Applied Health Analytics and Informatics Using SAS

Joseph M. Woodside

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 Miner^TM 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.

Sujets

• Health administration
• Data analysis

Informations

• Publié par: SAS Institute
• Date de parution: 08 novembre 2018
• Nombre de lectures: 0
• EAN13: 9781635266146
• Langue: English
• Poids de l'ouvrage: 23 Mo

Extrait

The correct bibliographic citation for this manual is as follows: Woodside, Joseph M. 2018. Applied Health Analytics and Informatics Using SAS . Cary, NC: SAS Institute Inc.
Applied Health Analytics and Informatics Using SAS
Copyright 2018, SAS Institute Inc., Cary, NC, USA
978-1-62960-881-5 (Hardcopy)
978-1-63526-616-0 (Web PDF)
978-1-63526-614-6 (epub)
978-1-63526-615-3 (mobi)
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SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414
November 2018
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Contents

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

What Does This Book Cover?
Health Anamatics is formed from the intersection of data analytics and health informatics. There is significant demand to take advantage of increasing amounts of data by using analytics for insights and decision-making in healthcare. This comprehensive textbook includes data analytics and health informatics concepts along with applied experiential learning exercises and case studies using SAS Enterprise Miner in the healthcare industry setting.  The intersection of distinct areas enables connections between data analytics, clinical informatics, and technical software to maximize learning outcomes. 

Is This Book for You?
This textbook is intended for professionals, lifelong learners, senior-level undergraduates, and graduate-level students, it can be used for professional development courses, health informatics courses, health analytics courses, and specialized industry track courses.

What Are the Prerequisites for This Book?
An introductory statistics course and an introductory computer applications course are the recommended prerequisites for this book. Topics in an introductory statistics course might include descriptive statistics (frequency, central tendency, and variation) and inferential statistics (s