Using Predictive Analytics to Improve Healthcare Outcomes

John W. Nelson, Jayne Felgen, Mary Ann Hozak, John W. Nelson, Jayne Felgen, Mary Ann Hozak

• Publisher: Wiley
• Year: 2021
• Print ISBN: 9781119747758
• eBook ISBN: 9781119747802
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

Using Predictive Analytics to Improve Healthcare Outcomes Winner of the American Journal of Nursing (AJN) Informatics Book of the Year Award 2021!

Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement.

Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a "how-to" manual, showing how to use existing theory and tools to achieve desired positive outcomes.

You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes:

• A thorough introduction to data, caring theory, Relationship-Based Care ®, the Caring Behaviors Assurance System ©, and healthcare operations, including how to build a measurement model and improve organizational outcomes.
• An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes.
• Discussions of how to refine quality improvement initiatives, including the use of "comfort" as a construct to illustrate the importance of solid theory and good measurement in adequate pain management.
• An examination of international organizations using analytics to improve operations within cultural context.

Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.

Appendix G
Crosswalk Hospital Tool and Guidelines

This is a crosswalk of the guidelines found in the literature with the specifics of the organization's customized tool. Get with the Guidelines® (GWTG), which is the tool provided by the American Heart Association, was the foundation of this organization's tool. ...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright Page
• Dedication Page
• Contributors
• Foreword
• Preface: Bringing the Science of Winning to Healthcare
• List of Acronyms
• Acknowledgments
• Section One: Data, Theory, Operations, and Leadership
• Section Two: Analytics in Action
• Section Three: Refining Theories to Improve Measurement
• Section Four: International Models to Study Constructs Globally
• Epilogue: Imagining What Is Possible
• Appendix A: Worksheets Showing the Progression from a Full List of Predictor Variables to a Measurement Model
• Appendix B: The Key to Making Your Relationship-Based Care® Implementation Sustainable Is “I2E2”
• Appendix D: Calculation for Cost of Falls
• Appendix D: Possible Clinical, Administrative, and Psychosocial Predictors of Readmission for Heart Failure in Fewer Than 30 Days After Discharge
• Appendix E: Process to Determine Variables for Lee, Jin, Piao, & Lee, 2016 Study
• Appendix F: Summary of National and International Heart Failure Guidelines
• Appendix G: Crosswalk Hospital Tool and Guidelines
• Appendix H: Comprehensive Model of 184 Variables Found in Guidelines and Hospital Tool
• Appendix I: Summary of Variables That Proved Insignificant After Analysis
• Appendix J: Summary of Inconclusive Findings
• Appendix K: Nine Tools for Measuring the Provision of Quality Patient Care and Related Variables
• Appendix L: Data From Pause and Flow Study Related to Participants’ Ability to Recall Moments of Pause and Flow Easily or with Reflection
• Appendix M: Identified Pauses and Proposed Interventions Resulting from a Pause and Flow Study
• Appendix N: Factors Related to a Focus on Pain Versus Factors Related to a Focus on Comfort
• Appendix O: Comfort/Pain Perception Survey (CPPS)—Patient Version
• Appendix P: Comfort/Pain Perception Survey (CPPS)—Care Provider Version
• Appendix Q: Predictors of OUD
• Appendix R: Personal Qualities of Clinicians and Others Suited to Become Trusted Others
• Appendix S: Qualities of Systems and Organizations Suited to Serve People Recovering from OUD
• Appendix T: Factor Loadings for Satisfaction with Staffing/Scheduling and Resources
• Appendix U: Detail Regarding Item Reduction of Instruments to Measure Caring
• Appendix V: Factor Loading for Items in the Healing Compassions Assessment (HCA) for Use in Western Scotland
• Appendix W: Factor Loadings of the Caring Professional Scale for Use in Western Scotland
• Appendix X: Factor Loadings for the Healing Compassion Survey—7Cs NHS Scotland (Staff Version)
• Appendix Y: Factor Analysis and Factor Ranking for Survey Items Related to Caring for Self and Caring of the Senior Charge Nurse
• Appendix Z: Demographics, Particularly Ward, as Predictors of Job Satisfaction
• Appendix AA: Demographic as Predictors of clarity
• Appendix BB: Correlates of Operations of CBAS with Items from the Healing Compassion Survey—7 Cs NHS Scotland (Staff Version)
• References
• Index
• End User License Agreement