Practical Predictive Analytics and Decisioning Systems for Medicine
Informatics Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery Including Medical Research
- 1st Edition - September 23, 2014
- Authors: Gary D. Miner, Linda A. Miner, Mitchell Goldstein, Robert Nisbet, Nephi Walton, Pat Bolding, Joseph Hilbe, Thomas Hill
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 0 0 6 2 - 2
- Hardback ISBN:9 7 8 - 0 - 1 2 - 4 1 1 6 4 3 - 6
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 1 1 6 4 0 - 5
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 do… Read more
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Request a sales quoteWith 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 cost–efficient 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.
- Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research
- Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations
- Demonstrates methods to help sort through data to make better observations and allow you to make better predictions
Research Scientists, Biomedical Researchers, Data scientists, Data Analysts, Statistical Analysts, Academics, Operations and Market Researchers
- Dedication
- Foreword by Thomas H. Davenport
- Foreword by James Taylor
- Foreword by John Halamka
- Preface
- Modern Medicine: An Exercise in Prediction and Preparation
- Wasted Costs in American Healthcare Systems
- References
- About the Authors
- Acknowledgments
- Guest Authors
- Software Instructions
- Introduction
- Organization of This Book – Why We Did It This Way
-
- Prologue to Part 1
- Prologue to Part 1
- Part 1: Historical Perspective and the Issues of Concern for Healthcare Delivery in the 21st Century
- Chapter 1. History of Predictive Analytics in Medicine and Health Care
- Preamble
- Background
- Introduction
- Part 1: Development of Bodies of Medical Knowledge
- Earliest Medical Records in Ancient Cultures
- Classification of Medical Practices in Ancient and Modern Cultures
- Medical Practice Documents in Major Ancient World Cultures of Europe and the Middle East
- Summary of Royal Decrees of Medical Documentation in Ancient Cultures
- Effects of the Middle Ages on Medical Documentation
- Rebirth of Interest in Medical Documentation During the Renaissance
- Medical Documentation Since the Enlightenment
- Part 2: Analytical and Decision Systems in Medicine and Health Care
- Computers and Medical Databases
- Best Practice Guidelines
- Postscript
- References
- Chapter 2. Why did We Write This Book?
- Preamble
- Introduction
- Reason 1: Current Problems in Medical Research
- Reason 2: Practical Assistance is Needed to Insure Success for the New Initiatives and Accreditation Standards
- Reason 3: To Meet The Standards, Healthcare Organizations Need Practical Assistance and Tools With Implementing Lean Systems
- Reason 4: Research into Technological/Organizational/Payment Changes Will be Necessary
- Reason 5: Practical Real World Examples are Needed that Bridge into A Phenomenal Future
- Postscript
- References
- Chapter 3. Biomedical Informatics
- Preamble
- The Rise of Predictive Analytics in Health Care
- Moving From Reactive to Proactive Response in Health Care
- Medicine and Big Data
- An Approach to Predictive Analytics Projects
- Meaningful Use
- Translational Bioinformatics
- Clinical Decision Support Systems
- Consumer Health Informatics
- Direct-to-Consumer Genetic Testing
- Use of Predictive Analytics to Avoid an Undesirable Future
- Consumer Health Kiosks
- Patient Monitoring Systems
- Public Health Informatics
- Medical Imaging
- Clinical Research Informatics
- Intelligent Search Engines
- Personalized Medicine
- Hospital Optimization
- Challenges
- Summary
- Postscript
- References
- Further Reading
- Chapter 4. HIMSS and Organizations That Develop HIT Standards
- Preamble
- Introduction
- Relationship Between ANSI, HIMSS, and ONC
- Organizations Connected to or Influenced By HIMSS
- Goals, Issues, and Ideals of HIMSS
- ICD-10
- HIMSS Attempts to Help
- Standardization in Coding
- Care Continuum Alliance (Another CCA) and Health Outcome Data
- HIMSS Website
- HIMSS Analytics
- Progress of HIMSS
- Long-Range Problems and Opportunities
- Some Questions
- The Challenge
- Postscript
- References
- Chapter 5. Electronic Medical Records: Analytics’ Best Hope
- Preamble
- Introduction
- What is an EMR?
