Using Predictive Analytics to Improve Healthcare Outcomes. Группа авторов
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Table of Contents
1 Cover
6 Foreword
7 Preface: Bringing the Science of Winning to Healthcare
10 Section One: Data, Theory, Operations, and Leadership 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 2 Advancing a New Paradigm of Caring Theory Maturation of a Discipline Theory Frameworks of Care RBC's Four Decades of Wisdom Summary 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 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
11 Section Two: Analytics in Action 5 Using Predictive Analytics to Reduce Patient Falls Predictors of Falls, Specified in Model 1 Lessons Learned from This Study Respecifying the Model Summary 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 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 8 Analyzing a Hospital‐Based Palliative Care Program to Reduce Length of Stay Building a Program for Palliative Care Demographics of the Patient Population for Model 1 Results from Model 1 Respecifying the Model Discussion 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