Welcome Back Quality Leadership Academy Session 3 “ How Do You Know it Works?” Anna Roth, RN, MS, MPH
Report on Projects to Date <ul><li>Refine and refresh aim statements  </li></ul><ul><li>Share results of our small tests o...
 
Theory
Today’s Objectives <ul><li>Review results of your project </li></ul><ul><ul><li>Review small tests of change </li></ul></u...
How will we know?
Why Else Should We Measure? <ul><li>You can’t manage what you don’t measure </li></ul><ul><li>How else would you know that...
Choosing appropriate statistics
Median v Mean <ul><li>10 people are on the bus </li></ul><ul><li>The mean income of the riders is $50,000/yr </li></ul><ul...
Median v Mean <ul><li>The median income of the riders remains $50,000/year </li></ul><ul><li>The mean income is now approx...
Mean <ul><li>Mean (average) </li></ul><ul><ul><li>Measures the center, or middle, of a numerical data set </li></ul></ul><...
Median <ul><li>Median </li></ul><ul><ul><li>Also measures the center of a numerical data set </li></ul></ul><ul><ul><li>Mu...
Percentage or Percentile? <ul><li>Suppose your score on the GRE was reported to be the 80 th  percentile </li></ul><ul><li...
Honest Errors <ul><li>Arithmetic errors or omissions </li></ul><ul><ul><li>Check to see if everything adds up </li></ul></...
Excercise
Report Out
Back in 15 minutes
Finding your way/Telling your story
Data Display and Analysis <ul><li>How do you want to tell your story?? </li></ul><ul><li>Who are you going to tell your st...
Common types of data display <ul><li>Pie charts </li></ul><ul><li>Bar graphs </li></ul><ul><li>Tables </li></ul><ul><li>Ti...
 
Charts and Graphs  and Spiders Oh My <ul><li>Watch for pitfalls </li></ul><ul><li>Size matters!  </li></ul><ul><li>Be awar...
Sizing up a pie chart <ul><li>Do the percentages add up to 100 </li></ul><ul><li>Beware of slices that are called ‘other’ ...
 
 
Elements of a Control Chart UCL LCL X Indicator Time An indication of a special cause
Non-Random Rules for Run Charts
Variation Common Cause vs. Special Cause <ul><li>Common cause </li></ul><ul><li>Always present </li></ul><ul><li>Inherent ...
 
 
<ul><li>Special cause </li></ul><ul><li>Identify and study special cause </li></ul><ul><li>If negative, minimize or preven...
First 24 Observations from Red Bead Data (without outlier employee)
12 Runs expect to find between 8 and 18 runs
 
 
On Death, Dying & Data DENIAL ANGER BARGAINING DEPRESSION ACCEPTANCE
On Death, Dying & Data DENIAL “ The data are wrong” ANGER “ The data are right, but it’s not a problem” BARGAINING “ The d...
Stages of Facing Reality:  “To live divided no more” <ul><li>“The data are wrong” </li></ul><ul><li>“The data are right, b...
Crimson Bead Company
 
 
“ Every system is perfectly designed to achieve the results that it achieves” Berwick: central law of improvement BMJ 1996...
Discussion
Oversight
Oversight <ul><li>Project-level e.g. </li></ul><ul><li>% AMI patients getting evidence-based care </li></ul><ul><li>% Pneu...
Projects Connected to Big Dots * Mortality Rate * Cost per Admission * Adverse Events * Functional Outcomes * Patient Sati...
A Senior Leader Perspective on Projects The Project:  e.g., Ventilator-Acquired Pneumonia Spreading and Sustaining This Im...
Issues at Each Tier  (Examples) Tier 1:  Big Dot Tier 2: Portfolio Tier 3: Projects <ul><li>Aims of strategic importance t...
 
Project  Level Measure (Tier 3) <ul><li>Family assistance </li></ul><ul><li>May 05 to Oct 06: 17 months of NO VAP’s </li><...
“ One Patient,  One List” Project  Level Measure (Tier 3)
Project Level Measure (Tier 3) <ul><li>% meds unreconciled:admission  25%   3% </li></ul><ul><li>% meds unreconciled:tran...
 
Driver Diagrams
What Changes Can We Make? Understanding the System for Weight Loss “ Every system is perfectly designed to achieve the res...
How Will We Know We Are Improving? Understanding the System for Weight Loss with Measures Measures let us • Monitor progre...
 
AIM Primary Driver Secondary Driver
<ul><li>At your tables write down 4-6 primary drivers for your project </li></ul><ul><li>For each primary driver, come up ...
Report Out
Tying it together
Transforming Care at the Bedside (TCAB) Med-Psych Workgroup Clinical Informatics ED Safety Central Line Infection Team Mul...
 
System Level Aims
System Level Aims Primary System Aims Additional System Level Aims Zero Hospital acquired infections Patient overall satis...
Prophylactic Antibiotics One Hour Prior to Incision
Hours of Behavioral Restraint Use
Inpatient Psychiatry: Discharge Care Planning
VAP per 1000 Ventilator Days Ventilator Days were 777 in 2006 and 645 in 2007
VAP per 1000 Ventilator Days
Number of VAPs and Ventilator Days
CCRMC 30 Day Readmission   Rates
Heart  Failure  Discharge Instructions Given
Heart  Failure  Discharge Instructions Given
Aiming for Perfect Care <ul><li>Discharge Instructions </li></ul><ul><li>Evaluation of LVS Function </li></ul><ul><li>ACEI...
Percent of Patients Who Received All Heart Failure Interventions at CCRMC
Percent of Patients Who Received All Heart Failure Interventions at CCRMC All-or-Nothing Measurement
Why the time is now
 
 
 
Who will if not you?
What can you do by next Tuesday?
Thank you <ul><li>Anna Roth, CEO </li></ul><ul><li>Contra Costa Regional Medical Center </li></ul><ul><li>[email_address] ...
 