- A Bit (Of a “Byte”) of History …
- Why aren’t We There Yet?
- Ferraris and Country Roads
- Postscript
- References
- Bibliography of Additional References on the Topic of Medical Records
- Chapter 6. Open-Source EMR and Decision Management Systems
- Preamble
- Introduction
- Why Choose an Open-Source EMR Software Application?
- Vista – The Veterans Administration System That Started it All
- Five of the Best Open-Source EMR Systems for Medical Practices
- Global Open-Source EMR Systems and the Future of Analytics
- Postscript
- References
- Chapter 7. Evidence-Based Medicine
- Preamble
- Introduction
- Geodemographic Elements of Medical Treatment
- How can we Define the Nature and Boundaries of EBM?
- General Problems with EBM
- Evidence-Based Medicine and Analytics
- The Path to Evidence
- What is a Randomized Controlled Trial?
- If not Evidence Based, then What?
- The EBM Process
- Evidence at the Bedside
- What do Patients Think?
- Evidence-Based Medicine Versus the Art of Medicine
- Predictive Analytics and EBM
- Postscript
- References
- Chapter 8. ICD-10
- Preamble
- Introduction
- Rise of the ICD
- Why the ICD?
- Elements of ICD Documentation
- The ICD Timetable
- Changes Ahead for ICD-10 Users
- Comparison of ICD-9 and ICD-10
- Implications of ICD-10 Changes
- ICD-10 Codes in Practice
- ICD-10 Changes in Terminology
- Implementation Issues of Changing to ICD-10
- What Lies Ahead for Payers and Providers?
- Transition is a Joint Effort
- Postscript
- References
- Chapter 9. “Meaningful Use” – The New Buzzword in Medicine
- Preamble
- Introduction
- Stage I of “Meaningful Use”
- Meaningful Use Goals for Hospitals
- Meaningful Use Goals For Doctors
- Meaningful Use Requirements of Stage I, Stage II, and Stage III
- Postscript
- Bibliography
- Chapter 10. The Joint Commission: Formerly the Joint Commission on Accreditation of Healthcare Organizations (JCAHO)
- Preamble
- History of the Joint Commission
- The Joint Commission International
- Joint Commission Accreditation
- Other Regulatory Organizations
- Joint Commission Standards
- National Patient Safety Goals
- Postscript
- References
- Chapter 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
- Introduction
- Part 1: Six Sigma and Quality Control, Root Cause Analysis, and Leapfrog as they Developed During the 1990s and Early 2000s: Learning from Medical Errors and Turning Them into Quality Improvements
- Definitions
- Methods to Improve Safety and Reduce Error
- History of Quality in Health Care
- The Leapfrog Initiative
- Part 2: Root Cause Analysis, Six Sigma and Quality Control, and Lean Concepts in Hospitals and Healthcare Facilities as They Exist in 2013–2014
- Six Sigma
- Quality Control
- Lean Concepts for Health Care: The Lean Hospital as a Methodology of Six Sigma
- Root Cause Analysis
- Part 3: Experiences of a Doctor who Implemented a Quality Control Department in a Hospital System During the 1990s – An Era When Quality was Anything But the Norm
- Quality Improvement
- Quality of Care Examples
- Postscript
- References
- Chapter 12. Lean Hospital Examples
- Preamble
- Introduction
- Lean Kaizen Concepts
- Henry Ford Hospitals
- The Joint Commission Annual Report, 2013
- Kaiser Permanente Managed Care Organization
- Virginia Mason Hospital in Seattle
- Examples of Lean Projects
- Summary
- Postscript
- References
- Chapter 13. Personalized Medicine
- Preamble
- What is Personalized Medicine?
- Personalized Medicine, Genomics, and Pharmacogenomics
- Changing the Definition of Diseases
- Systems Biology
- Efficacy of Current Methods – Why We Need Personalized Medicine
- Predictive Analytics in Personalized Medicine
- The Future: Predictive and Prescriptive Medicine
- The Diversity of Available Healthcare Data
- All the Other “Omics”
- The Future
- Postscript
- References
- Chapter 14. Patient-Directed Health Care
- Preamble
- The Empowered Patient
- Patient Defined
- Concept 1: Empowerment and Involvement – How Can Patients be Empowered to Become More Involved with their Medical Care?
