Atjaunināt sīkdatņu piekrišanu

E-grāmata: Practical Predictive Analytics and Decisioning Systems for Medicine: Informatics Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery Including Medical Research

, (Professor Emerit), , , , , , (CEO, M&M Predictive Analytics LLC; UCI Adjunct Professor for Continuing Education, Predictive Analytics Program; Associate Editor, The Journal of Geriatric Psychiatry and Neurology; Private Consulting, Tulsa, OK, USA)
  • Formāts: PDF+DRM
  • Izdošanas datums: 27-Sep-2014
  • Izdevniecība: Academic Press Inc
  • Valoda: eng
  • ISBN-13: 9780124116405
  • Formāts - PDF+DRM
  • Cena: 124,92 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Formāts: PDF+DRM
  • Izdošanas datums: 27-Sep-2014
  • Izdevniecība: Academic Press Inc
  • Valoda: eng
  • ISBN-13: 9780124116405

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most costefficient manner.Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions.

Recenzijas

"...strongly recommended to researchers or healthcare administrators to improve their data analysis skills and help them make more accurate and cost-effective decisions. Score: 84 - 3 Stars" --Doody's

"In-depth and eye-opening, this seminal tome serves both the healthcare professional and the analyst: If you are a healthcare provider, researcher, or administrator, this handbook will motivate and guide your data-crunching; if you are an analytics expert, this industry overview will illuminate the pertinent background you need from the complex and dynamic healthcare industry. To get a grip on the predictive healthcare revolution, one must begin with this book's comprehensive 26 chapters and 33 hands-on tutorials." --Eric Siegel, Ph.D., founder of Predictive Analytics World and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

Papildus informācija

A practical step-by-step guide to learning predictive analytical research methods and applications
Foreword xv
Thomas H. Davenport
Foreword xvii
James Taylor
Foreword xix
John Halamka
Preface xxi
About the Authors xxiii
Acknowledgments xxvii
Guest Authors xxix
Software Instructions xxxi
Introduction xxxii
Prologue to Part 1(4)
Part 1 Historical Perspective and the Issues of Concern for Healthcare Delivery in the 21st Century
1 History of Predictive Analytics in Medicine and Health Care
Preamble
5(1)
Background
5(1)
Introduction
6(1)
Part 1: Development of Bodies of Medical Knowledge
7(1)
Earliest Medical Records in Ancient Cultures
7(1)
Classification of Medical Practices IN Ancient and Modern Cultures
8(1)
Medical Practice Documents in Major Ancient World Cultures of Europe and the Middle East
8(7)
Egypt
8(1)
Mesopotamia
9(1)
Greece
10(2)
Ancient Rome
12(2)
Arabia
14(1)
Summary of Royal Decrees Of Medical Documentation in Ancient Cultures
15(1)
Effects of the Middle Ages on Medical Documentation
16(1)
Rebirth of Interest in Medical Documentation During the Renaissance
16(2)
The Printing Press
16(1)
The Protestant Reformation
17(1)
Erasmus
17(1)
Human Anatomy
17(1)
Andreas Vesalius (1514-1564)
17(1)
William Harvey (1578-1657)
17(1)
Medical Documentation Since the Enlightenment
18(1)
Medical Case Documentation
18(1)
The Development of the US National Library of Medicine
18(1)
Part 2: Analytical and Decision Systems in Medicine and Health Care
19(1)
Computers and Medical Databases
19(1)
Medical Databases
20(1)
Best Practice Guidelines
20(1)
Guidelines of the American Academy of Neurology
21(1)
Postscript
21(1)
References
22(1)
