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Health Care Data Guide: Learning from Data for Improvement 2nd edition [Mīkstie vāki]

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  • Formāts: Paperback / softback, 656 pages, height x width x depth: 274x216x36 mm, weight: 1247 g
  • Izdošanas datums: 08-Aug-2022
  • Izdevniecība: Jossey-Bass Inc.,U.S.
  • ISBN-10: 1119690137
  • ISBN-13: 9781119690139
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  • Cena: 106,73 €
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  • Formāts: Paperback / softback, 656 pages, height x width x depth: 274x216x36 mm, weight: 1247 g
  • Izdošanas datums: 08-Aug-2022
  • Izdevniecība: Jossey-Bass Inc.,U.S.
  • ISBN-10: 1119690137
  • ISBN-13: 9781119690139
Citas grāmatas par šo tēmu:
"This book is designed for those who want to use data to help improve health care. Specifically, this book focuses on deepening skills related to using data for improvement. Our goal is to help those working in health care to make improvements more readily and have greater confidence that their changes truly are improvements. Using data for improvement is a challenge and source of frustration to many. This book is designed to meet this challenge and alleviate frustration. This book is a good companion toThe Improvement Guide: a Practical Approach to Enhancing Organizational Performance, 2nd Edition, Langley and others, Jossey-Bass, 2009, which provides a complete guide to improvement. Our Chapter 1 summarizes the key content from The Improvement Guide and specific references to The Improvement Guide are made throughout this book"--

An Essential text on transforming raw data into concrete health care improvements 


Now in its second edition, The Health Care Data Guide: Learning from Data for Improvement delivers a practical blueprint for using available data to improve healthcare outcomes. In the book, a team of distinguished authors explores how health care practitioners, researchers, and other professionals can confidently plan and implement health care enhancements and changes, all while ensuring those changes actually constitute an improvement. 

This book is the perfect companion resource to The Improvement Guide: A Practical Approach to Enhancing Organizational Peformance, Second Edition, and offers fulsome discussions of how to use data to test, adapt, implement, and scale positive organizational change. 

The Health Care Data Guide: Learning from Data for Improvement, Second Edition provides: 

