Using Predictive Analytics to Improve Healthcare Outcomes E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Table of Contents
Cover
Title Page
Copyright Page
Dedication Page
Contributors
Foreword
Preface: Bringing the Science of Winning to Healthcare
List of Acronyms
Acknowledgments
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
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
Next Steps
10 Measuring What Matters in a Multi‐Institutional Healthcare System
Testing a Model of Caring
Methods
Results from the Study of Model 1
Respecifying the Model
Testing Model 2
Results from the Study of Model 2
The Self‐Care Factor
Further Discussion
Summary
11 Pause and Flow:
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
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
13 Theory and Model Development to Address Pain Relief by Improving Comfort
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
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
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
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
17 Developing a Customized Instrument to Measure Caring and Quality in Western Scotland
Developing an Instrument to Measure Caring as Perceived by the Patient
Developing an Instrument to Measure Caring as Perceived by the Nursing Staff
Building a Structural Model for Assessing How Caring, Clarity, and Job Satisfaction Relate to One Another in Western Scotland
Results
Testing the Final Model to Measure the Experience of Nurses
Discussion
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 “I
2
E
2
”
Appendix D: 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
Appendix G: Crosswalk 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
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
Appendix S: Qualities of Systems and Organizations Suited to Serve People Recovering from OUD
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
End User License Agreement
List of Tables
Chapter 7
Table 7.1 Factor loading of HCAHPS and CFS.
Chapter 9
Table 9.1 Number of readmissions and number of days from discharge to readmis...
Table 9.2 Frequency of readmission in fewer than 30 days.
Table 9.3 Disposition of patient upon discharge.
Table 9.4 Readmission by cardiologist from 12‐month readmission data set.
Table 9.5 Frequency of specific diagnoses concurrent with heart failure.
Table 9.6 Frequency of concurrent diagnosis.
Chapter 11
Table 11.1 Number of staff members by unit and campus.
Chapter 12
Table 12.1 Variable descriptions from specified model of measurement for prev...
Table 12.2 Frequency of audits and numbers of patients and line assessments.
Chapter 13
Table 13.1 Biomedical theories of pain.
Chapter 15
Table 15.1 Job satisfaction factors for nurses in Jamaica, from most to least...
Chapter 16
Table 16.1 Indicators of how well the model fit what we intended to measure.
Table 16.2 Indicators of how well the respecified model fit what we intended ...
Chapter 17
Table 17.1 Factor loadings for the healing compassion survey—7 Cs NHS Scotlan...
Table 17.2 Patients' report of caring, April 2, 2014 to March 30, 2020.
Table 17.3 Descriptive statistics for all constructs and facets of constructs...
Chapter 18
Table 18.1 Four items from Section B (Collaboration) of the PCQI.
Table 18.2 Nurse attendance of CBAS training and time since training.
Table 18.3 Correlations of every item of the Operations of CBAS Assessment in...
Appendix G
Table G.1 Crosswalk of hospital tools and guidelines.
Appendix I
Table I.1 Primary payment type frequency.
Table I.2 QRS morphology groupings.
Table I.3 Did the patient receive pneumococcal vaccination?
Table I.4 Did the patient receive influenza vaccine?
Appendix J
Table J.1 Frequency of race.
Appendix K
Table K.1 CPS items and subscales from factor analysis.
Table K.2 CPS‐CPV items and subscales from factor analysis.
Appendix N
Table N.1
Comparison of pain and comfort.
Appendix Q
Table Q.1 Risk factors for OUD.
Appendix V
Table V.1 Factor loading for items in the Healing Compassions Assessment (HCA...
Appendix W
Table W.1 Factor loadings of the CPS by rank.
Appendix X
Table X.1 Factor loadings for the Healing Compassion Survey—7Cs NHS Scotland ...
Appendix Y
Table Y.1 Factor loading caring for self.
Table Y.2 Factor loadings for caring of senior charge nurse.
Appendix Z
Table Z.1 Ward demographics as predictors of job satisfaction.
Appendix AA
Table AA.1
Demographics as predictors of clarity.
Appendix BB
Table BB.1 Relationship between operations of CBAS items and aspects of carin...
Table BB.2 Relationship between operations of CBAS items and aspects of carin...
List of Illustrations
Chapter 1
Figure 1.1 Variable of interest surrounded by constructs of predictor variab...
Figure 1.2 Explained variance of CLABSI, traditional graphic.
Figure 1.3 Explained variance of CLABSI, bubble graphic.
Figure 1.4 Model 1 to measure new charge nurse performance.
Figure 1.5 Model 2, respecified with new predictor variables to measure new ...
Chapter 5
Figure 5.1 Model 1 predictors of patient falls.
Figure 5.2 Model 1, examining what variables predict falls.
Figure 5.3 Model 2, examining what variables predict falls.
Chapter 6
Figure 6.1 A structural model to study falls.
Figure 6.2 A structural model to study falls, respecified for implementing a...
