes to develop a population health workforce are introduced, such as the Team Troika in Chapter 31.
- Each application of healthcare data is supported with evidence, ample references, and ancillary resources
- Navigate eBook Access (included with each printed text) enabling online or offline access to the text from a computer, tablet, or mobile device.
A first-of-its-kind text, Population Health Analytics, will prepare future and current healthcare professionals to improve health outcomes, understand patterns of health behavior and behavior determinants, develop effective interventions, and track health and cost performance across populations.
TABLE OF CONTENTS
- Section I Introduction to Population Health Analytics
- Chapter 1 Introduction to Population Health
- Chapter 2 Guiding Frameworks for Population Health Analytics
- Chapter 3 Medical Claims Data
- Chapter 4 Lab Data
- Chapter 5 Pharmacy Data
- Chapter 6 Electronic Medical Record Data
- Chapter 7 Social and Behavioral Data
- Section III Data Contextualizers
- Chapter 8 Decision Support
- Chapter 9 Receptivity, Engagement, & Activation
- Chapter 10 Risk and Disease Burden
- Chapter 11 Waste Inefficiency
- Section IV Creating the Population Health Data Model
- Chapter 12 Development of a Data Model
- Chapter 13 The Population Health Data Model
- Section V Data Infrastructure
- Chapter 14 Options for Warehousing Data
- Chapter 15 Process for Warehousing Data
- Chapter 16 Data Management and Preparation
- Chapter 17 Assessing Data Quality
- Chapter 18 Person Identity Management
- Section VI Analytic Methods
- Chapter 19 Overview of Analytic Methods for Population Health
- Chapter 20 Epidemiologic Methods
- Chapter 21 Risk Adjustment
- Chapter 22 Predictive and Prescriptive Analytics
- Chapter 23 Advanced Analytic Methods
- Chapter 24 Statistical Process Control
- Section VII Analytics Support for the Population Health Process
- Chapter 25 Assessing Populations
- Chapter 26 Targeting Individuals for Intervention
- Chapter 27 Analytics Supporting Population Health Interventions
- Chapter 28 Monitoring and Optimizing Interventions
- Chapter 29 Storytelling
- Section VIII Creating the Culture in Organizations
- Chapter 30 Assessing Organizational Capabilities
- Chapter 31 Building a Team Culture for Population Health
- Chapter 32 Skills Assessment of Population Health Analysts
- Chapter 33 Clinical Workflow Transformation
- Chapter 34 Ethical Principles for Population Health Analytics