- Concept 2: Coordination of Care and Communication
- Concept 3: Consumerism in Health Care
- Concept 4: Patient Payment Models
- Concept 5: Patient Education and Patient Self-Education and Decisions
- Concept 6: Alternatives and New Models
- Conclusion
- Postscript
- References
- Further Reading
- Chapter 1. History of Predictive Analytics in Medicine and Health Care
- Prologue to Part 1
-
- Prologue to Part 1, Chapter 15
- Prologue to Part 1, Chapter 15
- Chapter 15. Prediction in Medicine – The Data Mining Algorithms of Predictive Analytics
- Preamble
- Introduction
- The Use of Simple Descriptive Statistics, Graphics, and Visual Data Mining in Predictive Analytics
- Predictive Modeling: Using Data to Predict Important Outcomes
- Clustering: Identifying Clusters of Similar Cases, and Outliers
- Text Mining Algorithms
- Dimension Reduction Techniques
- Detecting the Interrelationships and Structure of Data Through Association and Link Analysis
- Summary
- Postscript
- References
- Prologue to Part 1, Chapter 15
-
- Prologue to Part 2
- Prologue to Part 2
- Part 2: Practical Step-by-Step Tutorials and Case Studies
- Guest Tutorial Authors
- Tutorial A. Case Study: Imputing Medical Specialty Using Data Mining Models
- Bending the Curve
- Identifying Cost-Efficient Physicians and Networks
- Why Physician Specialty is Important
- Using ETG Data to Impute Specialty
- The Analysis Sample
- Overview of the Data Mining Process
- Data Mining Software
- Data Mining Step by Step
- Testing the Data Mining Models
- Comparing the Performance of Different Models
- Additional Provider Information
- Face Validity of the SVM Model Specialty Reassignment
- Internal Medicine Reassignment
- IM Remaining IM – ETG Frequency
- IM Reclassified as Pulmonologist – ETG Frequency
- IM Reclassified as Gastroenterologist – ETG Frequency
- IM Reclassified as Cardiologist – ETG Frequency
- IM Reclassified as Rheumatologist – ETG Frequency
- Subspecialty Reassignment
- Pulmonologist Reclassified as IM – ETG Frequency
- Gastroenterologist Reclassified as IM – ETG Frequency
- Cardiologist Reclassified as IM – ETG Frequency
- Rheumatologist Reclassified as IM – ETG Frequency
- Models for General Practice/Family Practice
- Models for Pediatrics/General Surgery
- General Comments
- Testing Model Reliability
- Postscript
- Tutorial B. Case Study: Using Association Rules to Investigate Characteristics of Hospital Readmissions
- Objectives/Purpose
- Common Readmission Conditions
- Data Set
- Association Rule Basics
- Data Subsets
- Generation of Association Rules
- Adding Variables – Lift
- Readmission Rules – 30 Days
- Readmission Rules – 180 Days
- Summary
- References
- Tutorial C. Constructing Decision Trees for Medicare Claims Using R and Rattle
- Objective
- About Decision Trees
- About Rattle
- Data Preparation
- Installing R
- Installing Rattle
- Starting Rattle after Installation
- The Rattle Tab Bar
- Importing the Tutorial Text File
- Rattle Data Menu
- Example C2: Predicting Average Drug Cost for Medicare Part D
- The Rattle Log
- Conclusion
- Reference
- Further Reading
- Tutorial D. Predictive and Prescriptive Analytics for Optimal Decisioning: Hospital Readmission Risk Mitigation
- Overview
- The Goal
- Conclusions
- References
- Tutorial E. Obesity – Group: Predicting Medicine and Conditions That Achieved the Greatest Weight Loss in a Group of Obese/Morbidly Obese Patients
- Background
- The Tutorial
- References
- Tutorial F1. Obesity – Individual: Predicting Best Treatment for an Individual from Portal Data at a Clinic
- Introduction
- Background
- The Exercise
- References
- Tutorial 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
- Introduction
- The Exercise
- Tutorial G. Resiliency Study for First and Second Year Medical Residents
- Introduction
- Exercise G1: Predicting Year from Survey Questions
- Exercise G2: Predicting Total Positive Resources and Total Negative Drain from 16PF and Myers & Briggs
- Exercise G3: Predictive Analytics with Decisioning: Using Weight of Evidence