Chapter 7
Figure 7.1 Mean score by unit for CFS‐HCAHPS.
Chapter 8
Figure 8.1 Model 1, to reduce length of stay in palliative care.
Figure 8.2 Length of stay throughout span of study for Model 1.
Figure 8.3 Results from Model 1: predictors of length of stay in palliative ...
Figure 8.4 Model 2: predictors of length of stay in palliative care.
Figure 8.5 Results from Model 2: predictors of length of stay in palliative ...
Chapter 9
Figure 9.1 Model 1: organizational tool to examine heart failure readmission...
Figure 9.2 Model 2: detailed model for predictors of heart failure.
Figure 9.3 Comparing readmissions in fewer than 30 days with readmissions in...
Chapter 10
Figure 10.1 Model 1, to test relationships of self‐care, jobs satisfaction, ...
Figure 10.2 Results from testing Model 1.
Figure 10.3 Model 2, to test relationship of clarity to job satisfaction and...
Figure 10.4 Results of Model 2 to test relationship of clarity to job satisf...
Chapter 11
Figure 11.1 Most considerable pauses, by percentage of pause themed by unit ...
Figure 11.2 Second most considerable pauses, by percentage of pause themed b...
Figure 11.3 Third most considerable pauses, by percentage of pause theme by ...
Figure 11.4 Most considerable flow, by percentage of flow themed by unit (ea...
Figure 11.5 Second most considerable flow, percentage of flow themed by unit...
Figure 11.6 Third most considerable flow, by percentage of flow theme by uni...
Chapter 12
Figure 12.1 Specified model to study central‐line associated blood stream in...
Chapter 13
Figure 13.1 Nichols–Nelson Model of Comfort (NNMC).
Chapter 14
Figure 14.1 A proposed model of OUD treatment, which includes the variable o...
Figure 14.2 A model of OUD treatment integrating ASAM criteria, treatment mo...
Chapter 15
Figure 15.1 Proposed model to study whether clarity predicts nurse job satis...
Figure 15.2 Confirmatory factor analysis for 9 of the 11 items from the Heal...
Figure 15.3 Two‐dimensional model of nurse job satisfaction with 11 factors ...
Chapter 16
Figure 16.1 Model for the Quadruple Aim.
Figure 16.2 Model 1: a model to research the effects of nurse job satisfacti...
Figure 16.3 Model 1 for job satisfaction and clarity internationally.
Figure 16.4 Model 2 to measure the relationship of job satisfaction, clarity...
Figure 16.5 Model for the Quadruple Aim.
Chapter 17
Figure 17.1 Structural model to study the experience of work of nurses in We...
Figure 17.2 Caring as reported by patients over four consecutive quarters.
Figure 17.3 Caring as reported by patients over 73 months (2014–2020).
Figure 17.4 Interaction of time and ward for mean scores of job satisfaction...
Figure 17.5 Model 3: testing job satisfaction, caring, and clarity.
Chapter 18
Figure 18.1 Caring Behavior Assurance System (CBAS) model—Scotland.
Figure 18.2 Flow chart for implementation of CBAS.
Figure 18.3 Six CBAS dimensions and average number of items used per ward, o...
Figure 18.4 Types of evidence used for Section A, care and compassion, 2012–...
Figure 18.5 Types of evidence used for Section B, communication, 2012–2015....
Figure 18.6 Types of evidence used for Section C, collaboration, 2012–2015....
Figure 18.7 Types of evidence used for Section D, environment, 2012–2015.
Figure 18.8 Types of evidence used for Section E, continuity of care, 2012–2...
Figure 18.9 Types of evidence used for Section F, clinical excellence, 2012–...
Figure 18.10 Operations of CBAS mean scores for nurses trained in CBAS.
Appendix A
Figure A.1 Full list of predictor variables related to workload, the structu...
Figure A.2 Color‐coded worksheet showing predictor variables grouped into co...
Figure A.3 Example of a specified measurement model for a variable of intere...
Appendix B
Figure B.1 The I
2
E
2
formula for leading lasting change.
Appendix H
Figure H.1 Heart failure model.
Appendix O
Figure O.1 Part 1 Comfort/pain perception (CPPS) survey—for patient.
Appendix P
Figure P.1 Part 1 Comfort/pain perception (CPPS) survey—for staff.
Appendix T
Figure T.1 Factor loadings for Satisfaction with Staffing/Scheduling and Sat...