2 Why did We Write This Book?
Preamble
23(1)
Introduction
23(1)
Reason 1: Current Problems in Medical Research
24(3)
Inaccuracies in Published Research Papers
24(1)
Design Problems in Research Studies
25(1)
A "Framework" for Determining Research Gaps
26(1)
Reason 2: Practical Assistance is Needed to Insure Success for the New Initiatives and Accreditation Standards
27(2)
The Joint Commission
27(1)
Other Standards Organizations in Health Care
28(1)
Reason 3: To Meet The Standards, Healthcare Organizations Need Practical Assistance and Tools With Implementing Lean Systems
29(4)
Examples of Problems that Highlight the Need for "Lean" and Predictive Tools
29(2)
Six Sigma and Biomedical Informatics
31(2)
Reason 4: Research into Technological/Organizational/Payment Changes Will be Necessary
33(3)
Push Back in the Face of Change
33(1)
Confusion in Coding and Payments Caused by Changes
34(1)
Technology Difficulties
35(1)
Organizational Culture
35(1)
Population Studies Versus Patient-Focused Care
36(1)
Reason 5: Practical Real World Examples are Needed that Bridge into A Phenomenal Future
36(3)
Exploratory Statistics/Individualized Statistics/Predictive Statistics
36(1)
Quality Medical Care Examples
37(1)
Practical Predictive Analytics for the Lean Movement
37(1)
Back to the Future
38(1)
Postscript
39(1)
References
39(3)
3 Biomedical Informatics
Preamble
42(1)
The Rise of Predictive Analytics in Health Care
42(1)
Moving From Reactive to Proactive Response in Health Care
43(1)
Medicine and Big Data
43(1)
An Approach to Predictive Analytics Projects
44(5)
The Predictive Analytics Process in Health Care
45(1)
Process Steps
45(4)
Meaningful Use
49(1)
Translational Bioinformatics
49(1)
Clinical Decision Support Systems
49(2)
Hybrid CDSSs
50(1)
Consumer Health Informatics
51(1)
Direct-to-Consumer Genetic Testing
51(1)
Use of Predictive Analytics to Avoid an Undesirable Future
52(1)
Consumer Health Kiosks
52(1)
Patient Monitoring Systems
52(2)
Public Health Informatics
54(1)
Medical Imaging
55(1)
Clinical Research Informatics
56(1)
Intelligent Search Engines
56(1)
Personalized Medicine
56(1)
Hospital Optimization
57(1)
Challenges
57(1)
Summary
58(1)
Postscript
59(1)
References
59(1)
Further Reading
59(1)
4 HIMSS and Organizations That Develop HIT Standards
Preamble
60(1)
Introduction
60(2)
Introduction to the Strategic Partners
61(1)
Relationship Between ANSI, HIMSS, and ONC
62(1)
Organizations Connected to or Influenced By HIMSS
62(1)
Goals, Issues, and Ideals of HIMSS
62(1)
ICD-10
63(1)
HIMSS Attempts to Help
64(1)
Standardization in Coding
65(1)
Care Continuum Alliance (Another CCA) and Health Outcome Data
65(1)
HIMSS Website
66(1)
HIMSS Analytics
67(2)
Progress of HIMSS
69(1)
Compliance
69(1)
Interoperability
69(1)
Long-Range Problems and Opportunities
70(2)
Some Questions
72(1)
The Challenge
72(1)
Postscript
72(1)
References
73(1)
5 Electronic Medical Records: Analytics' Best Hope
Preamble
74(1)
Introduction
74(1)
What is an EMR?
75(1)
A Bit (Of a "Byte") of History
76(1)
Why aren't We There Yet?
77(3)
Cost
78(1)
Usability
78(1)
Disruption of Workflow
79(1)
Lack of Interoperability
79(1)
Ferraris and Country Roads
80(5)
Postscript
85(1)
References
85(1)
Bibliography of Additional References on the Topic of Medical Records
85(2)
6 Open-Source EMR and Decision Management Systems
Preamble
87(1)
Introduction
87(1)
Why Choose an Open-Source EMR Software Application?
88(1)
VistA — The Veterans Administration System That Started it All
89(1)
Five of the Best Open-Source EMR Systems for Medical Practices
89(5)
The OSCAR EMR System
89(1)
OpenEMR
90(1)
OpenMRS by Partners in Health
91(2)
Raxa Project
93(1)
MOTECH in Ghana
93(1)
Global Open-Source EMR Systems and the Future of Analytics
94(1)
Postscript
94(1)
References
95(1)
7 Evidence-Based Medicine
Preamble
96(1)
Introduction
96(1)
Geodemographic Elements of Medical Treatment
97(1)
How can we Define the Nature and Boundaries of EBM?
98(1)
General Problems with EBM
98(1)
Evidence-Based Medicine and Analytics
98(1)
The Path to Evidence
99(1)
What is a Randomized Controlled Trial?
100(1)
If not Evidence Based, then What?
101(1)
The EBM Process
102(1)
Evidence at the Bedside
103(1)
What do Patients Think?
103(1)
Evidence-Based Medicine Versus the Art of Medicine
104(1)
Predictive Analytics and EBM
104(1)
Postscript
104(1)
References
104(2)
8 ICD-10
Preamble
106(1)
Introduction
106(1)
Rise of the ICD
106(1)
Why the ICD?
107(1)
Elements Of ICD Documentation
107(1)
The ICD Timetable
108(1)
Changes Ahead for ICD-10 Users
108(1)
Comparison of ICD-9 and ICD-10
109(1)
Increased Ability to Describe and Justify Treatment
109(1)
The ICD-10 Descriptive Language is Much Richer
109(1)
Facilitation of Mortality and Morbidity Analyses
109(1)
Implications of ICD-10 Changes
109(1)
Greater Scalability and Extensibility Foster Information Sharing Among Institutions
109(1)
More Specific Categories and Codes
110(1)
Comparison of Codes
110(1)
ICD-10 Codes in Practice
110(1)
ICD-10 Changes in Terminology
111(1)
Implementation Issues of Changing to ICD-10
111(1)
What Lies Ahead for Payers and Providers?