- Appendix to Tutorial G
- References
- Tutorial H. Medicare Enrollment Analysis Using Visual Data Mining
- Introduction
- Medicare Enrollment Data
- Feature Selection and Root Cause Analysis
- 2D Mean Plot Analysis
- Conclusion
- Tutorial I. Case Study: Detection of Stress-Induced Ischemia in Patients with Chest Pain After “Rule-Out ACS” Protocol
- Background
- Case Study
- References
- Tutorial J1. Predicting Survival or Mortality for Patients with Disseminated Intravascular Coagulation (DIC) and/or Critical Illnesses
- Disseminated Intravascular Coagulation in Critically Ill Patients
- Predictive Analytic Exercise
- Conclusion
- References
- Tutorial J2. Decisioning for DIC
- Introduction
- Feature Selection
- Weight of Evidence
- Decisioning/Predictive Procedures
- Tutorial K. Predicting Allergy Symptoms
- Introduction
- Procedure
- Tutorial L. Exploring Discrete Database Networks of TriCare Health Data Using R and Shiny
- Introduction
- Example I: Business-Driven Problem Generation – What do I Want to Ask of My Data?
- Example 2: Data-Driven Problem Generation – “What Can My Data Inform Me of?”
- References
- Further Reading
- Tutorial M. Schistosomiasis Data from WHO
- Introduction
- The Tutorial
- Part 1: Cleaning Data and Using Feature Selection as a Beginning Predictive Technique
- Part 2: Examining the Original Data Using Statistica’s Data Health Check Module
- Some Thoughts on Data Cleaning
- References
- Tutorial N. The Poland Medical Bundle
- Introduction
- Data Verification
- Missing Data Analysis
- ROC Curves
- Meta-Analysis and Meta-Regression
- Logistic Regression Wizard
- References
- Tutorial O. Medical Advice Acceptance Prediction
- Introduction
- Background
- The Tutorial
- Tutorial P. Using Neural Network Analysis to Assist in Classifying Neuropsychological Data
- Introduction
- Examining the Data
- Reference
- Tutorial Q. Developing Interactive Decision Trees Using Inpatient Claims (with SAS Enterprise Miner)
- About Decision Trees
- About SAS© Enterprise Miner
- Data File Description
- Creating a New Project
- The Enterprise Miner Toolbar
- Importing the Data File into SAS Using Enterprise Miner
- Create a Data Source
- Assigning Roles
- Enterprise Miner Nodes
- Reading a Data Source
- Exploring the Data
- Filtering the Data
- Viewing Filter Results
- Partitioning the Data
- Decision Tree Modes
- Conclusion
- Acknowledgments
- Tutorial R. Divining Healthcare Charges for Optimal Health Benefits Under the Affordable Care Act
- Introduction
- Pre-Tutorial Background on Original Data Set
- Hospital Charge Analysis
- Quality Matters
- Tutorial S. Availability of Hospital Beds for Newly Admitted Patients: The Impact of Environmental Services on Hospital Throughput
- Introduction
- Data Extraction
- Running the Feature Selection for the EVS Throughput Tutorial Data Set
- Tutorial T. Predicting Vascular Thrombosis: Comparing Predictive Analytic Models and Building an Ensemble Model for “Best Prediction”
- Introduction
- Tutorial
- References
- Tutorial U. Predicting Breast Cancer Diagnosis Using Support Vector Machines
- Introduction
- Data Analysis and Exploration
- Modeling Using Support Vector Machine with Deployment
- Rapid Deployment, Cross-Validating, and Predicting on Different Data
- Summary
- Data Set Locations
- Tutorial V. Heart Disease: Evaluating Variables That Might Have an Effect on Cholesterol Level (Using Recode of Variables Function)
- Aim
- Tutorial Steps
- Tutorial W. Blood Pressure Predictive Factors
- Background
- Data Review
- Data Preparation
- Data Importation
- Variable Typing
- Pattern Discovery
- Conclusion
- Tutorial X. Gene Search and the Related Risk Estimates: A Statistical Analysis of Prostate Cancer Data
- Background and the Benchmark Data
- Visualization (I): Categorized Histograms and Matrix Plots
- The Benjamini-Hochberg FDR (False Discovery Rate)
- Prescreening and Dimension Reduction
- Lasso, Adaptive Lasso, and Elastic Net
- Hold-Out Data and Over-Fitting