Guide
Cover Page
Title Page
Copyright Page
Dedication Page
Contributors
Foreword
Preface: Bringing the Science of Winning to Healthcare
List of Acronyms
Acknowledgments
Table of Contents
Begin Reading
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 “I
2
E
2
”
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
Appendix G: Crosswalk 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
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
Appendix S: Qualities of Systems and Organizations Suited to Serve People Recovering from OUD
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
Wiley End User License Agreement
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Using Predictive Analytics to Improve Healthcare Outcomes
Edited by
John W. Nelson
Healthcare Environment
Jayne Felgen
Creative Health Care Management
Mary Ann Hozak
St. Joseph’s Health
This edition first published 2021© 2021 John Wiley & Sons, Inc.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of John W. Nelson, Jayne Felgen, and Mary Ann Hozak to be identified as the authors of the editorial material in this work has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication data applied for
ISBN 978‐1‐119‐74775‐8 (hardback)
Cover Image: © Kaikoro/Adobe Stock (adapted by Healthcare Environment)Cover Design by Wiley
This book is dedicated to the memory of John Lancaster, MBE, who died on June 20, 2020. A dedicated nurse, John felt it vital to capture the importance of caring in a scientific way. He was delighted to have been involved in the research described in this book. John will be fondly remembered by family, friends, colleagues, and patients for his humor, kindness, and compassion, as well as the admirable way he lived with cancer, facing death with a dignity sustained by the Catholic faith that was so important to him.
Contributors
Kate AbergerMedical Director Division of Palliative Care and Geriatric MedicineSt. Joseph’s HealthPaterson, NJ, US
Pauline Anderson‐JohnsonLecturerUniversity West Indies School of NursingMona, Jamaica
Alba BarrosProfessorFederal University São Paulo – EscolaPaulista de EnfermagemSão Paulo, Brazil
Jacqueline BrownClinical Educator Golden Jubilee National HospitalClydebank, Scotland
Dawna CatoChief Executive Officer Arizona Nurses AssociationMesa, AZ, US
Sally DampierProfessor Confederation CollegeThunder Bay, Ontario, Canada
Inge DiPasqualeManager Division of Palliative Care and Geriatric MedicineSt. Joseph’s HealthPaterson, NJ, US
Melissa D’MelloCongestive Heart Failure CoordinatorSt. Joseph’s HealthPaterson, NJ, US
Ana EstebanAssociate Director Quality Regulatory ComplianceColumbia Doctors – The FacultyPractice Organization of ColumbiaUniversity Irving Medical CenterNew York, NY, US
Jayne FelgenPresident Emeritus and ConsultantCreative Health Care ManagementMinneapolis, MN, US
Irit GantzCoordinator Woman‐Health Division School of NursingMeir HospitalKfar‐Saba, Israel
Kary GillenwatersChief Executive Officer Solidago Ventures and ConsultingElk River, MN, US
Sebahat GözümDean, School of Nursing Professor, Department of Public Health NursingAkdeniz UniversityAntalya, Turkey
Lidia GuandaliniCardiology NurseFederal University São Paulo – EscolaPaulista de EnfermagemSão Paulo, Brazil
Alicia HouseExecutive Director Steve Rummler Hope NetworkMinneapolis, MN, US
Mary Ann HozakAdministrative Director Department of CardiologySt. Joseph’s HealthPaterson, NJ, US
Michal ItzhakiSenior Lecturer Department of NursingTel Aviv UniversityTel Aviv, Israel
Benson KahiuNurse Manager Mount Sinai HealthEast Orange, NJ, US
Ayla KayaResearch Assistant Director Pediatric NursingAkdeniz UniversityAntalya, Turkey
Gay L. LandstromSenior Vice President and Chief Nursing OfficerTrinity HealthLivonia, MI, US
Marissa ManhartPerformance, Safety, and Improvement CoordinatorSt. Joseph’s HealthPaterson, NJ, US
John W. NelsonChief Executive Officer Healthcare EnvironmentSt. Paul, MN, US
Tara NicholsChief Executive Officer and Clinician Maters of ComfortMason City, IA, US
Kenneth OjaResearch ScientistDenver Health
Assistant ProfessorUniversity of ColoradoDenver, Colorado, US
Dawna Maria PerryChief Nursing OfficerThunder Bay Regional Health Science CenterThunder Bay, Ontario, Canada
Lance PodsiadManagerHelios Epic
Nurse ManagerHenry Ford Health SystemDetroit, Michigan, US
Karen PooleAssociate ProfessorLakehead University School of NursingThunder Bay, Ontario, Canada
Rebecca SmithWriter/EditorMinneapolis, MN, US
Susan SmithChief Executive OfficerChoice Dynamic InternationalLeeds, England
Kay TakesPresidentEastern Iowa Region of MercyOneDubuque, IA, US
Patricia ThomasManager – Associate Dean Nursing Faculty AffairsWayne State UniversityCollege of NursingDetroit, MI, US
Anna TrtchounianEmergency Medicine ResidentGood Samaritan HospitalMedical CenterWest Islip, Long Island, NY, US
Sebin VadasserrilManagerInnovative Nursing Practice and QualitySt. Joseph’s HealthPaterson, NJ, US
Linda ValentinoVice PresidentNursing OperationsMount Sinai HospitalNew York, NY, US
Dominika VrbnjakAssistant ProfessorUniversity of Maribor Faculty of Health SciencesMaribor, Slovenia
Josephine (Jo) Sclafani WahlAssociate Director BRG/PrismMI, US
Jacklyn WhitakerNurse ManagerSt. Joseph’s HealthPaterson, NJ, US
Theresa WilliamsonAssociate Nurse DirectorGolden Jubilee National HospitalClydebank, Scotland
Foreword
John W. Nelson and his colleagues are to be congratulated for creating this distinctive book. A very special feature of the book is the use of predictive analytics to explain, amplify, and validate caring theory. All too often, publications focusing on methods such as predictive analytics ignore the theoretical frameworks that guide the collection of data to which analytics are applied. The reader is then left with the thought, “Perhaps interesting results, but so what?” This book provides the answer to “so what?” by presenting the very interesting results, within the contexts of caring theory, specifically Relation‐Based Care®, the Caring Behaviors Assurance System©, and Watson’s Theory of Transpersonal Caring.
