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

Format:

Book

Contributor:

Nelson, John W.

Felgen, Jayne

Hozak, Mary Ann.

Wiley InterScience (Online service)

Louis B. Flexner Medical Book Fund.

Language:

English

Subjects (All):

Medicine--Research.

Medicine.

Predictive analytics.

Physical Description:

1 online resource (467 pages)

Place of Publication:

Newark : Wiley, 2021.

System Details:

text file

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

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

Chapter 6 Using the Profile of Caring® 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

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

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.

Local Notes:

Acquired for the Penn Libraries with assistance from the Louis B. Flexner Medical Book Fund.

Other Format:

Print version: Nelson, John W. Using Predictive Analytics to Improve Healthcare Outcomes

ISBN:

9781119747826

1119747821

9781119747772

1119747775

Publisher Number:

99988112141

Access Restriction:

Restricted for use by site license.