- Penalized Support Vector Machines
- Conflicting Results from the Tree Methods
- Visualization (II): Linear vs. Non-Linear Models
- The Benjamini-Hochberg FDR and Non-Parametric Tests
- Hybrid Methods
- Visualization (III): Seeing Can Be Deceiving
- Biomarkers and Visualization (IV)
- Concluding Remarks and the Limitations of Statistical Analysis of Gene Data
- Appendices
- References
- Tutorial Y. Ovarian Cancer Prediction via Proteomic Mass Spectrometry
- Background and the Data
- Data Preprocessing (I) and Dynamic Binning
- Prediction Accuracies of Competing Models
- False Positive, False Negative, and the Roc Index
- Probability of Cancer
- Limitation of Mass Spectrometry and Data Preprocessing (II)
- Variable Selection and Gene Search
- Appendices A–C
- References
- Tutorial Z. Influence of Stent Vendor Representatives in the Catheterization Lab
- Introduction and Review of the Literature
- Definition of Terms
- Method of Gathering Data
- Exploratory Analysis
- Drug Eluting Stent Output
- Conclusion
- References
- Prologue to Part 2
-
- Prologue to Part 3
- Prologue to Part 3
- Part 3: Practical Solutions and Advanced Topics in Administration and Delivery of Health Care Including Practical Predictive Analytics for Medicine
- Chapter 16. Predictive Analytics in Nursing Informatics
- Preamble
- Introduction
- Nursing Informatics
- Postscript
- References
- Chapter 17. The Predictive Potential of Connected Digital Health
- Preamble
- Why Don’t Clinicians Embrace Digital Consumer Connections?
- Promise and Problems of Shifting to Mobile Health Technology
- What Can We Learn from the VA About the Potential of Predictions?
- What Can We Learn from Financial Services Regarding Digital Transformation?
- Summary and Recommendations
- Postscript
- References
- Chapter 18. Healthcare Fraud
- Preamble
- Introduction
- Leakage Due to Fraud
- Definition of Fraud in the Healthcare Context
- Statutes and Regulations Intended to Prevent, Detect, and Prosecute Fraud
- Major Agencies Involved in Healthcare Anti-Fraud Efforts
- Challenges That Face Anti-Fraud Efforts
- Traditional Means of Detection
- The Emergence of Big Data in Healthcare Investigations
- Analytical Anti-Fraud Approaches
- The Future of Healthcare Anti-Fraud Efforts
- Anti-Fraud Organizations
- Postscript
- References
- Chapter 19. Challenges for Healthcare Administration and Delivery: Integrating Predictive and Prescriptive Modeling into Personalized Health Care
- Preamble
- Challenges
- Postscript
- References
- Chapter 20. Challenges of Medical Research for the Remainder of the 21st Century
- Preamble
- Challenges
- Postscript
- Chapter 21. Introduction to the Cornerstone Chapters of this Book, Chapters 22–25: The “Three Processes” – Quality Control, Predictive Analytics, and Decisioning
- Preamble
- Introduction
- Traditional Statistics vs Data Mining vs Predictive Analytics
- Postscript
- Chapter 22. The Nature of Insight from Data and Implications for Automated Decisioning: Predictive and Prescriptive Models, Decisions, and Actions
- Preamble
- Overview
- The Nature of Insight and Expertise
- Statistical Analysis vs Pattern Recognition
- Predictive Modeling and Prescriptive Models
- Summary
- Postscript
- References
- Chapter 23. Platform for Data Integration and Analysis, and Publishing Medical Knowledge as Done in a Large Hospital
- Preamble
- Introduction
- Functions and Applications of the Platform
- Platform Components and Architecture
- Conclusions
- Postscript
- References
- Chapter 24. Decisioning Systems (Platforms) Coupled With Predictive Analytics in a Real Hospital Setting – A Model for the World
- Preamble
- Introduction
- Setting the Stage for a Decisioning Platform
- Deploying the Decision Management System
- Conclusion
- Postscript
- References
- Chapter 25. IBM Watson for Clinical Decision Support
- Preamble
- Introduction
- Personalized Health Care and Clinical Decision Support
- IBM Watson and Medical Decision-Making
- Postscript
- References