The book’s content emphasizes quality improvement, which might be considered the most appropriate application of predictive analytics in healthcare. Determining how, when, and why to improve the quality of healthcare, as a way to improve individual‐level and organization‐level outcomes, is a major challenge for all healthcare team members and researchers. Theory‐based predictive analytics is an innovative approach to meeting this challenge.
A challenge for the authors of the chapters of this book, and for its readers, is to determine the most appropriate place for theory in the triad of data, theory, and operations. Given my passion for the primacy of theory, I recommend that the starting point be theory, which determines what data is to be collected and how the data can be applied to operations.
The case studies that make up the several chapters of Sections Two and Four of this book, the contents of which are as interesting as they are informative, help readers to appreciate the value of theory‐based predictive analytics. The case studies, which range from individual‐level problems to department‐level problems to health system‐level problems, underscore the wide reach of theory‐based predictive analytics.
I contend that the ultimate challenge of predictive analytics will be to carry out the theoretical and empirical work needed to test the book editors’ claim, in the Preface of this book, that the same formulas helping people in the trucking and mining industries to create profiles of risk that enable them to prevent unwanted outcomes before they happen, can be applied successfully to improve healthcare outcomes. Meeting this challenge will undoubtedly extend the knowledge of our discipline, which many of us now refer to as nursology (see https://nursology.net).
Jacqueline Fawcett, RN, PhD, ScD (hon), FAAN, ANEF
Professor, Department of Nursing, University of Massachusetts Boston
Management Team Facilitator, https://nursology.net
Preface: Bringing the Science of Winning to Healthcare
A few years before the publication of this book, I attended an international mathematics conference for research in simulation studies and predictive analytics. Out of more than 300 attendees, there was only one other attendee from healthcare. For three days there were presentations by researchers from the fields of logistics (trucking) and mining, reviewing how they used predictive analytics and simulation to proactively manage outcomes related to productivity and company output. Surely, I thought, the same kinds of mathematical formulas presented by the truckers and miners could be used in healthcare to move us from reactive use of data to a proactive approach.
Currently, hospitals evaluate outcomes related to falls and infections using hindsight‐based analytics such as case studies, root cause analyses, and regression analyses, using retrospective data to understand why these outcomes occurred. Once the underlying causes for the outcomes are identified, the organization creates action plans for improving the outcomes. The problem with this process is that retrospective data provides only hindsight, which does nothing to create a profile of current or future risk. Healthcare organizations typically stop short of supporting prospective management of the data, which would allow for the collection of meaningful data about real‐life trends and what is actually happening in practice right now. Conversely, the truckers and miners at the conference showed how predictive analytics can be used to study risk for the purpose of managing unwanted outcomes before they occur. Since I am both a data scientist and a nurse, I could see clearly that the formulas from the math conference could apply to healthcare; all you would have to do is specify the models.
This book is about how analytics—mostly predictive analytics—can be used to improve outcomes in healthcare. This book also reveals how good data, derived from good theory, good measurement instruments, and good data collection processes has provided actionable information about the patient, the caregiver, and the operations of care, which have in turn inspired structure and process changes that saved millions of dollars while improving the experience of both patients and providers.
Organizations that have embraced predictive analytics as a central part of operational refinement include Amazon, IBM (Bates, Suchi, Ohno‐Machado, Shah, & Escobar 2014), Harrah’s casino, Capital One, and the Boston Red Sox (Davenport 2006). In his 2004 book (and the 2011 film), Moneyball, Michael Lewis, documents an example of how in 2002 the Oakland A’s professional baseball team, which had the lowest payroll in baseball, somehow managed to win the most games. This paradox of winning the most games despite having the skimpiest budget in the league was due to an assistant general manager who used a baseball‐specific version of predictive analytics called sabermetrics to examine what combination of possible recruits would reach first base most reliably, and would therefore result in the team winning the most games. These recruits were not the most obvious players—in fact, they were not considered by almost anyone to be the best players. It was only predictive analytics that made them visible as the right players to comprise this winning team.
If predictive analytics can help a team win more games, why couldn’t they help patients heal faster? Why couldn’t they help clinicians take better care of themselves? Why couldn’t predictive analytics be used to improve every outcome in healthcare?