112(1)
For Providers
112(1)
For Payers
112(1)
Transition is a Joint Effort
112(1)
Postscript
113(1)
References
113(3)
9 "Meaningful Use" — The New Buzzword in Medicine
Preamble
116(1)
Introduction
116(1)
Stage I of "Meaningful Use"
117(1)
Meaningful Use Goals for Hospitals
117(6)
The 14 Requirements (Hospitals Must Meet All of These)
117(3)
The 10 Choice Objectives (Hospitals Must Meet 5 of These)
120(3)
Meaningful Use Goals For Doctors
123(5)
The 15 Requirements (Doctors Must Meet All of These)
124(2)
The 10 Additional Choice Objectives for Individual Physicians (5 of These Must Be Met to Achieve Compliance)
126(2)
Meaningful Use Requirements Of Stage I, Stage II, and Stage III
128(1)
Requirements for Stage I of Meaningful Use
128(1)
Postscript
129(1)
Bibliography
129(4)
10 The Joint Commission: Formerly the Joint Commission on Accreditation of Healthcare Organizations (JCAHO)
Preamble
133(1)
History of the Joint Commission
133(1)
The Joint Commission International
134(1)
Joint Commission Accreditation
135(1)
Preparing for a Survey
136(1)
Other Regulatory Organizations
136(1)
Joint Commission Standards
137(1)
National Patient Safety Goals
138(2)
Postscript
140(1)
References
140(4)
11 Root Cause Analysis, Six Sigma, and Overall Quality Control and Lean Concepts: The First Process to Bring Quality and Cost-Effectiveness to Medical Care Delivery
Preamble
144(1)
Introduction
144(1)
Part 1: Six Sigma and Quality Control, Root Cause Analysis, and Leapfrog as they Developed During the 1990's and Early 2000's: Learning from Medical Errors and Turning Them into Quality Improvements
144(1)
The Need For Quality: Medical Errors
144(1)
Epidemiology of Medical Errors
144(1)
Approaches to Error
144(1)
Definitions
144(2)
Statistical Process Control
144(1)
Total Quality Management
145(1)
Deming's Principles
145(1)
Six Sigma
145(1)
Cost—Benefit Analysis
145(1)
Pareto Efficiency
146(1)
Kaldor-Hicks Efficiency
146(1)
Examples of Errors
146(1)
Methods to Improve Safety and Reduce Error
146(1)
Root Cause Analysis
146(1)
Ishikawa Diagram
146(1)
Apollo Process
147(1)
Systems for Ensuring Review
147(1)
History of Quality in Health Care
147(3)
Crossing the Quality Chasm: The IOM Health Care Quality Initiative
147(2)
Comprehensive Drug Safety
149(1)
The Leapfrog Initiative
150(2)
Organizational Goals of the Leapfrog Group
150(1)
Why Leapfrog?
150(1)
Leaps in Hospital Quality and Safety
151(1)
Four Primary Criteria for Purchasing
151(1)
Timeline
152(1)
Part 2: Root Cause Analysis, Six Sigma and Quality Control, and Lean Concepts in Hospitals and Healthcare Facilities as They Exist in 2013-2014
152(1)
Part Outline
152(1)
Six Sigma
152(1)
Quality Control
153(1)
Examples of Using Six Sigma in Health Care
153(1)
Lean Concepts for Health Care: The Lean Hospital as a Methodology of Six Sigma
154(1)
Root Cause Analysis
155(3)
Part 3: Experiences of a Doctor who Implemented a Quality Control Department in a Hospital System During the 1990's — An Era When Quality was Anything But the Norm
158(1)
Quality Improvement
158(4)
General Introduction
158(1)
Definition of Healthcare Quality
158(1)
The Quality Department in a Hospital
159(1)
Issues Discovered
160(2)
Quality of Care Examples
162(1)
Example 11.1
162(1)
Example 11.2
163(1)
Postscript
163(1)
References
163(2)
12 Lean Hospital Examples
Preamble
165(1)
Introduction
165(1)
Lean Kaizen Concepts
165(3)
Henry Ford Hospitals
168(1)
The Joint Commission Annual Report, 2013
168(1)
Transparency Just Increased
168(1)
Kaiser Permanente Managed Care Organization
169(1)
Virginia Mason Hospital in Seattle
170(1)
Examples of Lean Projects
171(3)
Oncology: Infusion Therapy
171(1)
Cardiology
172(1)
Reducing Patient Falls
172(1)
Reducing Catheter-Associated Urinary Tract Infections
173(1)
Intravenous (IV) Laboratory Lean Project
173(1)
Emergency Room Application of Lean
173(1)
Summary
174(1)
Postscript
174(1)
References
174(3)
13 Personalized Medicine
Preamble
177(1)
What is Personalized Medicine?
