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Expert answer:Alcohol consumption in Florida Healthcare Data Ana - Ray writers

Solved by verified expert:For our experiential learning activity this week you will conduct a healthcare data analysis and make healthcare management recommendations. The CDC (Centers for Disease Control and Prevention) analyzes BRFSS (Behavioral Risk Factor Surveillance System) data, to provide localized health information that can help public health practitioners identify local emerging health problems, plan and evaluate local responses, and efficiently allocate resources to specific needs. Review the data and recommend an action plan for public health practitioners.Visit BRFSS the BRFSS page, click Prevalence Data and Data Analysis Tools, and then click Prevalence and Trends Data.Select one Class and Topic for the most current year available (e.g. Cholesterol Awareness -> Cholesterol High)Provide the measure(s) definition for the map/chart. How does Florida compare to the nation on the selected class/topic?Using an Evidence-Based-Practice (EBP) approach connecting with your knowledge from Chapter 2, provide a summary action plan recommendation to public health practitioners for ways to improve the particular factor in Florida (best practices in other states, methods, etc.).Please be sure to include and cite your sources following best writing practices.The book is attached!

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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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About this Book
Chapter 1: 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
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
Chapter 6: Modeling Provider Data
Chapter Summary
Chapter Learning Goals
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
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
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
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 decisionmaking 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 (sampling, probability, correlation, and experimental design).
Topics included in an introductory computer applications course might
include computer hardware, productivity software (Microsoft Office,
Excel, Word), data access and manipulation, and strategic use of
What Should You Know about the Examples?
Experiential learning activities and applications are included in each chapter
so that you can gain hands-on experience with SAS in various healthcare
disciplines and in real-world settings. The practical nature of this book helps
you to integrate healthcare, analytics, and informatics into health anamatics
knowledge, skills, and abilities.
Software Used to Develop the Book’s Content
SAS Enterprise Miner 14 is the graphical user interface (GUI) software for
data mining and analytics.
Example Code and Data
You can access the example code and data for this book by linking to its
author page at
About the Author
Dr. Joseph M. Woodside is an Assistant Professor of
Business Intelligence and Analytics at Stetson University teaching
undergraduate, graduate, and executive courses on analytics, health
informatics, business analysis, and information systems. He has been a
SAS user for over ten years and is responsible for updating the analytics
learning goals and course content for the SAS Joint Certificate Program.
Before accepting the Business Intelligence and Analytics position at
Stetson, Dr. Woodside worked with KePRO, a national healthcare
management company, as the Vice President of Health Intelligence, with
responsibility for healthcare applications, informatics, business
intelligence, data analytics, customer relationship management,
employee wellness online platforms, cloud-based systems deployment
strategy, technology roadmaps, database management systems, multiple
contract sites, and program management. Dr. Woodside previously held
positions with Kaiser Permanente, with responsibility for HIPAA
Electronic Data Interchange (EDI), national claims and electronic health
record implementations, National Provider Identifiers, cost containment
financial analytics, and various data analytic initiatives. Learn more about
this author by visiting his author page at There you can download free book
excerpts, access example code and data, read the latest reviews, get
updates, and more.
We Want to Hear from You
SAS Press books are written by SAS Users for SAS Users. We welcome
your participation in their development and your feedback about SAS
Press books that you are using. Please visit to do the