As a data scientist and operations analyst, it is my job to present data to healthcare leaders and staff members in a way that allows them to easily understand the data. Therefore, it is the job of this book to help people in healthcare understand how to use data in the most meaningful, relevant ways possible, in order to identify the smartest possible operational improvements.
For decades, the three editors of this book have been conducting research to measure some of the most elusive aspects of caring. This book provides instructions and examples of how to develop models that are specified to the outcomes that matter most to you, thereby setting you up to use predictive analytics to definitively identify the most promising operational changes your unit or department can make, before you set out to change practice.
List of Acronyms
A&O
Alert and oriented
ACCF
American College of Cardiology Foundation
ACE
Angiotensin‐converting enzyme
ACEI
Angiotensin‐converting enzyme inhibitor
AGFI
Adjusted goodness of fit index
AHA
American Heart Association
AMI
Acute myocardial infarction
ANEF
Academy of Nursing Education Fellow
ANOVA
Analysis of variance
APN
Advanced practice nurse
ARB
Angiotensin receptor blockers
ARNI
Angiotensin receptor‐neprilysin inhibitor
ASAM
American Society of Addiction Medicine
Auto‐Falls RAS
Automated Falls Risk Assessment System
BNP
Brain natriuretic peptide
BSN
Bachelor of science in nursing (degree)
BUN
Blood urea nitrogen
CAC
Coronary artery calcium
CAD
Coronary artery disease
CARICOM
Caribbean Community (a policy‐making body)
CAT
Caring Assessment Tool
CBAS
Caring Behaviors Assurance System
©
CDI
Choice Dynamic International
CFI
Comparative fit index
CCU
Coronary care unit
CDC
Centers for Disease Control
CEO
Chief executive officer
CES
Caring Efficacy Scale
CFS
Caring Factor Survey
©
CFS‐CM
Caring Factor Survey – Caring of Manager
CFS‐CS
Caring Factor Survey – Caring for Self
CFS‐CPV
Caring Factor Survey – Care Provider Version
CFS‐HCAHPS
Caring Factor Survey – hospital consumer assessment of healthcare providers and systems (a 15‐item patient/provider survey)
CKD
Chronic kidney disease
CL
Central line
CLABSI
Central line‐associated bloodstream infection
CMS
Centers for Medicare and Medicaid Services
CNA
Certified nursing assistant
CNO
Chief nursing officer
CNS
Clinical nurse specialist
COPD
Chronic obstructive pulmonary disease
CPM
Clinical Practice Model
CPR
Cardiopulmonary resuscitation
CPS
Caring Professional Scale
CRT
Cardiac resynchronization therapy
CRT‐D
Cardiac resynchronization therapy defibrillator
CRT‐P
Cardiac resynchronization therapy pacemaker
CVA
Cerebrovascular accident
CQI
Continuous quality improvement
CVC
Central venous catheter
CHCM
Creative Health Care Management®
DNP
Doctor of nursing practice
DNR
Do not resuscitate
DNR‐B
Allows aggressive care, but not to the point of cardiopulmonary resuscitation
DVT
Deep vein thrombosis
ED
Emergency department
EF
Ejection fraction
EFA
Exploratory factor analysis
EKG
Electrocardiogram
EKG QRS
A segment of the EKG tracing
ELNEC
End‐of‐Life Nursing Education Consortium
EMR
Electronic medical record
ESC
European Society of Cardiology
FAAN
Fellow American Academy of Nursing
FTE
Full‐time employee
GFR
Glomerular filtration rate
GLM
General linear model
GPU
General patient‐care unit
GWTG
Get With The Guidelines (measurement tool)
HAI
Hospital‐acquired infection
HCA
Healing Compassion Assessment
HCAHPS
Hospital Consumer Assessment of Healthcare Providers and Systems
HEE
Health Education of England
HES
Healthcare Environment Survey (measurement instrument)
HF
Heart failure
HMO
Health maintenance organization
ICD
Implantable cardioverter defibrillator
ICU
Intensive care unit
IRB
Institutional review board
IV
Intravenous or information value
I
2
E
2
Inspiration, infrastructure, education and evidence
IOM
Institute of Medicine
KMO
Kaiser–Myer–Olkin (mathematical tool)
LOS
Length of stay
LCSW
Licensed clinical social worker
LVN
Licensed vocational nurse
LVSD
Left ventricle systolic dysfunction
MAT
Medication‐assisted treatment
MBE
Member of the British Empire
MICU
Medical intensive care unit
MFS
Morse Falls Scale
ML
Machine learning
MSN
Master of science in nursing (degree)
MRN
Medical record number
NA
Nursing assistant
NHS
National Health Service
NHSN
National Healthcare Safety Network
NICE
National Institute of Health and Care Excellence
NNMC
Nichols–Nelson Model of Comfort
NT‐proBNP
N‐terminal pro‐brain natriuretic peptide
O
2
Oxygen
OT
Occupational therapist/occupational therapy
OUD
Opioid use disorder
PC
Palliative care
PCA
Patient care attendant
PCI
Percutaneous coronary intervention
PICC
Peripherally inserted central catheter
PMT
Pacemaker mediated tachycardia
PN
Pneumonia
PCLOSE
An indicator of model fit to show the model is close‐fitting and has some specification error, but not very much.