177(1)
P4 Medicine
178(1)
P5 to P6 Medicine
178(1)
Personalized Medicine, Genomics, and Pharmacogenomics
178(2)
Differences Among Us
179(1)
Differences Go Beyond Our Body and Into Our Environment
179(1)
Changes from Birth to Death
179(1)
Ancestry and Disease
180(1)
It Is Not About Just Our Genome
180(1)
Changing the Definition of Diseases
180(1)
Systems Biology
181(1)
Efficacy of Current Methods — Why We Need Personalized Medicine
182(1)
Predictive Analytics in Personalized Medicine
183(1)
The Future: Predictive and Prescriptive Medicine
183(2)
Application of Predictive Analytics and Decisioning in Predictive and Prescriptive Medicine
183(2)
The Diversity of Available Healthcare Data
185(13)
Diversity of Data Types Available
185(13)
All the Other "Omics"
198(1)
The Future
198(4)
Challenges
200(2)
Postscript
202(1)
References
202(3)
14 Patient-Directed Health Care
Preamble
205(1)
The Empowered Patient
205(1)
Patient Defined
206(1)
Concept 1: Empowerment and Involvement —How can Patients be Empowered to Become More Involved with their Medical Care?
207(2)
Patient Involvement
207(1)
Hindrances to Patient Involvement
207(2)
Concept 2: Coordination of Care and Communication
209(4)
The Integrated Healthcare Delivery System Model
213(1)
Concept 3: Consumerism in Health Care
213(4)
Concept 4: Patient Payment Models
217(3)
Burden of Health Care upon the Future
218(1)
Mis-application of Treatment Increases Costs
218(1)
Many Insurance Plans — Few Differences
218(2)
Concept 5: Patient Education and Patient Self-Education and Decisions
220(3)
Information Concerning Obesity
221(2)
Patient Portals
223(1)
Conclusion
223(1)
Concept 6: Alternatives and New Models
223(8)
Insurance Companies Going International
223(1)
Alternative Screenings
224(1)
Alternative Ways of Knowing about Ourselves
225(6)
Conclusion
231(1)
Postscript
231(1)
References
231(3)
Further Reading
234(5)
Prologue to Part 1,
Chapter 15
15 Prediction in Medicine — The Data Mining Algorithms of Predictive Analytics
Preamble
239(1)
Introduction
239(1)
The Use of Simple Descriptive Statistics, Graphics, and Visual Data Mining in Predictive Analytics
240(2)
The Insight of Simple Descriptive Statistics
240(1)
Visual Data Mining
240(2)
Predictive Modeling: Using Data to Predict Important Outcomes
242(8)
The Difference Between Statistical Models and General Predictive Modeling
242(1)
The Algorithms of Predictive Modeling
243(6)
Choosing the Right Algorithm for the Right Analysis
249(1)
Clustering: Identifying Clusters of Similar Cases, and Outliers
250(5)
Clustering Algorithms
250(5)
Text Mining Algorithms
255(1)
Dimension Reduction Techniques
255(3)
Latent Semantic Indexing
255(1)
Partial Least Squares
256(1)
Feature Selection vs. Feature Extraction
257(1)
Detecting the Interrelationships and Structure of Data Through Association and Link Analysis
258(1)
The Support and Confidence Statistics
258(1)
Summary
258(1)
Postscript
259(1)
References
259(8)
Prologue to Part 2
Part 2 Practical Step-by-Step Tutorials and Case Studies
Guest Tutorial Authors
A Case Study: Imputing Medical Specialty Using Data Mining Models
267(32)
B Case Study: Using Association Rules to Investigate Characteristics of Hospital Readmissions
299(17)
C Constructing Decision Trees for Medicare Claims Using R and Rattle
316(25)
D Predictive and Prescriptive Analytics for Optimal Decisioning: Hospital Readmission Risk Mitigation
341(18)
E Obesity — Group: Predicting Medicine and Conditions That Achieved the Greatest Weight Loss in a Group of Obese/Morbidly Obese Patients
359(29)
H Obesity — Individual: Predicting Best Treatment for an Individual from Portal Data at a Clinic
388(58)
F2 Obesity — Individual: Automatic Binning of Continuous Variables and WoE to Produce a Better Model Than the "Hand Binned" Stepwise Regression Model of Tutorial F1
446(16)
G Resiliency Study for First and Second Year Medical Residents
462(69)
H Medicare Enrollment Analysis Using Visual Data Mining
531(13)
I Case Study: Detection of Stress-Induced Ischemia in Patients with Chest Pain After "Rule-Out ACS" Protocol
544(14)
J1 Predicting Survival or Mortality for Patients with Disseminated Intravascular Coagulation (DIC) and/or Critical Illnesses
558(66)
J2 Decisioning for DIC 603 Predicting Allergy Symptoms
624(11)
C Exploring Discrete Database Networks of Tricare Health Data Using R and Shiny
635(24)
D Schistosomiasis Data from WHO
659(38)
E The Poland Medical Bundle
697(29)
O Medical Advice Acceptance Prediction
726(19)
P Using Neural Network Analysis to Assist in Classifying Neuropsychological Data
745(12)
Q Developing Interactive Decision Trees Using Inpatient Claims (with SAS Enterprise Miner)
757(38)
R Divining Healthcare Charges for Optimal Health Benefits Under the Affordable Care Act
