Applied Health Analytics and Informatics Using SAS

Applied Health Analytics and Informatics Using SAS

by Joseph M. Woodside
Released November 2018
Publisher(s): SAS Institute
ISBN: 9781635266146

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Book description

Leverage health data into insight!

Applied Health Analytics and Informatics Using SAS describes health anamatics, a result of the intersection of data analytics and health informatics. Healthcare systems generate nearly a third of the world’s data, and analytics can help to eliminate medical errors, reduce readmissions, provide evidence-based care, demonstrate quality outcomes, and add cost-efficient care. This comprehensive textbook includes data analytics and health informatics concepts, along with applied experiential learning exercises and case studies using SAS Enterprise MinerTM within the healthcare industry setting. Topics covered include:

  • Sampling and modeling health data – both structured and unstructured
  • Exploring health data quality
  • Developing health administration and health data assessment procedures
  • Identifying future health trends
  • Analyzing high-performance health data mining models
Applied Health Analytics and Informatics Using SAS is intended for professionals, lifelong learners, senior-level undergraduates, graduate-level students in professional development courses, health informatics courses, health analytics courses, and specialized industry track courses. This textbook is accessible to a wide variety of backgrounds and specialty areas, including administrators, clinicians, and executives.

This book is part of the SAS Press program.

Table of contents

  1. About this Book
  2. Acknowledgments
  3. Chapter 1: Introduction
    1. Introduction
    2. Audience Accessibility
    3. Learning Approach
    4. Experiential Learning Activity: Learning Journal
  4. Chapter 2: Health Anamatics
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Health Anamatics
    4. Health Informatics
    5. Experiential Learning Activity: Telemedicine
    6. Health Analytics
    7. Health Anamatics Architecture
    8. Experiential Learning Activity: Evidence-Based Practice and Research
    9. Health Anamatics Careers
    10. Experiential Learning Activity: Health Anamatics Careers
    11. Learning Journal Reflection
  5. Chapter 3: Sampling Health Data
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Health Anamatics Process
    4. Health Anamatics Tools
    5. SEMMA: Sample Process Step
    6. SAS OnDemand for Academics Setup
    7. Experiential Learning Application: Health and Nutrition Sampling
    8. Experiential Learning Application: Health and Nutrition Data Partitioning
    9. Experiential Learning Application: Claim Errors Rare-Event Oversampling
    10. Learning Journal Reflection
  6. Chapter 4: Discovering Health Data Quality
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Healthcare Quality
    4. Experiential Learning Activity: Healthcare Data Quality Check
    5. Healthcare Data Quality Case Study
    6. Six Sigma Health Data Quality
    7. Experiential Learning Activity: Public Data Exploration
    8. SEMMA: Exploration
    9. Experiential Learning Activity: Health Data Surveillance
    10. SEMMA: Modify
    11. Experiential Learning Application: Heart Attack Payment Data
    12. Experiential Learning Application: Data Quality Exploration
    13. Learning Journal Reflection
  7. Chapter 5: Modeling Patient Data
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Patients
    4. Patient Anamatics
    5. Patient Data
    6. Healthcare Technology Disruption
    7. Experiential Learning Activity: Personal Health Records
    8. SEMMA: Model Process Step
    9. Experiential Learning Application: Caloric Intake Simple Linear Regression
    10. Experiential Learning Application: Caloric Intake Multiple Linear Regression
    11. Model Summary
    12. Experiential Learning Application: mHealth Heart Rate App
    13. Experiential Learning Application: Inpatient Utilization - HCUP
    14. Reflection
  8. Chapter 6: Modeling Provider Data
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Providers
    4. Provider Anamatics
    5. Provider Data
    6. EHR Implementations
    7. EHR Implementation and Success Factors
    8. EHR Implementation Process
    9. Experiential Learning Activity: Electronic Health Records
    10. SEMMA: Model
    11. Experiential Learning Application: Hospital-Acquired Conditions
    12. Model Summary
    13. Experiential Learning Application: Immunizations
    14. Learning Journal Reflection
  9. Chapter 7: Modeling Payer Data
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Payers
    4. Payer Anamatics
    5. Payer Data
    6. Claim Forms
    7. Experiential Learning Activity - Claim Forms Billing
    8. Experiential Learning Activity: Claims Adjudication Processing
    9. Electronic Data Interchange
    10. Experiential Learning Activity: EDI Translation
    11. SEMMA: Model
    12. Experiential Learning Application: Patient Mortality Indicators
    13. Model Summary
    14. Experiential Learning Application: Self-Reported General Health
    15. Learning Journal Reflection
  10. Chapter 8: Modeling Government Data
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Government Agencies
    4. Government Health Anamatics
    5. Government Regulations
    6. Experiential Learning Activity: Government Data Sharing
    7. Government Billing and Payments
    8. Experiential Learning Activity: Billing Issues and Fraud and Abuse
    9. SEMMA: Model
    10. Experiential Learning Application: Fraud Detection
    11. Model Summary
    12. Experiential Learning Application: Hospital Readmissions
    13. Learning Journal Reflection
  11. Chapter 9: Health Administration and Assessment
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Health Anamatics Administration
    4. Code Sets
    5. Security
    6. Privacy
    7. Experiential Learning Activity: HIPAA Administration
    8. SEMMA: Assess
    9. Experiential Learning Application: Health Risk Score
    10. Assess Summary
    11. Experiential Learning Application: Hip Fracture Risk
    12. Learning Journal Reflection
  12. Chapter 10: Modeling Unstructured Health Data
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Unstructured Health Anamatics
    4. Social Media
    5. Experiential Learning Activity: Social Media Policy
    6. Social Media Maturity
    7. Experiential Learning Activity: Dr. Google
    8. Text Mining
    9. Experiential Learning Application: U.S. Presidential Speeches
    10. Model Summary
    11. Experiential Learning Application: Healthcare Legislation Tweets
    12. Learning Journal Reflection
  13. Chapter 11: Identifying Future Health Trends and High-Performance Data Mining
    1. Chapter Summary
    2. Chapter Learning Goals
    3. Population and Consumer Changes
    4. Artificial Intelligence and Robotics Automation
    5. Experiential Learning Activity: Robotic Surgery
    6. Healthcare Globalization and Government
    7. Public Health
    8. Big Data Health Anamatics
    9. Big Data and High-Performance Data Mining Model
    10. Experiential Learning Application: SIDS
    11. Model Summary
    12. Healthcare Digital Transformation
    13. Experiential Learning Application: Lifelogs
    14. Learning Journal Reflection
    15. Experiential Learning Application: Health Anamatics Project
  14. References
  15. Index

Product information

  • Title: Applied Health Analytics and Informatics Using SAS
  • Author(s): Joseph M. Woodside
  • Release date: November 2018
  • Publisher(s): SAS Institute
  • ISBN: 9781635266146