POLST
Physician orders for life sustaining treatments
PPCI
Professional Patient Care Index
PR
Pregnancy related
PSI
Performance and safety improvement
PSI RN
Performance and safety improvement registered nurse
QI
Quality improvement
QRS
(See EKG QRS)
R
A programming language for statistical computing supported by the R Foundation for Statistical Computing.
R4N
Name of medical unit
R6S
Name of medical unit
RAA or R+A+A
Responsibility, authority, and accountability
RBBB
Right bundle branch block
RBC
Relationship‐Based Care
®
RMC
Recovery management checkups
RMSEA
Root mean square error of approximation
RN
Registered nurse
SAMSA
Substance Abuse and Mental Health Services Administration
SAS
Statistical Analysis System is a software system for data analysis
SBP
Systolic blood pressure
ScD
Doctor of science
SCIP
Surgical care improvement project
SCN
Senior charge nurse
SCU
Step‐down unit
SEM
Structural equation model
SPSS
Statistical Package for the Social Sciences is a software system owned by IBM (International Business Machines)
SRMR
Standardized root mean square residual
STS
Sociotechnical systems (theory)
ST‐T
Segment of the heart tracing in an electrocardiograph
SUD
Substance use disorder
TIA
Transient ischemic attack
TIP
Treatment improvement protocols
TLC
Triple lumen catheter
TTE
Transthoracic echocrdiogram
UTD
Unable to determine
VS
Vital sign
VS: SBP
Vital sign: systolic blood pressure
VS: DBP
Vital sign: diastolic blood pressure
Acknowledgments
First, the three editors of this book would like to acknowledge our developmental editor, Rebecca Smith, who has made the inaccessible, complex concepts of data analytics simple to understand and exciting to contemplate.
Secondly, we would like to acknowledge all the analysts and mathematicians from other disciplines who have enthusiastically and humbly shared their knowledge of mathematics and how it is applied in science. We have been inspired by the depth and breadth of what you know and by your eagerness to learn from others. The lead editor would also like to ask the indulgence of all of the mathematicians, analysts, and scientists who will read this book, as you encounter moments in this book where brevity and simplicity have taken precedence over thorough scientific explanations. In an effort to make this book accessible to a lay audience, much of the technical talk has been truncated or eliminated.
Thirdly, we acknowledge the visionary leaders who had the courage to step out and measure what matters—behavior and context. Without your understanding that data beyond frequencies was needed, the ability to use predictive analytics to improve healthcare outcomes would still be an elusive dream.
Finally, the editors of this book acknowledge all the staff members who took part in these studies. Every one of you made each model of measurement better, and you played a vital part in producing the groundbreaking findings in this book. Without you, this book would not exist.
Section OneData, Theory, Operations, and Leadership
1Using Predictive Analytics to Move from Reactive to Proactive Management of Outcomes
John W. Nelson
For predictive analytics to be useful in your quest to improve healthcare outcomes, models for measurement must reflect the exact context in which you seek to make improvements. Data must resonate with the staff members closest to the work, so that action plans premised on the data are specific, engaging, and instantly seen as relevant. This chapter provides 16 steps the author has used in healthcare settings to engage staff members in outcomes improvement. Models created using these steps have proven effective in improving outcomes and saving millions of dollars because the process engages the entire healthcare team to provide input into (a) the design of measurement instruments, (b) interpretation of results, and (c) application of interventions, based on the data, to improve outcomes. Analysts and staff members build models of measurement that tell the story of the organization empirically, which makes the data not only actionable but relatable.
The Art and Science of Making Data Accessible
Data can and should read like a story. The presentation of data in healthcare should be interesting and engaging because it reflects empirically what people are experiencing operationally. It is the experience of this author that when data is presented as part of the employees' story, they love it. Everybody likes to talk about their experience, and when a data analyst is able to tell them what they themselves are experiencing with the numbers to back it up, it places staff members at the edges of their seats.
The presentation of data in healthcare should be interesting and engaging because it reflects empirically what people are experiencing operationally.
With the advent of big data, machine learning, and artificial intelligence, we now too often turn one of our oldest, most cherished human traditions—storytelling—over to machines. The stories machines tell reveal patterns and relationships that staff members are familiar with, but they leave out the context, rendering their stories unrelatable. If your goal is to provide people with information they instantly recognize as accurate and relevant, your models must be specified to the people and contexts they presume to report on, and only then should they be examined empirically.
The stories machines tell leave out the context, rendering their stories unrelatable.
