Alcohol consumption in Florida Healthcare Data Analysis & Recommendations Paper

Question Description

I need an explanation for this Health & Medical question to help me study.

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.

From 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) All Rights Reserved. Produced in the United States of America. For a hard copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated. U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S. federal law, the minimum restricted rights as set out in FAR 52.227-19 (DEC 2007). If FAR 52.227-19 is applicable, this provision serves as notice under clause (c) thereof and no other notice is required to be affixed to the Software or documentation. The Government’s rights in Software and documentation shall be only those set forth in this Agreement. SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414 November 2018 SAS® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. SAS software may be provided with certain third-party software, including but not limited to opensource software, which is licensed under its applicable third-party software license agreement. For license information about third-party software distributed with SAS software, refer to 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 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 technology. 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 following: ● ● ● ● Sign up to review a book. Recommend a topic. Request information on how to become a SAS Press author. Provide feedback on a book. 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: Acknowledgments 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 Introduction Audience Accessibility Learning Approach Experiential Learning Activity: Learning Journal Introduction 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 examples. 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 course. 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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Assignment 1 outline





Alcohol consumption in Florida
Data analysis


Healthcare data analysis and make healthcare management recommendations.




Alcohol consumption in Florida
Alcohol consumption is a problem that has become a concern in Florida in that it needed
intensive research in the year 2017 by BRSFF, which is used as a source by the CDC.
Consumption of alcohol becomes a health concern when a large group f people are taking too
much of it. The fact that the topic found itself under a CDC discussion means that it is something
that has to be looked into. Alcohol consumption is one of the problems that can be easily
missed since people have normalized drinking as a social activity. Most people only think of
drugs when the addiction subject is brought up. It is, therefore, sometimes not possible to realize
that one is becoming an addict until it is too late.
The BRSFF data indicates that more than 50% of the Florida population consumes
alcohol frequently (CDCa, 2019). This is an indication that many people have become alcoholics
with an inability to control their drinking desires. Being a famous vacation destination makes
Florida one of the states that worship partying and other activities that would make a person feel
like they are having a good time. Florida has records of several seriou...

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