795(22)
S Availability of Hospital Beds for Newly Admitted Patients: The Impact of Environmental Services on Hospital Throughput
817(15)
T Predicting Vascular Thrombosis: Comparing Predictive Analytic Models and Building an Ensemble Model for "Best Prediction"
832(18)
U Predicting Breast Cancer Diagnosis Using Support Vector Machines
850(16)
V Heart Disease: Evaluating Variables That Might Have an Effect on Cholesterol Level (Using Recode of Variables Function)
866(10)
W Blood Pressure Predictive Factors
876(20)
X Gene Search and the Related Risk Estimates: A Statistical Analysis of Prostate Cancer Data
896(25)
Y Ovarian Cancer Prediction via Proteomic Mass Spectrometry
921(18)
Z Influence of Stent Vendor Representatives in the Catheterization Lab
939(30)
Prologue to Part 3
Part 3 Practical Solutions and Advanced Topics in Administration and Delivery of Health Care Including Practical Predictive Analytics for Medicine
16 Predictive Analytics in Nursing Informatics
Preamble
969(1)
Introduction
969(1)
Nursing Informatics
970(3)
Patient Education
970(1)
Supporting Nurses' Work
971(1)
Predicting the Patient's Future Development
971(1)
Patient Monitoring Data
971(1)
Home Nursing and Nursing Homes
971(1)
Telemedicine
972(1)
Triaging Patients
972(1)
Preventing Inpatient Morbidity
972(1)
Patient Comfort and Satisfaction
972(1)
Staffing
973(1)
Patient Hand-Offs
973(1)
Approach to Projects
973(1)
Postscript
973(1)
References
973(3)
17 The Predictive Potential of Connected Digital Health
Preamble
976(1)
Why Don't Clinicians Embrace Digital Consumer Connections?
976(6)
1 What Do I Do with the Data?
977(1)
2 Who Says That the Data Are Valuable?
977(1)
3 What New Liabilities Emerge from Precision and Probabilistic Medicine?
978(1)
4 How Cumbersome and Difficult Are New Data Collection Solutions?
979(1)
5 How Do the Devices and Apps Integrate and Interoperate?
980(1)
6 How Do You Maintain Privacy and Security with Mobile Consumer Engagement?
981(1)
7 How and When Will Clinicians Get Paid for Participating in Mobile Health?
982(1)
Promise and Problems of Shifting to Mobile Health Technology
982(1)
What Can We Learn from the VA About the Potential of Predictions?
983(2)
What Can We Learn from Financial Services Regarding Digital Transformation?
985(3)
The Rise of Mobile Financial Services
985(1)
How Do These Five Insights from the Digitization of the Financial Services Industry Inform Our Views About the Digitization of Healthcare Information?
986(2)
Summary and Recommendations
988(1)
Postscript
988(1)
References
988(1)
18 Healthcare Fraud
Preamble
989(1)
Introduction
989(1)
Leakage Due to Fraud
990(1)
Definition of Fraud in the Healthcare Context
990(2)
Fraud Perpetrated by a Provider
991(1)
Fraud Perpetrated by a Patient Subscriber
991(1)
Fraud Perpetrated by Third Parties
991(1)
Fraud Perpetrated by Agents/Brokers
991(1)
Statutes and Regulations Intended to Prevent, Detect, and Prosecute Fraud
992(1)
Major Agencies Involved in Healthcare Anti-Fraud Efforts
992(1)
Challenges That Face Anti-Fraud Efforts
992(2)
Traditional Challenges
993(1)
Emerging Challenges
993(1)
Traditional Means of Detection
994(1)
Limitations of Traditional Means of Detection
994(1)
The Emergence of Big Data in Healthcare Investigations
994(1)
ACA Anti-Fraud Provisions
994(1)
Analytical Anti-Fraud Approaches
995(1)
Anomaly Detection
995(1)
Text Analytics
995(1)
Link Analysis
995(1)
Combined Analytical Techniques
995(1)
The Future of Healthcare Anti-Fraud Efforts
995(1)
Anti-Fraud Organizations
996(1)
Postscript
996(1)
References
996(2)
19 Challenges for Healthcare Administration and Delivery: Integrating Predictive and Prescriptive Modeling into Personalized Health Care
Preamble
998(1)
Challenges
998(2)
Postscript
1000(1)
References
1001(1)
20 Challenges of Medical Research for the Remainder of the 21st Century
Preamble
1002(1)
Challenges
1002(1)
Postscript
1003(1)
21 Introduction to the Cornerstone
Chapters of this Book,
Chapters 22-25: The "Three Processes" — Quality Control, Predictive Analytics, and Decisioning
Preamble
1004(1)
Introduction
1004(1)
Traditional Statistics vs Data Mining vs Predictive Analytics
1005(2)
Postscript
1007(1)
22 The Nature of Insight from Data and Implications for Automated Decisioning: Predictive and Prescriptive Models, Decisions, and Actions
Preamble
1008(1)
Overview
1008(1)
The Nature of Insight and Expertise
1009(1)
Procedural and Declarative Knowledge
1009(1)
Non-Conscious Acquisition of Knowledge
1009(1)
Conclusion: Expertise and the Application of Pattern Recognition Methods
1010(1)
Statistical Analysis vs Pattern Recognition
1010(4)
Fitting a priori Models
1011(1)
Pattern Recognition: Data are the Model
1011(2)