You are about to meet a 16‐step process for how to tell a story, using data, that is not only interesting; it is actionable operationally. No two organizations are the same, and no organization stays the same over time. Thus, it is critical to evaluate whether data presented within an organization accurately captures the context and nuance of the organization at a point in time.
Admittedly, the idea of 16 steps may initially feel prohibitively complex. As you spend time looking at the process in terms of some practical examples, however, you will find that what I have provided is simply a template for examining and sorting data which you will find not only simple to use, but ultimately quite liberating.
As you read through the steps, you are likely to intuit what role you would play and what roles you would not play, in this process. Some of the work described in the steps will be done by staff members closest to the work being analyzed, and some will be done by mathematicians, statisticians, programmers, and/or data analysts. If some of the content is unfamiliar to you or seems beyond your reach, rest assured that someone on the team will know just what to do.
If some of the content is foreign to you or seems beyond your reach, rest assured that someone on the team will know just what to do.
Step 1: Identify the Variable of Interest
What problem or improvement opportunity is of concern and/or interest to your team? This could be an issue such as patient falls or the civility of employees, managers, or patients. This problem or improvement opportunity is referred to as the variable of interest; it will also serve as the focal point of the story your team will eventually be telling with data. In some settings, the variable of interest may be referred to as the outcome of interest.
Step 2: Identify the Things That Relate to the Variable of Interest: AKA, Predictor Variables
If your team was looking to improve an outcome related to falls, for example, you would want to examine anything that could predict, precede, or contribute to a fall. Assemble members of the care team and think together about what might lead to a fall, such as (a) a wet floor, (b) staff members with stature too small to be assisting patients with walking, (c) the patient taking a heart medication a little before the fall, and so on. As the discussion of everything that relates to your variable of interest continues, designate one person to write down all the things being mentioned, so the people brainstorming what relates to falls can focus solely on describing the experience and are not distracted by writing things down (Kahneman, 2011). Do not search far and wide for possible predictor variables or even think about the evidence from the literature at this point; just brainstorm and share. Variables from the literature can and should eventually supplement this list, but the focus in Step 2 is on the team's personal experience and subsequent hunches about variables that could affect the variable of interest.
Step 3: Organize the Predictor Variables by Similarity to Form a Structural Model
Have the team organize into groups all predictor variables that seem to be similar to one another. For now, you will simply separate them into columns or write them on separate sheets of paper. For example, one group of variables that may be found to predict falls may be “patient‐related,” such as the patient's age, level of mobility, the different diagnoses the patient is dealing with, and so on. These could all be listed in a construct under the heading “patient‐related variables.” You might also create a construct for “staff‐related variables,” such as “was walking with a staff member,” “staff member’s level of training in ambulating patients,” “the staff member was new to this type of unit,” and so on.
Step 4: Rank Predictor Variables Based on How Directly They Appear to Relate to the Variable of Interest
First, rank the individual variables within each construct, determining which of the variables within each construct appear to relate most directly to the variable of interest. These will be thought of as your most influential variables. Knowledge from the literature of what relates to the variable of interest is welcome at this point, but you should continue to give extra credence to the clinical experience of the team and what their personal hunches are regarding the relevance of each predictor variable. Once the predictor variables within each construct are ranked, then rank each overall construct based on how directly the entire grouping of predictor variables appears to relate to the variable of interest.
Step 5: Structure the Predictor Variables into a Model in Order to Visually Communicate Their Relationship to the Variable of Interest
It will eventually be important to get others on board who are interested in studying and improving the variable of interest. This can be done by noting the variable of interest in a circle in the middle of a blank page and then grouping all the predictor variables around the outcome variable so the visual looks a bit like the hub of a bike wheel with each predictor variable connected to the outcome variable by a spoke. Figure 1.1 is an example of a structural model that is ready to be converted to a measurement model.
For ease of understanding, items in the same construct would be the same color, and items in each color would then be arranged with those considered most influential positioned closest to the variable of interest. Selecting the most influential variables is important because you may decide you have only enough time or resources to address some of the variables. If this is the case, select those variables that are perceived to be the most influential.
A collection of three worksheets used by a neurosurgical nursing care unit for a study on workload, showing the progression from a full list of predictor variables to a workable model (Steps 2–5), can be found in Appendix A. These worksheets visually represent the conversion of a structural model to a measurement model. While Figure 1.1 offers a visual representation of a model with multiple constructs, showing the variables arranged by rank, Appendix A shows a slightly different visual representation, which is the representation typically used by the author of this chapter. Readers are encouraged to try both methods of representing the constructs to see which is more useful in understanding and visually communicating the information.
Figure 1.1 Variable of interest surrounded by constructs of predictor variables, arranged by rank.
Step 6: Evaluate if and/or Where Data on the Predictor Variables Is Already Being Collected (AKA, Data Discovery)
Investigate whether data on any of the predictor variables in your model is already being collected in current databases within your organization. Where you find that data is already being collected, you will use the existing data. You may find that data related to the variables of interest is being collected in more than one place, which will provide an opportunity for consolidation, making your data management process more efficient and standardized.
