Staff View
MARC
| LEADER | 00000nmm a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047496444 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 211005s2021 |||| o||u| ||||||eng d | ||
| 020 | |a 9781119747772 |9 978-1-119-74777-2 | ||
| 035 | |a (OCoLC)1277026857 | ||
| 035 | |a (DE-599)BVBBV047496444 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-1050 | ||
| 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) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 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 |
| 912 | |a ZDB-30-PQE | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032897598 | ||
| 966 | e | |u https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=6675137 |l FHD01 |p ZDB-30-PQE |q FHD01_PQE_Kauf |x Aggregator |3 Volltext | |
Record in the Search Index
| _version_ | 1804182825537110016 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author2 | Nelson, John W. Felgen, Jayne Hozak, Mary Ann |
| author2_role | edt edt edt |
| author2_variant | j w n jw jwn j f jf m a h ma mah |
| author_facet | Nelson, John W. Felgen, Jayne Hozak, Mary Ann |
| building | Verbundindex |
| bvnumber | BV047496444 |
| collection | ZDB-30-PQE |
| 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 |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04300nmm a2200421 c 4500</leader><controlfield tag="001">BV047496444</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">211005s2021 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781119747772</subfield><subfield code="9">978-1-119-74777-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1277026857</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047496444</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-1050</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using predictive analytics to improve healthcare outcomes</subfield><subfield code="c">edited by John W. Nelson, Jayne Felgen, Mary Ann Hozak</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="b">Wiley</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xxv, 438 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Medicine / Research</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Predictive analytics</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Medicine / Research</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Predictive analytics</subfield><subfield code="2">fast</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nelson, John W.</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Felgen, Jayne</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hozak, Mary Ann</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, hardback</subfield><subfield code="z">978-1-119-74775-8</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032897598</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=6675137</subfield><subfield code="l">FHD01</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">FHD01_PQE_Kauf</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV047496444 |
| 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 |
| oclc_num | 1277026857 |
| open_access_boolean | |
| owner | DE-1050 |
| owner_facet | DE-1050 |
| 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 |
| record_format | marc |
| 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 |