- Chapter 26. 21st Century Health Care and Wellness: Getting the Health Care Delivery System That Meets Global Needs: Cost-Effective, Evidence Based, Meaningful Use Perfected, and Effective Delivery for a New “Personalized Prescriptive Medicine” Resulting from Rapid Decisions Derived from Accurate Predictive Analytics Models
- Introduction
- Background and Need for Change
- Learning Objectives
- Trends Impacting Healthcare Industries
- Existing and Emerging Healthcare Organizations
- Health Start-Ups and Established Technology Firms Contributing to Health Care
- Technology Trends That Impact Health and Wellness
- Trends and Expectations for the Future of Health IT and Analytics
- Conclusions and Summary of Important Concepts Presented in This Book
- References
- Bibliography
- Chapter 16. Predictive Analytics in Nursing Informatics
- Prologue to Part 3
- Index
- No. of pages: 1110
- Language: English
- Edition: 1
- Published: September 23, 2014
- Imprint: Academic Press
- Paperback ISBN: 9780128100622
- Hardback ISBN: 9780124116436
- eBook ISBN: 9780124116405
GM
Gary D. Miner
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 Alzheimer’s 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. Miner’s 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 Dell’s 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’.
Affiliations and expertise
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, USALM
Linda A. Miner
Linda A. Winters-Miner, PhD, earned her bachelor’s and master’s 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.
Affiliations and expertise
Professor Emeritus and Director of Academic Programs, Southern Nazarene University; Editorial Board, The Journal of Geriatric Psychiatry and Neurology; Private Consulting, Tulsa, OK, USAMG
Mitchell Goldstein
Dr. Goldstein MD, FAAP attended the University of Miami’s 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 Children’s 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 EMR’s 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 PACLAC’s Neonatal Guidelines of Care as well as the Principal author of both the National Perinatal Association’s 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. Goldstein’s 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.
Affiliations and expertise
Professor of Pediatrics, Division of Neonatology, Director Neonatal ECMO Program; Loma Linda School of Medicine, Loma Linda University Children’s Hospital; Loma Linda, CA, USARN
Robert Nisbet
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.
Affiliations and expertise
Researcher-Medical Informatics, H.H. Chao Comprehensive Digestive Disease Center, University of California Irvine Medical Center, Private Consulting, Santa Barbara, CA, USANW
Nephi Walton
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.
Affiliations and expertise
Associate Medical Director, Intermountain Healthcare, Precision Genomics, Salt Lake City, UT, USAPB
Pat Bolding
Pat Bolding, MD, FAAFP is a practicing board certified family physician. He has used an EMR (Electronic Medical Record) since his residency training in the mid 1980’s 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 finally 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.
Affiliations and expertise
Warren-St. Francis Medical Clinics, Private Family Practice Physician, Southern Nazarene University, Tulsa, OK, USAJH
Joseph Hilbe
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 1990’s 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, Genentech’s National Registry for Myocardial Infarctions; lead biostatistical consultant, Hoffman-La Roche’s National Canadian Registry for Cardiovascular Disease; and was Senior Statistical Consultant for HCFA’s Medicare Infrastructure Project.
Affiliations and expertise
Arizona State University, Phoenix, AZ, USATH
Thomas Hill
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.
Affiliations and expertise
StatSoft, Inc., Tulsa, OK, USARead Practical Predictive Analytics and Decisioning Systems for Medicine on ScienceDirect