Pattern Recognition and Declarative Knowledge: Interpretability of Results
1013(1)
Predictive Modeling and Prescriptive Models
1014(4)
Rules, Conditional Scoring Logic, Action Plans
1015(1)
An Example System: The STATISTICA Enterprise Decisioning Platform®
1015(3)
Summary
1018(1)
Postscript
1018(1)
References
1018(1)
23 Platform for Data Integration and Analysis, and Publishing Medical Knowledge as Done in a Large Hospital
Preamble
1019(1)
Introduction
1019(1)
Functions and Applications of the Platform
1020(1)
Platform Components and Architecture
1020(9)
Data Warehouse and OLAP
1021(1)
Data Collection Module
1021(1)
Medical Research Environment-Overview
1022(3)
Management Portal
1025(2)
Reports for NHF Contract Monitoring and Clearance
1027(2)
Optimizer
1029(1)
Conclusions
1029(1)
Postscript
1029(1)
References
1029(1)
24 Decisioning Systems (Platforms) Coupled With Predictive Analytics in a Real Hospital Setting — A Model for the World
Preamble
1030(1)
Introduction
1030(1)
Setting the Stage for a Decisioning Platform
1031(3)
Getting Support from Information Technology and Hospital Leadership
1031(1)
Creating an Analytical Culture (Or, Have You Ever Tried to Tell a Surgeon He's Doing Things Wrong?)
1032(1)
Defining the Outcomes Targets
1033(1)
Defining the Clinical Decisions
1033(1)
Define the Resources That Need to be Managed
1033(1)
Determine What Data You Have Access to
1034(1)
Deploying the Decision Management System
1034(2)
Decision Management System Tools
1034(1)
Decision Management Process
1035(1)
Decision Management System Workflow Example
1035(1)
Conclusion
1036(1)
Postscript
1037(1)
References
1037(1)
25 IBM Watson for Clinical Decision Support
Preamble
1038(1)
Introduction
1038(1)
Personalized Health Care and Clinical Decision Support
1038(1)
IBM Watson and Medical Decision-Making
1039(1)
Postscript
1040(1)
References
1040(1)
26 21st Century Health Care and Wellness: Getting the Health Care Delivery System That Meets Global Needs
Introduction
1041(1)
Overview
1042(1)
Background and Need for Change
1042(1)
Learning Objectives
1043(1)
Trends Impacting Healthcare Industries
1043(1)
Existing and Emerging Healthcare Organizations
1044(1)
Health Start-Ups and Established Technology Firms Contributing to Health Care
1045(4)
IBM Watson
1045(1)
New Technology and 21st Century Health Care: Health Start-Up Firms
1046(1)
Building the Star Trek Tricorder
1046(2)
Wearable Computers for Doctors
1048(1)
Explores
1048(1)
Technology Trends That Impact Health and Wellness
1049(1)
Current Trends Outside Healthcare Facilities
1049(1)
Trends and Expectations for the Future of Health It and Analytics
1049(2)
The Next 4 Years — by-2018 Predictions
1050(1)
The Next 9 Years — by-2023 Predictions
1050(1)
Conclusions and Summary of Important Concepts Presented in This Book
1051(2)
Technology for the Elderly
1051(1)
Technology for Rural Areas
1052(1)
Final Concluding Statements
1052(1)
References
1053(1)
Bibliography
1054(1)
Index 1055
Dr. Gary Miner PhD received a B.S. from Hamline University, St. Paul, MN, with biology, chemistry, and education majors; an M.S. in zoology and population genetics from the University of Wyoming; and a Ph.D. in biochemical genetics from the University of Kansas as the recipient of a NASA pre-doctoral fellowship. He pursued additional National Institutes of Health postdoctoral studies at the U of Minnesota and U of Iowa eventually becoming immersed in the study of affective disorders and Alzheimer's disease.

In 1985, he and his wife, Dr. Linda Winters-Miner, founded the Familial Alzheimer's Disease Research Foundation, which became a leading force in organizing both local and international scientific meetings, bringing together all the leaders in the field of genetics of Alzheimer's from several countries, resulting in the first major book on the genetics of Alzheimers disease. In the mid-1990s, Dr. Miner turned his data analysis interests to the business world, joining the team at StatSoft and deciding to specialize in data mining. He started developing what eventually became the Handbook of Statistical Analysis and Data Mining Applications (co-authored with Drs. Robert A. Nisbet and John Elder), which received the 2009 American Publishers Award for Professional and Scholarly Excellence (PROSE). Their follow-up collaboration, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, also received a PROSE award in February of 2013. Gary was also co-author of Practical Predictive Analytics and Decisioning Systems for Medicine (Academic Press, 2015). Overall, Dr. Miners career has focused on medicine and health issues, and the use of data analytics (statistics and predictive analytics) in analyzing medical data to decipher fact from fiction.

