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ویرایش: [1 ed.]
نویسندگان: John W. Nelson
سری:
ISBN (شابک) : 1119747759, 9781119747758
ناشر: Wiley
سال نشر: 2021
تعداد صفحات: 466
[467]
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 8 Mb
در صورت تبدیل فایل کتاب Using Predictive Analytics to Improve Healthcare Outcomes به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب استفاده از تجزیه و تحلیل پیش بینی کننده برای بهبود نتایج مراقبت های بهداشتی نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
استفاده از تجزیه و تحلیل پیش بینی کننده برای بهبود نتایج مراقبت های بهداشتی برنده جایزه کتاب سال انفورماتیک مجله پرستاری آمریکا (AJN) 2021! یک مرور کلی از رهبران تثبیت شده در این زمینه، در مورد نحوه استفاده از تجزیه و تحلیل پیش بینی و سایر روش های تحلیلی برای بهبود کیفیت مراقبت های بهداشتی کشف کنید. استفاده از تجزیه و تحلیل پیشبینیکننده برای بهبود نتایج مراقبتهای بهداشتی، یک فرآیند 16 مرحلهای را برای استفاده از تجزیه و تحلیل پیشبینیکننده برای بهبود عملیات در صنعت پیچیده مراقبتهای بهداشتی ارائه میکند. این کتاب شامل مطالعات موردی متعددی است که از تجزیه و تحلیل پیشبینیکننده و سایر روشهای ریاضی برای صرفهجویی در هزینه و بهبود نتایج بیمار استفاده میکند. این کتاب به عنوان یک کتابچه راهنمای \"چگونه\" سازماندهی شده است، که نشان می دهد چگونه از نظریه و ابزار موجود برای دستیابی به نتایج مثبت مورد نظر استفاده کنید. شما یاد خواهید گرفت که چگونه سازمان شما می تواند از تجزیه و تحلیل پیش بینی برای شناسایی موثرترین مداخلات عملیاتی قبل از تغییر عملیات استفاده کند. این شامل: معرفی کامل داده ها، نظریه مراقبت، مراقبت مبتنی بر رابطه، سیستم تضمین رفتارهای مراقبتی ©، و عملیات مراقبت های بهداشتی، از جمله نحوه ساخت یک مدل اندازه گیری و بهبود نتایج سازمانی است. کاوش در تجزیه و تحلیل در عمل، از جمله مطالعات موردی جامع در مورد زمین خوردن بیمار، مراقبت تسکینی، کاهش عفونت، کاهش نرخ بستری مجدد برای نارسایی قلبی، و موارد دیگر - همه منجر به برنامههای عملی میشوند که به پزشکان اجازه میدهد تغییراتی را انجام دهند که نتیجه آن از قبل ثابت شده است. در نتایج مثبت بحث در مورد چگونگی اصلاح ابتکارات بهبود کیفیت، از جمله استفاده از \"آرامش\" به عنوان سازه ای برای نشان دادن اهمیت تئوری جامد و اندازه گیری خوب در مدیریت کافی درد. بررسی سازمان های بین المللی با استفاده از تجزیه و تحلیل برای بهبود عملیات در بافت فرهنگی. استفاده از تجزیه و تحلیل پیشبینیکننده برای بهبود نتایج مراقبتهای بهداشتی برای مدیران اجرایی، محققان و کارکنان بهبود کیفیت در سازمانهای مراقبتهای بهداشتی و همچنین مربیانی که ریاضیات، علوم داده یا بهبود کیفیت را آموزش میدهند، عالی است. از این منبع ارزشمند استفاده کنید که شما را در مراحل مدیریت و بهینه سازی نتایج در عملیات مراقبت بالینی راهنمایی می کند.
Using Predictive Analytics to Improve Healthcare Outcomes Winner of the American Journal of Nursing (AJN) Informatics Book of the Year Award 2021! Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement. Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a “how-to” manual, showing how to use existing theory and tools to achieve desired positive outcomes. You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes: A thorough introduction to data, caring theory, Relationship-Based Care®, the Caring Behaviors Assurance System©, and healthcare operations, including how to build a measurement model and improve organizational outcomes. An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes. Discussions of how to refine quality improvement initiatives, including the use of “comfort” as a construct to illustrate the importance of solid theory and good measurement in adequate pain management. An examination of international organizations using analytics to improve operations within cultural context. Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.
