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Visualizing Health Care Statistics: a Data-Mining Approach: A Data-Mining Approach 2nd New edition [Minkštas viršelis]

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  • Formatas: Paperback / softback, 450 pages, weight: 652 g
  • Išleidimo metai: 09-Oct-2020
  • Leidėjas: Jones and Bartlett Publishers, Inc
  • ISBN-10: 1284197522
  • ISBN-13: 9781284197525
Kitos knygos pagal šią temą:
Visualizing Health Care Statistics: a Data-Mining Approach: A Data-Mining Approach 2nd New editionDidesnis paveikslėlis
  • Formatas: Paperback / softback, 450 pages, weight: 652 g
  • Išleidimo metai: 09-Oct-2020
  • Leidėjas: Jones and Bartlett Publishers, Inc
  • ISBN-10: 1284197522
  • ISBN-13: 9781284197525
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781284197525.html
Raktažodžiai:
Visualizing Health Care Statistics: A Data-Mining Approach is an engaging, introduction to health care statistics that demonstrates how to visualize health care statistics by using Microsoft Excel and R-Project (open source statistical software) along with hands-on examples using real-world data. In each chapter, readers are encouraged to apply statistical knowledge to real-world health care situations. Through this approach, the reader develops data gathering and analysis skills, while learning how to report and present the data in an impactful way. Key Features: A breadth of real-world, current health data, including global examples to put health care into a worldwide context. An introduction to the modern computer software that readers will use in their careers to apply statistics, including Excel and open-source R-Project.A data-mining approach helps readers understand how big data can be used systematically to increase health care quality and safety while cutting costs.Data visualization in each chapter to highlight the importance of reporting data to end-users in a meaningful way.Alignment with new CAHIIM standards, and notes those standards throughout the text.An ideal resource for preparing for the Registered Health Information Technician (RHIT) exam. Helpful appendices including common abbreviations used in statistics, formulas for health care statistics, resources for further information, CAHIIM competency exercises, and more.
Foreword ix
Preface x
Reviewers xi
Acknowledgments xiii
Chapter 1 Introduction to Healthcare Statistics
1(13)
Introduction
2(1)
History and Rationale of Healthcare Statistics
2(1)
Definition of Statistics
3(1)
The Use of Statistics in Health Care
4(1)
Key Producers and Users of Healthcare Statistics
4(1)
Data Mining
5(2)
Definition
5(1)
History
6(1)
Current Applications
6(1)
Basic Statistical Concepts
7(1)
Dataset
7(1)
Variables
8(1)
Data Distribution
8(1)
Types of Data
8(1)
Types of Data Mining Models
9(1)
Predictive Models
9(1)
Descriptive Models
9(1)
Decision Models
9(1)
Obtaining Data
10(1)
Global Perspective
11(1)
Chapter Summary
11(1)
Apply Your Knowledge
12(1)
References
12(1)
Web Links
12(2)
Chapter 2 Central Tendency, Variance, and Variability
14(15)
Introduction
15(1)
Measures of Central Tendency
15(3)
Mean
15(2)
Median
17(1)
Mode
17(1)
Frequency Distribution
18(1)
Variance and Measures of Dispersion or Variability
19(6)
Min and Max
19(1)
Range
20(1)
Outlier Data
21(1)
Interquartile Range
21(1)
Standard Deviation
22(1)
Variance
22(3)
Data Harvesting
25(1)
Global Perspective
26(1)
Chapter Summary
26(1)
Apply Your Knowledge
26(2)
References
28(1)
Web Links
28(1)
Chapter 3 Patient Data
29(26)
Introduction
30(1)
Census Data and Their Importance
31(2)
Calculation and Reporting of Patient Census Data
33(8)
Inpatient Service Days
33(1)
Average Daily Census
34(1)
Data Visualization
35(3)
Visually Examine Data With Sparklines and Microcharts
38(3)
Newborn Services
41(1)
Open-Source Software
41(1)
Freeware and Shareware
42(2)
Types of Databases
44(1)
Flat-File Database
44(1)
Relational Database
45(1)
Data Formats
45(1)
R-Project
46(2)
Data Stored in R
48(4)
Global Perspective
52(1)
Chapter Summary
53(1)
Apply Your Knowledge
53(1)
References
54(1)
Web Links
54(1)
Chapter 4 Occupancy and Utilization Data
55(24)
Introduction
56(1)
Bed Count Computation
56(2)
Definition of Inpatient Bed Count
56(1)
Importance of Inpatient Bed Count
57(1)
Bed Occupancy Ratios
57(1)
Certificate of Need
58(1)
Calculating Newborn Bassinet Occupancy Ratio
58(1)
Bed Turnover Rate
59(1)
Two Formulas for Bed Turnover
59(1)
Length of Stay
59(7)
Discharge Days and How to Calculate Them
60(1)
Total Length of Stay
61(1)
Average Length of Stay
61(5)
Median and Standard Deviation for Length of Stay
66(2)
Median Length of Stay
66(1)
Why Might You Use Median Instead of Mean?
