Using predictive analytics to improve healthcare outcomes:
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2021
|
| Schlagworte: | |
| Online-Zugang: | FHD01 |
| Beschreibung: | 1 Online-Ressource (xxv, 438 Seiten) |
| ISBN: | 9781119747772 |
Internformat
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| 245 | 1 | 0 | |a Using predictive analytics to improve healthcare outcomes |c edited by John W. Nelson, Jayne Felgen, Mary Ann Hozak |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2021 | |
| 300 | |a 1 Online-Ressource (xxv, 438 Seiten) | ||
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| 337 | |b c |2 rdamedia | ||
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| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 Caring to Improve Safety Outcomes -- The Profile of Caring -- Machine Learning -- Exploration of Two Variables of Interest: Early Readmission for Heart Failure and Falls | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 650 | 4 | |a Medicine / Research | |
| 650 | 4 | |a Predictive analytics | |
| 650 | 7 | |a Medicine / Research |2 fast | |
| 650 | 7 | |a Predictive analytics |2 fast | |
| 700 | 1 | |a Nelson, John W. |4 edt | |
| 700 | 1 | |a Felgen, Jayne |4 edt | |
| 700 | 1 | |a Hozak, Mary Ann |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, hardback |z 978-1-119-74775-8 |
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Datensatz im Suchindex
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| author2 | Nelson, John W. Felgen, Jayne Hozak, Mary Ann |
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| author_facet | Nelson, John W. Felgen, Jayne Hozak, Mary Ann |
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| 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 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 -- 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 |
| ctrlnum | (OCoLC)1277026857 (DE-599)BVBBV047496444 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:17:21Z |
| indexdate | 2024-07-10T09:13:42Z |
| institution | BVB |
| isbn | 9781119747772 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032897598 |
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| physical | 1 Online-Ressource (xxv, 438 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE FHD01_PQE_Kauf |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Wiley |
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| spelling | Using predictive analytics to improve healthcare outcomes edited by John W. Nelson, Jayne Felgen, Mary Ann Hozak Hoboken, NJ Wiley 2021 1 Online-Ressource (xxv, 438 Seiten) txt rdacontent c rdamedia cr rdacarrier 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 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 -- 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 Medicine / Research Predictive analytics Medicine / Research fast Predictive analytics fast Nelson, John W. edt Felgen, Jayne edt Hozak, Mary Ann edt Erscheint auch als Druck-Ausgabe, hardback 978-1-119-74775-8 |
| spellingShingle | Using predictive analytics to improve healthcare outcomes 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 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 -- 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 Medicine / Research Predictive analytics Medicine / Research fast Predictive analytics fast |
| title | Using predictive analytics to improve healthcare outcomes |
| title_auth | Using predictive analytics to improve healthcare outcomes |
| title_exact_search | Using predictive analytics to improve healthcare outcomes |
| title_exact_search_txtP | Using predictive analytics to improve healthcare outcomes |
| title_full | Using predictive analytics to improve healthcare outcomes edited by John W. Nelson, Jayne Felgen, Mary Ann Hozak |
| title_fullStr | Using predictive analytics to improve healthcare outcomes edited by John W. Nelson, Jayne Felgen, Mary Ann Hozak |
| title_full_unstemmed | Using predictive analytics to improve healthcare outcomes edited by John W. Nelson, Jayne Felgen, Mary Ann Hozak |
| title_short | Using predictive analytics to improve healthcare outcomes |
| title_sort | using predictive analytics to improve healthcare outcomes |
| topic | Medicine / Research Predictive analytics Medicine / Research fast Predictive analytics fast |
| topic_facet | Medicine / Research Predictive analytics |
| work_keys_str_mv | AT nelsonjohnw usingpredictiveanalyticstoimprovehealthcareoutcomes AT felgenjayne usingpredictiveanalyticstoimprovehealthcareoutcomes AT hozakmaryann usingpredictiveanalyticstoimprovehealthcareoutcomes |