Franklin

Using predictive analytics to improve healthcare outcomes / edited by John W. Nelson, Jayne Felgen, Mary Ann Hozak.

Publication:
Newark : Wiley, 2021.
Format/Description:
Book
1 online resource (467 p.)
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Subjects:
Medicine -- Research.
Predictive analytics.
Contents:
Cover
Title Page
Copyright Page
Contents
Contributors
Foreword
Preface: Bringing the Science of Winning to Healthcare
List of Acronyms
Acknowledgments
Section One Data, Theory, Operations, and Leadership
Chapter 1 Using Predictive Analytics to Move from Reactive to Proactive Management of Outcomes
The Art and Science of Making Data Accessible
Summary 1: The "Why"
Summary 2: The Even Bigger "Why"
Implications for the Future
Chapter 2 Advancing a New Paradigm of Caring Theory
Maturation of a Discipline
Theory
Frameworks of Care
RBC's Four Decades of Wisdom
Summary
Chapter 3 Cultivating a Better Data Process for More Relevant Operational Insight
Taking on the Challenge
"PSI RNs": A Significant Structural Change to Support Performance and Safety Improvement Initiatives and Gain More Operational Insight
The Importance of Interdisciplinary Collaboration in Data Analysis
Key Success Factors
Summary
Chapter 4 Leadership for Improved Healthcare Outcomes
Data as a Tool to Make the Invisible Visible
Leaders Using Data for Inspiration: Story 1
Leaders Using Data for Inspiration: Story 2
How Leaders Can Advance the Use of Predictive Analytics and Machine Learning
Understanding an Organization's "Personality" Through Data Analysis
Section Two Analytics in Action
Chapter 5 Using Predictive Analytics toReduce Patient Falls
Predictors of Falls, Specified in Model 1
Lessons Learned from This Study
Respecifying the Model
Summary
Chapter 6 Using the Profile of CaringĀ® to Improve Safety Outcomes
The Profile of Caring
Machine Learning
Exploration of Two Variables of Interest: Early Readmission for Heart Failure and Falls
Proposal for a Machine Learning Problem
Constructing the Study for Our Machine Learning Problem
Chapter 7 Forecasting Patient Experience: Enhanced Insight Beyond HCAHPS Scores
Methods to Measure the Patient Experience
Results of the First Factor Analysis
Implications of This Factor Analysis
Predictors of Patient Experience
Discussion
Transforming Data into Action Plans
Summary
Chapter 8 Analyzing a Hospital-Based Palliative Care Program to Reduce Length of Stay
Building a Program for Palliative Care
The Context for Implementing a Program of Palliative Care
Building a Model to Study Length of Stay in Palliative Care
Demographics of the Patient Population for Model 1
Results from Model 1
Respecifying the Model
Discussion
Chapter 9 Determining Profiles of Risk to Reduce Early Readmissions Due to Heart Failure
Step 1: Seek Established Guidelines in the Literature
Step 2: Crosswalk Literature with Organization's Tool
Step 3: Develop a Structural Model of the 184 Identified Variables
Step 4: Collect Data
Details of the Study
Limitations of the Study
Results: Predictors of Readmission in Fewer Than 30 Days
Next Steps
Notes:
Description based upon print version of record.
Chapter 10 Measuring What Matters in a Multi-Institutional Healthcare System.
Includes bibliographical references and index.
Electronic reproduction. Hoboken, N.J. Available via World Wide Web.
Contributor:
Nelson, John W.
Felgen, Jayne.
Hozak, Mary Ann.
Wiley InterScience (Online service)
Other format:
Print version: Nelson, John W. Using Predictive Analytics to Improve Healthcare Outcomes
ISBN:
9781119747826
1119747821
9781119747772
1119747775
1119747759
9781119747758
Publisher Number:
99988112141
10.1002/9781119747826 doi
Access Restriction:
Restricted for use by site license.