Step 7: Find Ways to Measure Predictor Variables Not Currently Being Measured
If there are important variables not being measured, it will be necessary to develop ways to measure them. If influential variables are left out of the study, the model will remain mis‐specified (wrong).
If influential variables are left out of the study, the model will remain mis‐specified (wrong).
Step 8: Select an Analytic Method
This work will likely be overseen by an in‐house or consultant mathematician, statistician, or data analyst. Consider types of analytics beyond linear methods or qualitative descriptive methods, which are the most common methods currently used in healthcare. For example, if the dataset is very large and complex and it is not clear how to sort the predictor variables in a linear method such as regression analysis, try Pareto mathematics where outliers are examined. Pareto mathematics looks at the highs and lows in the dataset to create a profile of success factors. In his book Where Medicine Went Wrong: Rediscovering the Path to Complexity, Dr. Bruce West asserts that Gaussian mathematics throws out the extreme values despite the fact that these extreme values often provide the most valuable information because they provide a profile of the biggest successes and the biggest failures (2007).
Another type of analysis to consider is constructal theory, a method derived from physics, which is the study of “flow” (Bejan & Zane, 2012). If employees are able to talk about what makes their work flow or what makes their work pause, constructal theory will allow for their comments to be themed and addressed operationally. It is the experience of this author that if employees can talk about their workflow, and data can then be arranged for them in themes, the employees get excited about working on productivity because it is readily apparent to them that the overall aim is figuring out how to do more of what works well and less of what does not. Constructal theory makes productivity, or the lack of it, visible. Gaussian mathematics provides insight into linear processes, while analyses like Parato mathematics and constructal theory provide insight into more dynamic/complex and unknown processes, respectively. Pairing the analytic method with the variable of interest is important to achieving insight into the operations of work and associated outcomes.
The overall aim is figuring out how to do more of what works well and less of what does not.
For your most complex models, you may want to consider using a machine learning problem designed to let the computer tell you the rank order of predictor variables as they relate to your variable of interest. This is suggested on the condition that you never allow the machine to have the “final say.” Machines function without regard for theory and context, so they are not able to tell a story capable of deeply resonating with the people whose work is being measured. It is tempting to be lazy and not do the work of carefully building models based on theory and rich in context that will result in data that makes people excited to take action. More on machine learning can be found in Chapter 6.
Step 9: Collect Retrospective Data Right Away and Take Action
If available, use retrospective data (data used for reporting what has already happened) to determine which predictor variables, of those already being measured, most closely relate to your variable of interest. Have a mathematician, statistician, or data analyst run a correlation table of all the predictor variables. Most organizations employ or contract with mathematicians, statisticians, and/or others who know how to run and read a correlation table using statistics software such as SPSS, SAS, or R. The mathematician, statistician, or analyst will be able to identify all the predictor variables found to have a relationship with the variable of interest and rank them in order of the strength of the relationship. (Many examples of these ranked predictor variables will appear in this book.) This will help start a meaningful conversation about what is being discovered while data on the remaining variables from the measurement model is still being collected manually. If actionable information is discovered during this step, operational changes can be implemented immediately. Further examination of the data will continue, but if what you have discovered by this point makes some opportunities for improvements apparent, do not wait to improve operations!
Step 10: Examine Data Before Analysis
Once the data is collected, it is tempting to proceed directly to the fun part and view the results. How do things relate to one another? What are the high and low scores? Viewing the results that answer questions like these is rewarding; however, prior to viewing any results (even in Step 9, where you may have gathered together some pretty compelling retrospective data), it is critical to ensure that all the data is correct. Have the statistician, analyst, or mathematician use statistics software, or even Excel, to examine the distribution of the scores and look for indications that there is missing data. If the distribution of the data for any predictor variable has a prominent leaning toward low or high scores, with a few outliers, the analyst will need to decide whether the outliers should be removed or whether the data should be weighted. If there are patterns of missing data (e.g. one item from a construct has many missing scores), then the group should discuss why the data is missing and what to do about it. Understanding the distribution and missing data will provide additional insight into the population being studied.
Reviewing the data for accuracy can also begin to give the team a feel for the “personality” of the data.
Reviewing the data for accuracy can also begin to give the team a feel for the “personality” of the data. This data all comes from people, but instead of a coherent conversation or observation of the people contained in the data, it is initially just data. As the data is examined, an understanding of the respondents will begin to emerge.
Step 11: Analyze the Data
For this step, it is important to engage a professional who is trained in analytics so the data can be interpreted accurately. There are several options in today's analytic software, such as SPSS, SAS or R, to aid in the examination of data flaws that are not obvious by merely looking at the dataset or its associated graphic representations. Your organization may have software, such as Tableau, which generates graphs automatically and is dependable for graphic visualization. In this step, it is important to engage an analyst or data scientist who can take advantage of the tools and tests contained in analytic software.