Gary has also served as Merit Reviewer for PCORI (Patient Centered Outcomes Research Institute) that awards grants for predictive analytics research into the comparative effectiveness and heterogeneous treatment effects of medical interventions including drugs among different genetic groups of patients; additionally he teaches on-line classes in Introduction to Predictive Analytics, Text Analytics, Risk Analytics, and Healthcare Predictive Analytics for the University of California-Irvine. Recently, until official retirement 18 months ago, he spent most of his time in his primary role as Senior Analyst-Healthcare Applications Specialist for Dell | Information Management Group, Dell Software (through Dells acquisition of StatSoft (www.StatSoft.com) in April 2014). Currently Gary is working on two new short popular books on Healthcare Solutions for the USA and Patient-Doctor Genomics Stories. Linda A. Winters-Miner, PhD, earned her bachelors and masters degrees at University of Kansas, her doctorate at the University of Minnesota, and completed post-doctoral studies in psychiatric epidemiology at the University of Iowa. She spent most of her career as an educator, in teacher education and statistics and research design. She spent nearly two years as a site coordinator for a major (Coxnex) drug trial. For 23 years, she was a Program Director at Southern Nazarene University - Tulsa. Her program direction included three undergraduate programs in business and psychology and three graduate programs in management, business administration, and health care administration. She has authored or co-authored numerous articles and books including with Gary and others, the first book concerning the genetics of Alzheimer's, Alzheimer's disease: Molecular genetics, Clinical Perspectives and Promising New Research. L Miner authored some of the tutorials in the first two predictive analytic books published in 2009 and 2012 by Elsevier. For ten years, she served as a Community Faculty for Research and Data Analysis at IHI Family Practice Medical Residency program in Tulsa. She taught predictive analytics online, including healthcare predictive analytics, for the University of California-Irvine. At present, Dr. Miner is Professor Emeritus, Professional and Graduate Studies, Southern Nazarene University and serves on the Editorial Board, The Journal of Geriatric Psychiatry and Neurology. Dr. Goldstein MD, FAAP attended the University of Miamis Honor Program in Medical Education under an Isaac B. Singer full tuition scholarship, completed his pediatric residency training at the University of California, Los Angeles, and finished his Neonatal Perinatal Medicine training at the University of California, Irvine in 1994. Dr. Goldstein is board certified in both Pediatrics and Neonatal Perinatal Medicine. He is an Associate Professor of Pediatrics at Loma Linda University Childrens Hospital and emeritus medical director of the Neonatal Intensive Care Unit at Citrus Valley in West Covina, CA. He has been in clinical practice for 20 years. At the various places he has worked, Dr. Goldstein has become fluent in a multitude of EMRs including EPIC, Cerner, and Meditech. As a member of the Department Deputies Users Group at Loma Linda University Hospital, Dr. Goldstein participates in an ongoing EMR improvement process. Dr. Goldstein is a past president of the Perinatal Advisory Council, Legislation, Advocacy and Consultation (PACLAC) as well as a past president of the National Perinatal Association (NPA). Dr. Goldstein is the twice recipient of the annual Jack Haven Emerson Award presented to the physician with the most promising study involving innovative pulmonary research and the 2013 recipient of the National Perinatal Association Stanley Graven lifetime achievement award presented for his ongoing commitment to the advancement of neonatal and perinatal health issues. He is the editor of PACLACs Neonatal Guidelines of Care as well as the Principal author of both the National Perinatal Associations 2011 Best Practice Checklist Oxygen Management for Preterm Infants and Respiratory Syncytial Virus (RSV) Prophylaxis 2012 Guidelines. Dr. Goldstein serves on the editorial board of the Journal of Perinatology as well as Neonatology Today, has represented the NPA to the American Academy of Pediatrics (AAP) perinatal section, and is a moderator of NICU-NET, a neonatal listserv. He is an executive board member and is on the nominations committee for the Section on Advances in Therapeutics & Technology (SOATT) of the AAP. Dr. Goldstein chaired the NPA National Conferences in 2004, 2008 and 2011 and continues to be active in conference planning as the CME Continuing Medical Education (CME) chair for PACLAC.