66(1)
Standard Deviation of Length of Stay
67(1)
Visually Representing Data
68(2)
PowerPoint Presentation
69(1)
Data Mining: Association Rules With R-Project
70(4)
Global Perspective
74(3)
Chapter Summary
77(1)
Apply Your Knowledge
77(1)
References
78(1)
Web Links
78(1)
Chapter 5 Morbidity and Mortality Data
79(29)
Introduction
80(1)
Morbidity Rates
81(2)
Incidence
81(1)
Prevalence
82(1)
Mortality Rates
83(12)
Gross Mortality Rate
83(1)
Fatality Rate
84(2)
Net Mortality Rate
86(1)
Postoperative Mortality Rate
87(2)
Maternal Mortality Rate
89(1)
Maternal Mortality Rate as the Number of Deaths per 100,000 or 10,000 Births
90(1)
Anesthesia Mortality Rate
91(1)
Newborn Mortality Rate
92(1)
Fetal Mortality Rate
92(2)
Mortality Rates for Cancer
94(1)
Mortality-Adjusted Rates
94(1)
Interpreting Mortality Rate Results
95(1)
Conducting Formal Research
95(1)
Research Design
95(1)
The Hypothesis and Null Hypothesis Statements
96(1)
Statistical Measures
96(8)
P Values and Significance
96(1)
Type I Errors
97(1)
Type II Errors
97(1)
Either End of the Curve: Tails
97(1)
The Normal Distribution of Data
98(1)
Parametric and Nonparametric Tests
98(1)
z Score
98(2)
t Test
100(4)
Infant Mortality by Race and County
104(1)
Global Perspective
104(1)
Chapter Summary
104(1)
Apply Your Knowledge
104(3)
References
107(1)
Web Links
107(1)
Chapter 6 Autopsy Data
108(22)
Introduction
109(1)
What Is an Autopsy and Why Is It Important?
109(3)
Centers for Disease Control and Prevention Data
112(1)
Types of Autopsy Data
112(4)
Autopsy Rate
112(1)
Net Autopsy Rates
113(1)
Inpatient Hospital Autopsies
114(1)
Adjusted Hospital Autopsy Rate
114(1)
Autopsy Rate for Newborns
115(1)
Fetal Autopsy Rate
116(1)
Statistical Measures
116(10)
F Test: Comparison of Two Variances
116(3)
Analysis of Variance
119(1)
One-Way Analysis of Variance
119(1)
Two-way Analysis of Variance
119(7)
Global Perspective
126(1)
Chapter Summary
126(1)
Apply Your Knowledge
126(3)
References
129(1)
Web Links
129(1)
Chapter 7 Infection, Consultation, and Other Data
130(22)
Introduction
131(1)
Infection Rates
131(5)
Infection Control Committee
131(1)
Nosocomial Infection Rate
132(1)
Specific Infection Rate
133(1)
Postoperative Infection Rate
134(2)
Complication Rates
136(1)
Cesarean Section Rate
137(1)
Consultation Rates
138(1)
General Occurrence Rates
139(1)
Statistical Measures and Tools
140(6)
R Commander Graphical User Interface
140(4)
Post-Hoc Analysis of Variance
144(1)
Tukey Honest Significant Difference
144(1)
HAI Reporting and Tracking
145(1)
Text Mining and Visualization Using R-Project and Wordle
146(3)
Global Perspective
149(1)
Chapter Summary
149(1)
Apply Your Knowledge
149(1)
References
150(1)
Web Links
151(1)
Chapter 8 Health Information Management Statistics
152(18)
Introduction
153(1)
Functions of Health Information Management
153(1)
Requirements to Work in Health Information Management
153(1)
Labor Cost and Compensation
154(7)
Transcription Cost and New Technology
155(2)
Other Costs Associated With Health Information Management
157(2)
Productivity
159(1)
Healthcare Facility Staffing
159(2)
Measuring Utilization
161(1)
Types of Financial Reports
161(1)
Readmission Rate Reports
161(1)
Discharge Reports
162(1)
Two Types of Budgets: Operational and Capital
162(4)
Operational Budget
162(1)
Capital Budget
163(3)
Data Mining With Naive Bayes and R-Project
166(2)
Global Perspective
168(1)
Chapter Summary
168(1)
Apply Your Knowledge
168(1)
References
169(1)