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 Chapter 10 Measuring What Matters in a Multi-Institutional Healthcare System Testing a Model of Caring Further Discussion Summary Chapter 11 Pause and Flow: Using Physics to Improve the Efficiency of Workflow Types of Pause Types of Flow Methods Sample Size and Response Rates What We Learned About Pause What We Learned About Flow Application of Results to Operations Reflections from the Medical Unit—R6S Analyzing Pause and Flow of Work as a Method of Quality Improvement Summary and Next Steps Chapter 12 Lessons Learned While Pursuing CLABSI Reduction Development of a Specified Model of Measurement for Prevention of CLABSI First Lesson Learned: Quality Data Collection Requires Well-Trained Data Collectors Other Lessons Learned Summary and Next Steps Section Three Refining Theories to Improve Measurement Chapter 13 Theory and Model Development to Address Pain Relief by Improving Comfor t A New Theory Developing a New Model Based on a New Theory Clinicians’ Beliefs Drive Their Practice Dimensions of Comfort Predictors of Comfort The Model Summary Chapter 14 Theory and Model Development to Improve Recovery from Opioid Use Disorder The Current Costs of Opioid Use Disorder (OUD) Interventions for OUD Pain Management, OUD, and Therapeutic Relationships Interventions Which Include Potential Trusted Others Existing OUD Measurement Instruments Updating the Old OUD Measurement Instrument and Model to Include the Trusted Other Discussion Conclusion Section Four International Models to Study Constructs Globally Chapter 15 Launching an International Trajectory of Research in Nurse Job Satisfaction, Starting in Jamaica Background The Hunch: Where Measurement Begins The Model Understanding the Context of Jamaica Methods to Study Job Satisfaction and Clarity in Jamaica Managing Disappointment with the Low Response Rate Results on the Social and Technical Dimensions of Nurse Job Satisfaction in Jamaica Results on the Relationship of Role Clarity and Demographics to Nurse Job Satisfaction in Jamaica Application of the Findings Chapter 16 Testing an International Model of Nurse Job Satisfaction to Support the Quadruple Aim The Four Goals of Our Study Methods Theoretical Framework Measurement Instruments and a Model of Measurement Order of Operations of the Study Simplifying the Model Respecifying the Model to Include Caring Results from Model 2 How Job Satisfaction Relates to Turnover and Sick Time Recommendations Based on Findings Chapter 17 Developing a Customized Instrument to Measure Caring and Quality in Western Scotland Developing an Instrument to Measure Caring as Perceived by the Patient Results Discussion Chapter 18 Measuring the Effectiveness of a Care Delivery Model in Western Scotland The Caring Behaviors Assurance System (CBAS) Implementation of CBAS Measurement of CBAS Findings from the PCQI, the Operations of CBAS Assessment, and the HES Action Planning Discussion Epilogue: Imagining What Is Possible Appendix A Worksheets Showing the Progression from a Full List of Predictor Variables to a Measurement Model Appendix B The Key to Making Your Relationship-Based Care® Implementation Sustainable Is “I2E2” Appendix C Calculation for Cost of Falls Appendix D Possible Clinical, Administrative, and Psychosocial Predictors of Readmission for Heart Failure in Fewer Than 30 Days After Discharge Appendix E Process to Determine Variables for Lee, Jin, Piao, & Lee, 2016 Study Appendix F Summary of National and International Heart Failure Guidelines National Institute for Health and Care Excellence (NICE) Guidelines European Society of Cardiology (ESC), Acute and Chronic Heart Failure Guidelines American College of Cardiology Foundation (ACCF)/American Heart Association (AHA), 2013 and 2017 Guidelines for Management of Heart Failure Appendix GCrosswalk Hospital Tool and Guidelines Appendix H Comprehensive Model of 184 Variables Found in Guidelines and Hospital Tool Appendix I Summary of Variables That Proved Insignificant After Analysis Appendix J Summary of Inconclusive Findings Appendix K Nine Tools for Measuring the Provision of Quality Patient Care and Related Variables Three Tools for Assessing “Caring for the Patient” As Perceived By the Patient Three Tools for Assessing “Caring for the Patient” as Perceived By Staff Members Tools to Measure Self-Care; Nurse Job Satisfaction; and Clarity of Self, Role, and System Appendix L Data From Pause and Flow Study Related to Participants’ Ability to Recall Moments of Pause and Flow Easily or with Reflection Appendix M Identified Pauses and Proposed Interventions Resulting from a Pause and Flow Study Appendix N Factors Related to a Focus on Pain Versus Factors Related to a Focus on Comfort Appendix O Comfort/Pain Perception Survey(CPPS)—Patient Version Appendix P Comfort/Pain Perception Survey (CPPS)—Care Provider Version Appendix Q Predictors of OUD Appendix R Personal Qualities of Clinicians and Others Suited to Become Trusted Others Modeling “Not Knowing” Cultivating Self-Awareness Commitment to Perspective-Seeking Appendix S Qualities of Systems and Organizations Suited to Serve People Recovering from OUD Advancing a Culture in Which “Not Knowing” Is Accepted Advancing a Culture in Which Systemic Awareness Is Actively Sought Advancing a Culture in Which Perspective Seeking Is Prized Appendix T Factor Loadings for Satisfaction with Staffing/Scheduling and Resources Appendix U Detail Regarding Item Reduction of Instruments to Measure Caring Appendix V Factor Loading for Items in the Healing Compassions Assessment (HCA) for Use in Western Scotland Appendix W Factor Loadings of the Caring Professional Scale for Use in Western Scotland Appendix X Factor Loadings for the Healing Compassion Survey—7Cs NHS Scotland (Staff Version) Appendix Y Factor Analysis and Factor Ranking for Survey Items Related to Caring for Self and Caring of the Senior Charge Nurse Appendix Z Demographics, Particularly Ward, as Predictors of Job Satisfaction Appendix AA Demographic as Predictors of clarity Appendix BB Correlates of Operations of CBAS with Items from the Healing Compassion Survey—7 Cs NHS Scotland (Staff Version) References Index EULA