His research interests include the development of non-invasive monitoring techniques, evaluation of signal propagation during high frequency ventilation, and data mining techniques for improving quality of care. Dr. Goldstein has also been a vocal advocate for RSV prophylaxis and right” sizing technology for the needs of neonates. Dr. Goldsteins recent publications have included Critical Complex Congenital Heart Disease (CCHD)” which was dual published in Neonatology Today and Congenital Cardiology Today, the Late Preterm Guidelines of Care” published in the Journal of Perinatology, and How Do We COPE with CPOE” published in Neonatology Today. Bob Nisbet, PhD, is a Data Scientist, currently modeling precancerous colon polyp presence with clinical data at the UC-Irvine Medical Center. He has experience in predictive modeling in Telecommunications, Insurance, Credit, Banking. His academic experience includes teaching in Ecology and in Data Science. His industrial experience includes predictive modeling at AT&T, NCR, and FICO. He has worked also in Insurance, Credit, membership organizations (e.g. AAA), Education, and Health Care industries. He retired as an Assistant Vice President of Santa Barbara Bank & Trust in charge of business intelligence reporting and customer relationship management (CRM) modeling. Nephi Walton MD, MS, FACMG, FAMIA earned his MD from the University of Utah School of Medicine and a Masters degree in Biomedical Informatics from the University of Utah Department of Biomedical Informatics where he was a National Library of Medicine fellow. His Masters work was focused on data mining and predictive analytics of viral epidemics and their impact on hospitals. He was the winner of the 2009 AMIA Data Mining Competition and has published papers and co-authored books on data mining and predictive analytics. Also during his time at the University of Utah he spent several years studying genetic epidemiology of autoimmune disease and the application of analytical methods to determining genetic risk for disease, a work that continues today. His work has included several interactive medical education products. He founded a company called Brainspin that continues this work and has won international awards for innovative design in this area. He is currently a combined Pediatrics/Genetics fellow at Washington University where he is pursuing several research interests including the application of predictive analytics models to genomic data and integration of genomic data into the medical record. He continues to work with the University of Utah and Intermountain Healthcare to further his work in viral prediction models and hospital census prediction and resource allocation models. Pat Bolding, MD, FAAFP is a practicing board certied family physician. He has used an EMR (Electronic Medical Record) since his residency training in the mid 1980s which at the time was the pioneering” Technicon Medical Information System. Later, as the CEO of a large family practice group (which also hosted a 30 resident training program), he led the selection and implementation of several EMR systems, beginning with the text-based Medic Autochart then Misys EMR and nally the A4-Healthmatics system. In 2007, he joined a multi-specialty group practice/integrated delivery system where he serves on the EMR committee which oversaw the implementation of the NextGen ambulatory EMR. More recently he was a member of the search committee that chose the Epic system to replace NextGen. He is a frequent speaker on health/medical topics and has a special interest in evidence-based medicine. He is an adjunct faculty member of Southern Nazarene University, teaching in the Health Care MBA program. Joseph M. Hilbe is an emeritus professor at the University of Hawaii, an adjunct professor of statistics at Arizona State University, and a Solar System Ambassador with NASA/Jet Propulsion Laboratory, Caltech. An elected Fellow of the American Statistical Association and elected member of the International Statistical Institute, Dr. Hilbe is currently President of the International Astrostatistics Association, is a full member of the American Astronomical Society, and Chairs the Statistics in Sports section of the American Statistical Association (ASA). He has authored fifteen books in statistical modeling, and over 200 book chapters, encyclopedia entries, journal articles, and published statistical software, and is currently on the editorial board of seven academic journals. During the 1990s Dr Hilbe was on the founding executive committee of the ASA Section on Health Policy Statistics, and served in various capacities in the health research industry, including: CEO of National Health Economics and Research Corp.; Director of Research at Transitional Hospitals Corp, a national chain of long term hospitals; Senior Statistician of NRMI-2, Genentechs National Registry for Myocardial Infarctions; lead biostatistical consultant, Hoffman-La Roches National Canadian Registry for Cardiovascular Disease; and was Senior Statistical Consultant for HCFAs Medicare Infrastructure Project. Dr. Thomas Hill is Senior Director for Advanced Analytics (Statistica products) in the TIBCO Analytics group. He previously held positions as Executive Director for Analytics at Statistica, within Quest's and at Dell's Information Management Group. He was a Co-founder and Senior Vice President for Analytic Solutions for over 20 years at StatSoft Inc. until the acquisition by Dell in 2014. At StatSoft, he was responsible for building out Statistica into a leading analytics platform. Dr. Hill received his Vordiplom in psychology from Kiel University in Germany, earned an M.S. in industrial psychology and a Ph.D. in psychology from the University of Kansas. He was on the faculty of the University of Tulsa from 1984 to 2009, where he conducted research in cognitive science and taught data analysis and data mining courses. He has received numerous academic grants and awards from the National Science Foundation, the National Institute of Health, the Center for Innovation Management, the Electric Power Research Institute, and other institutions. Over the past 20 years, his team has completed diverse consulting projects with companies from practically all industries in the United States and internationally on identifying and refining effective data mining and predictive modeling / analytics solutions for diverse applications. Dr. Hill has published widely on innovative applications for data mining and predictive analytics. He is the author (with Paul Lewicki, 2005) of Statistics: Methods and Applications, the Electronic Statistics Textbook (a popular on-line resource on statistics and data mining), a co-author of Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications (2012) and Practical Predictive Analytics and Decisioning Systems for Medicine (2014); he is also a contributing author to the popular Handbook of Statistical Analysis and Data Mining Applications (2009). Dr. Hill also authored numerous patents related to data science, Machine Learning, and specialized applications of of analytics to various domains.