  • Easy to use strategies for learning more readily from existing health care data 
  • Clear guidance on the most useful graph for different types of data used in health care 
  • A step-by-step method for making use of highly aggregated data for improvement 
  • Examples of using patient-level data in care 
  • Multiple methods for making use of patient and other feedback data 
  • A vastly better way to view data for executive leadership 
  • Solutions for working with rare events data, seasonality and other pesky issues
  • Use of improvement methods with epidemic data 
  • Improvement case studies using data for learning 
A must read resource for those committed to improving health care including allied health professionals in all aspects of health care, physicians, managers, health care leaders, and researchers.
Figures, Tables, and Exhibits
xiii
Preface xxix
Acknowledgments xxxiii
The Authors xxxv
About the Companion Website xxxvii
Part I Using Data for Improvement
1(222)
Chapter 1 Improvement Methodology
3(24)
Fundamental Questions for Improvement
4(5)
What Are We Trying to Accomplish?
5(2)
How Will We Know that a Change is an Improvement?
7(1)
What Changes Can We Make That Will Result in Improvement?
8(1)
The PDSA Cycle for Improvement
9(17)
Tools and Methods to Support the Model for Improvement
13(2)
Designing PDSA Cycles for Testing Changes
15(4)
Analysis of Data from PDSA Cycles
19(7)
Summary
26(1)
Key Terms
26(1)
Chapter 2 Using Data for Improvement
27(50)
What Does the Concept of Data Mean?
27(16)
How are Data Used?
29(7)
Types of Data
36(7)
Using A Family of Measures
43(4)
The Importance of Operational Definitions
47(4)
Data for Different Types of Studies
51(2)
Sampling
53(8)
Sampling Strategies
55(3)
What About Sample Size?
58(3)
Stratification of Data
61(2)
What about Case-Mix Adjustment?
63(2)
Transforming Data
65(3)
Analysis and Presentation of Data
68(7)
Summary
75(1)
Key Terms
75(2)
Chapter 3 Understanding Variation Using Run Charts
77(46)
Introduction
77(1)
What Is a Run Chart?
77(3)
Use of a Run Chart
80(1)
Constructing a Run Chart
80(17)
Examples of Run Charts for Improvement Projects
84(5)
Rules to Aid in Interpreting Run Charts
89(8)
Special Issues in Using Run Charts
97(23)
Stratification with Run Charts
113(3)
Using the Cumulative Sum Statistic with Run Charts
116(4)
Summary
120(1)
Key Terms
121(2)
Chapter 4 Learning from Variation in Data
123(36)
The Concept of Variation
123(6)
Introduction to Shewhart Charts
129(6)
Depicting and Interpreting Variation Using Shewhart Charts
135(5)
The Role of Annotation with Shewhart Charts
140(1)
Establishing Limits for Shewhart Charts
141(4)
Revising Limits for Shewhart Charts
145(2)
Stratification with Shewhart Charts
147(5)
Shewhart Charts and Targets, Goals, or Other Specifications
152(3)
Special Cause: Is It Good or Bad?
155(2)
Summary
157(1)
Key Terms
158(1)
Chapter 5 Understanding Variation Using Shewhart Charts
159(64)
Selecting the Type of Shewhart Chart
160(3)
Shewhart Charts for Continuous Data
163(1)
I Charts
164(7)
Examples of Shewhart Charts for Individual Measurements
166(2)
Rational Ordering with an I Chart
168(2)
Example of I Chart for Deviations from a Target
170(1)
Xbar S Shewhart Charts
171(6)
Shewhart Charts for Attribute Data
177(3)
Subgroup Size for Attribute Charts
178(2)
The P Chart for Classification Data
180(8)
Examples of P Charts
182(4)
Creation of Funnel Limits for a P Chart
186(2)
Shewhart Charts for Counts of Nonconformities
188(2)
C Charts
190(2)
U Chart
192(5)
Creation of Funnel Limits for a U Chart
195(2)
Alternatives for Attribute Charts for Rare Events
197(9)
G Chart for Opportunities Between Rare Events
198(4)
T Chart for Time Between Rare Events
202(4)
Process Capability
206(5)
Process Capability from an I Chart
208(1)
Capability of a Process from Xbar and S Charts
208(2)
Capability of a Process from Attribute Control Charts
210(1)
Capability from a P Chart
210(1)
Capability from a C or U Chart
210(1)
Summary
211(1)
Key Terms
212(1)
5.1 Calculating Shewhart Limits
213(1)
I Chart (For Individual Values Of Continuous Data)
213(1)
Xbar S Chart (For Continuous Data In Subgroups)
214(3)
P Chart (For Classification Data)
217(1)
C Chart (Count Of Incidences)
218(1)
U Chart (Incidences Per Area Of Opportunity)
219(1)
G Chart (Cases Between Incidences)
220(1)
T Chart
221(2)
Additional Tools For Understanding Variation In Data
223(74)
Depicting Variation
223(2)
Additional Tools for Learning from Variation
225(1)
Frequency Plots
225(11)
Frequency Plot Construction
226(2)
Frequency Plots Used with Shewhart Charts
228(4)
Frequency Plots and Stratification
232(4)
Pareto Charts
236(14)
Pareto Chart Construction
238(1)
Pareto Charts Used with Shewhart Charts
239(5)
Pareto Chart and Stratification
244(6)
Scatterplots
250(10)
Scatterplot Construction
251(3)
Scatterplots Used with Shewhart Charts
254(4)
Scatterplots and Stratification
258(2)
Radar Charts
260(5)
Constructing a Radar Chart
261(1)
Radar Charts Used with Shewhart Charts
261(2)
Radar Charts and Stratification
263(2)
Summary
265(1)
Key Terms
265(2)
Chapter 7 Shewhart Chart Savvy: Dealing with Common Issues
267(30)
Creating Effective Shewhart Charts
267(12)
Tip 1 Type of Data and Subgroup Size
267(1)
Tip 2 Rounding Data
268(1)
Tip 3 Formatting Charts
268(6)
Tip 4 Decisions for Recalculating limits, or Rephasing, on a Shewhart Chart
274(3)
Extending Centerline and Limits Backward
277(2)
Typical Problems with Software for Calculating Shewhart Charts
279(6)
Characteristics to Consider When Purchasing SPC Software
282(3)
Another Caution with I Charts and Chart Selection
285(2)
Guidelines for Shewhart Charts in Research Studies and Publications
287(5)