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Do you have questions about a SAS Press book that you are reading?
Contact the author through or
SAS has many resources to help you find answers and expand your
knowledge. If you need additional help, see our list of resources:
I would like to thank the numerous individuals who have provided input
and feedback in support of Health Anamatics. Thanks to my family
members, editors, colleagues, leadership, and students in my previous
healthcare and analytics coursework who have encouraged me to
develop a customized textbook to maximize learning outcomes. This is an
area of great interest to me. The efforts of the support team at SAS Press
in preparing the manuscript copies and final textbook are greatly
appreciated. I would like to provide individual appreciation to the
following people:
SAS Press editor Lauree for the high level of personalized support and
feedback throughout the publishing process.
The SAS technical reviewers Roy, Malorie, Catherine, Laurie and Jeremy
for their valuable reviews and recommendations.
The SAS Press team of Julie, Stacey, and Sian for the topic design plan and
publication opportunity.
The academic leadership team Wendy, Noel, Neal, Monica, and Yiorgos
for their support of the interdisciplinary teacher-scholar role.
All my departmental colleagues Betty, Bill, Fred, John, Mahdu, Petros,
Shahram, and school and university colleagues for their encouragement
and contributions to my development.
My family members, parents, and Stephanie for their lifetime of care.
Chapter 1: Introduction
Audience Accessibility
Learning Approach
Experiential Learning Activity: Learning Journal
Health Anamatics is formed from the intersection of data analytics and
health informatics. Healthcare systems generate nearly 1/3 of the world’s
data, and healthcare stakeholders are promised a better world through
data analytics and health informatics by eliminating medical errors,
reducing re-admissions, providing evidence-based care, demonstrating
quality outcomes, and adding cost-efficient care among others. Although
healthcare has traditionally lagged behind other industries, the turning
point is near with an increased focus across the healthcare sector by way
of cost pressures, new technologies, population changes, and
government initiatives. There is significant demand to take advantage of
increasing amounts of data by using analytics for insights and decision
making in healthcare. Healthcare costs keep rising and we can use our
technology and analytics capabilities to help address these costs while
also improving quality of care. It is our aim to use our knowledge for good
and worthwhile causes.
Having conducted several health analytics and informatics related
courses and professional education workshops, I have found a need for a
comprehensive and current textbook that combines the applied analytics
knowledge using SAS with the clinical healthcare informatics concepts. In
addition to my ten years of healthcare industry experience, I have met
with over 50 industry organizations and executives over the last several
years to research relevant content, topics, and applications for health
anamatics. This textbook provides a distinguishing feature as a holistic
approach as shown in Figure 1.1.
Figure 1.1: Health Anamatics Textbook Distinguishing Approach
Related resources have a primary focus on clinical informatics, technical
software, or analytics aspects exclusively, without a connection between
all areas to integrate knowledge and maximize learning outcomes.
This textbook contains content and learning objectives, including data
analytics and health informatics concepts along with applied experiential
learning exercises and case studies using SAS Enterprise Miner within the
healthcare industry setting. All clinical data sets are designed to follow
the same data structure, data variable set, data characteristics, and
methods of published research and industry applied experiential learning
Audience Accessibility
Healthcare and analytics are among the fastest growing areas in industry
and curriculum development. This textbook is intended for professionals,
lifelong learners, upper-level undergraduates, graduate level students,
and can be used for professional development courses, health
informatics courses, health analytics courses, and specialized industry
track courses. At the graduate level there are currently over 125 analytics
programs for which this could be an applied elective or track course,
along with over 100 informatics programs for which this could be a core
Sample University and Professional Education course titles and current
coverage includes:

Health Anamatics
Health Informatics
Health Information and Analytics Management
Health Analytics
Healthcare Analytics Management
Evidence-Based Healthcare Management
Healthcare Managerial Decision Making
Applied Analytics in Healthcare
In previous courses, I have had the opportunity to enroll students from a
wide variety of specialty areas with a strong interest in learning
healthcare and analytics and have helped them be successful in the
applied topics. This textbook follows my teaching approach in being
accessible to a wide variety of backgrounds and specialty areas including
industry professionals, administrators, clinicians, and executives.
Examples of major specialty areas from prior enrollment include nursing,
information technology, business, international studies,
entrepreneurship, sports management, finance, biology, economics,
marketing, accounting, and mathematics.
Learning Approach
You might be familiar with the 2015 Disney film, Inside Out, which follows
the main character Riley, and her emotions of Joy, Sadness, Anger,
Disgust, and Fear (Disney, 2017). Watch the following YouTube clip: “Long
Term Memory Clip – Inside Out”
During the film, Joy and Sadness find themselves stuck in endless banks
of long-term memory and have trouble finding their way back to
headquarters. That is, they do not know the pathway back. Similarly,
suppose you are traveling through an endless forest. How do you find
your way back? If you walk the path hundreds or thousands of times, you
will find it easier each time to find your way back through a clear trail that
you have made over time. After a while it will be easy to follow the trail
back and find your way home. Human memory is like a nature trail:
through frequent retrieval of information that you are creating a
pathway, and if you retrieve the information enough, a clear trail forms.
Many times along your journey, you might feel that remembering is
impossible and you might be like Sadness – this will never happen!
Instead, be positive like Joy – with repeated practice and determination
that you will find the pathway! Learning takes tremendous effort. It is
through this effort that the pathways and memory are built, increasing
your intellectual capabilities. Synapses are connected in the brain, and by
frequently retrieving memories that you are forming a path to that
information. If you retrieve the memory enough times, a well-defined
path forms.
Like Riley in Inside Out, mental models are psychological representations of
real, hypothetical, or imaginary situations, and the individual
representation that is used for reasoning. Mental models allow users to
understand phenomena, make inferences, respond appropriately to a
situation, and define strategies, environment, problems, technology, and
tasks. Mental models influence behavior and create reasoning basis,
which improve human decision making, by allowing pre-defined models
which speed information processing. Mental-model maintenance occurs
when new information is incorporated into existing mental models and
reinforcement occurs. Mental-model building occurs when mental
models are modified based on the new information. Achievement of both
mental models is important to achieving quality and sustained
performance. Similarly, health anamatics is intended to provide all
stakeholders with high quality, easy to use, and relevant information for
decision making. To measure the success, one might gauge whether
health anamatics capabilities help users learn. L …
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