Web Links
169(1)
Chapter 9 Research Methodology and Ethics
170(19)
Introduction
171(1)
Types of Research
172(1)
Research Process
172(1)
Step 1 Identify the Problem
172(1)
Step 2 Research the Problem
172(1)
Step 3 Develop Research Questions
172(1)
Step 4 Determine the Type of Data Needed, Sample Size, and Methods of Analysis
172(1)
Step 5 Collect Data
173(1)
Step 6 Analyze the Data
173(1)
Step 7 Draw a Conclusion
173(1)
Step 8 Report the Results and Implications for Further Research
173(1)
Research Ethics and the Abuse of Human Subjects
173(3)
Late 1700s: Edward Jenner and the First Smallpox Vaccine
173(1)
1850s: J. Marion Sims, the Father of Gynecology
174(1)
1900: Walter Reed and Yellow Fever Transmission
174(1)
1932 to 1972: Tuskegee Study of Untreated Syphilis
175(1)
1964: The Declaration of Helsinki
175(1)
1979: The Belmont Report
175(1)
The Institutional Review Board
176(1)
Review Process
176(1)
Exemption and Types of Review
176(1)
Informed Consent
177(1)
Membership
177(1)
Data Dictionary
177(1)
Statistical Measures and Tools
178(8)
Common Nonparametric Statistics
178(3)
Regression Analysis: Simple Linear Regression
181(5)
Global Perspective
186(1)
Chapter Summary
186(1)
Apply Your Knowledge
187(1)
References
187(1)
Web Links
188(1)
Chapter 10 Data Collection and Reporting Methods
189(21)
Introduction
190(1)
Descriptive Research and Information Collection
191(1)
Sampling
191(5)
Nonprobability Sampling
191(1)
Probability Sampling
192(1)
Random Numbers and Random Sampling
193(3)
Quality Question Design for Data Collection
196(3)
Bloom's Taxonomy and Question Design
196(1)
Guidelines for Question Writing
196(1)
Types of Questions
197(2)
Types of Studies
199(1)
Longitudinal Study
199(1)
Case Study
200(1)
Documentary Study
200(1)
Data Collection Methods
200(8)
Survey
200(1)
Interview
201(1)
Questionnaire
201(1)
Observation
201(1)
Appraisal
201(1)
Survey Tools
201(1)
Pivot Tables
202(3)
Multiple Regression Analysis
205(3)
Global Perspective
208(1)
Chapter Summary
208(1)
Apply Your Knowledge
208(1)
References
209(1)
Web Links
209(1)
Chapter 11 The Future of Healthcare Statistics
210(19)
Introduction
211(1)
The Future of Healthcare Statistics
211(1)
Radio Frequency Identification
211(1)
Automatic Medication Dispenser
212(1)
Health Information Exchange
212(1)
Efforts to Decrease Healthcare Costs
213(1)
Time Series Analysis of Data
213(2)
Forecasting Future Data
215(2)
Project Management General Concepts
217(3)
Analysis of Covariance
220(4)
Coronavirus---The Next Pandemic
224(1)
Ebola
225(1)
Global Perspective
225(1)
Chapter Summary
225(1)
Apply Your Knowledge
226(1)
References
227(1)
Web Links
228(1)
Appendix A Common Statistical Abbreviations Used in This Text 229(1)
Appendix B Resources for Further Information 230(1)
Appendix C Historical Abuse of Human Research Subjects 231(3)
Appendix D Formulas for Healthcare Statistics 234(3)
Appendix E CAHIIM Competency Exercises 237(3)
Glossary 240(9)
Index 249
Zada T. Wicker, MBA, RHIT, CCS, CCS-P has 35 years experience in Health Information Management with 21 of those as a professor.' She has extensive experience in curriculum design and course design both in face-to-face and online.

Starting his educational career with one of President George Bushs 1000 points of light educational reform project, Dr. Browning has worked in public schools, universities, and colleges as administrator, professor, and consultant. His areas of expertise are in Education and Technology