Use of Shewhart Charts in Research Studies
288(2)
Shewhart Charts in Publications
290(2)
Shewhart's Theory versus Statistical Inference
292(4)
Summary
296(1)
Key Terms
296(1)
Part II Advanced Theory and Methods with Data For Improvement
297(106)
Chapter 8 More Shewhart-Type Charts
299(42)
Other Shewhart-Type Charts
301(15)
The NP Chart
301(1)
Xbar Range (Xbar R) Chart
302(2)
Median Chart
304(2)
Attribute Charts with Large Subgroup Sizes (P' and U')
306(1)
Prime Charts (P' and U')
307(6)
Negative Binomial Chart
313(3)
Some Adaptations to Shewhart Charts
316(22)
MA Chart
317(3)
CUSUM Chart
320(8)
Exponentially Weighted Moving Average (EWMA) Chart
328(3)
Standardized Shewhart Charts
331(3)
Multivariate Shewhart-Type Charts
334(4)
Summary
338(1)
Key Terms
339(2)
Chapter 9 Special Uses for Shewhart Charts
341(34)
Shewhart Charts with a Changing Centerline
341(20)
Shewhart Charts with a Sloping Centerline
342(2)
Shewhart Charts with Seasonal Effects
344(5)
Adjusting Shewhart Charts for Confounders
349(6)
Transformation of Data with Shewhart Charts
355(6)
Shewhart Charts for Autocorrelated Data
361(12)
Risk-Adjusted or Case-Mix Adjusted Shewhart Charts
366(2)
Comparison Charts
368(1)
Confidence Intervals and Confidence Limits
369(4)
Summary
373(1)
Key Terms
373(2)
Chapter 10 Drilling Down into Aggregate Data for Improvement II
375(28)
What are Aggregate Data?
375(1)
What is the Challenge Presented by Aggregate Data?
376(5)
Introduction to the Drill Down Pathway
381(3)
Stratification
381(1)
Sequencing
382(1)
Rational Subgrouping
383(1)
An Illustration of the Drill Down Pathway: Adverse Drug Events
384(19)
Drill Down Pathway Step One
385(1)
Drill Down Pathway Step Two
385(2)
Drill Down Pathway Step Three
387(2)
Drill Down Pathway Step Three, Continued
389(4)
Drill Down Pathway Step Four
393(4)
Drill Down Pathway Step Five
397(3)
Drill Down Pathway Step Six
400(1)
Summary
400(1)
Key Terms
401(2)
Part III Applications of Shewhart Charts in Health Care
403(192)
Chapter 11 Learning from Individual Patient Data
405(20)
Examples of Shewhart Charts for Individual Patients
407(18)
Example 1 Asthma Patient Use of Shewhart Charts
408(1)
Example 2 Prostate-Specific Antigen (PSA) Screening for Prostate Cancer
409(2)
Example 3 Monitoring Patient Measures in the Hospital
411(1)
Example 4 Bone Density for a Patient Diagnosed with Osteoporosis
412(3)
Example 5 Temperature Readings for a Hospitalized Patient
415(3)
Example 6 Shewhart Charts for Continuous Monitoring of Patients
418(2)
Example 7 Monitoring Weight
420(1)
Example 8 Monitoring Blood Sugar Control for Patients with Diabetes
421(1)
Example 9 Using Shewhart Charts in Pain Management
422(1)
Summary
423(2)
Chapter 12 Learning from Patient Feedback to Improve Care
425(26)
Summarizing Patient Feedback Data
429(8)
Presentation of Patient Satisfaction Data
437(1)
Using Patient Feedback for Improvement
438(7)
The PDSA Cycle for Testing and Implementing Changes
438(1)
Improvement Team Working on Clinic Satisfaction
438(4)
Improvement Team Working on Pain
442(2)
Feedback from Employees
444(1)
Using Patient Satisfaction Data in Planning for Improvement
445(2)
Special Issues with Patient Feedback Data
447(4)
Are There Challenges When Summarizing and Using Patient Satisfaction Survey Data?
447(2)
Does Survey Scale Matter?
449(1)
Summary
450(1)
Key Terms
450(1)
Chapter 13 Using Shewhart Charts in Health Care Leadership
451(24)
A Health Care Organization's Vector of Measures
452(1)
Developing a VOM
453(8)
So How do We Best Display a VOM?
461(3)
Administrative Issues with a VOM
464(3)
Some Examples of Measures for Other VOMs
467(8)
Emergency Department
468(1)
Primary Care Center
468(1)
System Flow Measures
469(1)
Health Authority
469(2)
Large Urban Hospital
471(1)
IHI Whole System Measures
471(2)
Summary
473(1)
Key Terms
474(1)
Chapter 14 Shewhart Charts for Epidemic Data
475(18)
Shewhart Charts in Epidemiology
476(3)
Development of Shewhart Charts for Epidemic Data
479(8)
C Charts (Epoch 1)
479(2)
Charts of Epoch 2
481(4)
Charts for Epoch 3
485(1)
Charts for Epoch 4
486(1)
Some Issues with the Hybrid Chart for COVID-19 Deaths
487(6)
Data Quality
487(1)
Day-of-the-Week Adjustment
487(2)
Application of the Hybrid Charts to Cases, Hospitalizations, and Intensive Care Unit Admissions
489(3)
Summary
492(1)
Key Term
492(1)
Chapter 15 Case Studies
493(102)
Case Study A Improving Access to a Specialty Care Clinic
495(9)
Case Study B Radiology Improvement Projects
504(10)
Case Study C Reducing Post-Cabg Infections
514(12)
Case Study D Drilling Down into Percentage of C-Sections
526(11)
Case Study E Reducing Length of Stay After Surgery
537(14)
Case Study F Reducing Hospital admissions
551(7)
Case Study G Accidental Puncture/Laceradon Rate
558(10)
Case Study H Improving Telemedicine Failed Calls and No Shows
568(15)
Case Study I Variation in Financial Data
583(12)
Index 595(14)
Shewhart Chart Selection Guide 609
LLOYD P. PROVOST is a cofounder of Associates in Process Improvement, the developers of the Model for Improvement roadmap and the Quality as a Business Strategy template for focusing organizations on improvement. Lloyd is a senior fellow at the Institute for Healthcare Improvement, where he supports the use of data for learning in programs.

SANDRA K. MURRAY is a principal in Corporate Transformation Concepts, an independent consulting firm. She is faculty for the Institute for Healthcare Improvements year-long Improvement Advisor Professional Development Program and their Breakthrough Series College. Sandra has taught numerous programs through the National Association for Healthcare Quality. Her cohort of client organizations encompasses the spectrum of health care delivery.