Big Data in Healthcare: Statistical Analysis of the Electronic Health Record
Big Data in Healthcare: Statistical Analysis of the Electronic Health Record provides the statistical tools that healthcare leaders need to organize and interpret their data. Designed for accessibility to those with a limited mathematics background, the book demonstrates how to leverage EHR data for applications as diverse as healthcare marketing, pay for performance, cost accounting, and strategic management. Topics include: using real-world data to compare hospitals' performance; measuring the prognosis of patients through massive data; distinguishing between fake claims and true improvements; comparing the effectiveness of different interventions using causal analysis; benchmarking different clinicians on the same set of patients, and more. This book can be used in introductory courses on hypothesis testing, intermediate courses on regression, and advanced courses on causal analysis. It can also be used to learn SQL language. Its extensive online instructor resources include course syllabi, PowerPoint and video lectures, Excel exercises, individual and team assignments, answers to assignments, and student-organized tutorials. Big Data in Healthcare applies the building blocks of statistical thinking to the basic challenges that healthcare leaders face every day. Prepare for those challenges with the clear understanding of your data that statistical analysis can bring—and make the best possible decisions for maximum performance in the competitive field of healthcare.
1136008239
Big Data in Healthcare: Statistical Analysis of the Electronic Health Record
Big Data in Healthcare: Statistical Analysis of the Electronic Health Record provides the statistical tools that healthcare leaders need to organize and interpret their data. Designed for accessibility to those with a limited mathematics background, the book demonstrates how to leverage EHR data for applications as diverse as healthcare marketing, pay for performance, cost accounting, and strategic management. Topics include: using real-world data to compare hospitals' performance; measuring the prognosis of patients through massive data; distinguishing between fake claims and true improvements; comparing the effectiveness of different interventions using causal analysis; benchmarking different clinicians on the same set of patients, and more. This book can be used in introductory courses on hypothesis testing, intermediate courses on regression, and advanced courses on causal analysis. It can also be used to learn SQL language. Its extensive online instructor resources include course syllabi, PowerPoint and video lectures, Excel exercises, individual and team assignments, answers to assignments, and student-organized tutorials. Big Data in Healthcare applies the building blocks of statistical thinking to the basic challenges that healthcare leaders face every day. Prepare for those challenges with the clear understanding of your data that statistical analysis can bring—and make the best possible decisions for maximum performance in the competitive field of healthcare.
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Big Data in Healthcare: Statistical Analysis of the Electronic Health Record

Big Data in Healthcare: Statistical Analysis of the Electronic Health Record

by Farrokh Alemi
Big Data in Healthcare: Statistical Analysis of the Electronic Health Record

Big Data in Healthcare: Statistical Analysis of the Electronic Health Record

by Farrokh Alemi

eBook

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Overview

Big Data in Healthcare: Statistical Analysis of the Electronic Health Record provides the statistical tools that healthcare leaders need to organize and interpret their data. Designed for accessibility to those with a limited mathematics background, the book demonstrates how to leverage EHR data for applications as diverse as healthcare marketing, pay for performance, cost accounting, and strategic management. Topics include: using real-world data to compare hospitals' performance; measuring the prognosis of patients through massive data; distinguishing between fake claims and true improvements; comparing the effectiveness of different interventions using causal analysis; benchmarking different clinicians on the same set of patients, and more. This book can be used in introductory courses on hypothesis testing, intermediate courses on regression, and advanced courses on causal analysis. It can also be used to learn SQL language. Its extensive online instructor resources include course syllabi, PowerPoint and video lectures, Excel exercises, individual and team assignments, answers to assignments, and student-organized tutorials. Big Data in Healthcare applies the building blocks of statistical thinking to the basic challenges that healthcare leaders face every day. Prepare for those challenges with the clear understanding of your data that statistical analysis can bring—and make the best possible decisions for maximum performance in the competitive field of healthcare.

Product Details

ISBN-13: 9781640550667
Publisher: Academic Series
Publication date: 02/18/2020
Series: Academic Series , #1
Sold by: Bookwire
Format: eBook
Pages: 553
File size: 24 MB
Note: This product may take a few minutes to download.

About the Author

Dr. Farrokh Alemi, PhD, maintains patents on sentiment analysis, measurement of episodes of illness, and personalized medicine. He is the creator of the widely used multimorbidity index. He is the author of three books, including Decision Analysis for Healthcare Managers (Health Administration Press, 2006).

Table of Contents

Chapter 1. Introduction Chapter 2. Preparing Data Using Structured Query Language Chapter 3. Introduction to Probability and Relationships Chapter 4. Distributions and Univariate Analysis Chapter 5. Risk Assessment: Prognosis of Patients with Multiple Chapter 6. Comparison of Means Chapter 7. Comparison of Rates Chapter 8. Time to Adverse Events Chapter 9. Analysis of One Observation per Time Period: Tukey's Chart Chapter 10. Causal Control Charts Chapter 11. Regression Chapter 12. Logistic Regression Chapter 13. Propensity Scoring Chapter 14. Multilevel Modeling: Intercept Regression Chapter 15. Matched Case Control Studies Chapter 16. Stratified Covariate Balancing Chapter 17. Application to Benchmarking Clinicians: Chapter 18. Stratified Regression: Rethinking Regression Chapter 19. Association Network Chapter 20. Causal Networks
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