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SFGH Quality Leadership Training

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SFGH Quality Leadership Training

  1. 1. Welcome Back Quality Leadership Academy Session 3 “ How Do You Know it Works?” Anna Roth, RN, MS, MPH
  2. 2. Report on Projects to Date <ul><li>Refine and refresh aim statements </li></ul><ul><li>Share results of our small tests of change </li></ul><ul><li>Will or did you revise your test of change? </li></ul><ul><li>If so, what would/did you revise and why? </li></ul>
  3. 4. Theory
  4. 5. Today’s Objectives <ul><li>Review results of your project </li></ul><ul><ul><li>Review small tests of change </li></ul></ul><ul><li>Review techniques for organizing and displaying data for maximum impact </li></ul><ul><li>Your toolkit- Driver Diagram </li></ul><ul><li>Share examples of reports designed to get the attention of those who need the information </li></ul><ul><li>Action Planning </li></ul>
  5. 6. How will we know?
  6. 7. Why Else Should We Measure? <ul><li>You can’t manage what you don’t measure </li></ul><ul><li>How else would you know that your steps are making things better or worse? </li></ul><ul><li>It’s often cause for reward, recognition and celebration </li></ul>
  7. 8. Choosing appropriate statistics
  8. 9. Median v Mean <ul><li>10 people are on the bus </li></ul><ul><li>The mean income of the riders is $50,000/yr </li></ul><ul><li>The median income of the riders is $50,000/yr </li></ul><ul><li>What does this tell us????? </li></ul>
  9. 10. Median v Mean <ul><li>The median income of the riders remains $50,000/year </li></ul><ul><li>The mean income is now approx $50 million </li></ul><ul><li>So is the average income of bus riders now $50 million because Bill Gates got on the bus? </li></ul>
  10. 11. Mean <ul><li>Mean (average) </li></ul><ul><ul><li>Measures the center, or middle, of a numerical data set </li></ul></ul><ul><ul><li>The sum of all the numbers divided by the total number of numbers </li></ul></ul><ul><ul><li>May not be a fair representation of the data </li></ul></ul><ul><ul><li>Easily influenced by outliers </li></ul></ul>
  11. 12. Median <ul><li>Median </li></ul><ul><ul><li>Also measures the center of a numerical data set </li></ul></ul><ul><ul><li>Much like the median of an interstate highway </li></ul></ul><ul><ul><li>The point at which there are an equal number of data points whose values lie above and below the median value </li></ul></ul><ul><ul><li>Is truly the middle of the data set </li></ul></ul><ul><ul><li>Better measure of CT than the mean when there are outlying values in the data set </li></ul></ul>
  12. 13. Percentage or Percentile? <ul><li>Suppose your score on the GRE was reported to be the 80 th percentile </li></ul><ul><li>Does this mean you scored 80% of the questions correctly? </li></ul>
  13. 14. Honest Errors <ul><li>Arithmetic errors or omissions </li></ul><ul><ul><li>Check to see if everything adds up </li></ul></ul><ul><ul><li>Double check even the basic calculations </li></ul></ul><ul><ul><li>Verify the total to put results in proper perspective; if sample size really small you may not want to use </li></ul></ul>
  14. 15. Excercise
  15. 16. Report Out
  16. 17. Back in 15 minutes
  17. 18. Finding your way/Telling your story
  18. 19. Data Display and Analysis <ul><li>How do you want to tell your story?? </li></ul><ul><li>Who are you going to tell your story to? </li></ul>
  19. 20. Common types of data display <ul><li>Pie charts </li></ul><ul><li>Bar graphs </li></ul><ul><li>Tables </li></ul><ul><li>Time charts </li></ul><ul><li>Run charts </li></ul><ul><li>Control charts </li></ul>
  20. 22. Charts and Graphs and Spiders Oh My <ul><li>Watch for pitfalls </li></ul><ul><li>Size matters! </li></ul><ul><li>Be aware of tick marks on the y-axis </li></ul><ul><li>10s, 20s, 100s, 1000s? </li></ul><ul><li>Check the scale to put results in perspective </li></ul>
  21. 23. Sizing up a pie chart <ul><li>Do the percentages add up to 100 </li></ul><ul><li>Beware of slices that are called ‘other’ if they are larger than many other slices of the pie </li></ul><ul><li>Look for a reported total number of units so you can see how big the pie was before it was divided up </li></ul>
  22. 26. Elements of a Control Chart UCL LCL X Indicator Time An indication of a special cause
  23. 27. Non-Random Rules for Run Charts
  24. 28. Variation Common Cause vs. Special Cause <ul><li>Common cause </li></ul><ul><li>Always present </li></ul><ul><li>Inherent in process </li></ul><ul><li>Is due to regular, natural, ordinary causes </li></ul><ul><li>Results in a stable process that is predictable </li></ul><ul><li>Special cause </li></ul><ul><li>Abnormal, unexpected </li></ul><ul><li>Due to causes not inherent in process </li></ul><ul><li>Also known as non-random or assignable process </li></ul>
  25. 31. <ul><li>Special cause </li></ul><ul><li>Identify and study special cause </li></ul><ul><li>If negative, minimize or prevent </li></ul><ul><li>If positive, build into process </li></ul>Appropriate Actions to Take <ul><li>Common cause </li></ul><ul><li>If undesirable need to change the process. </li></ul><ul><li>If only common cause variation and treat as special cause (tampering), leads to greater variation, mistakes, defects </li></ul>
  26. 32. First 24 Observations from Red Bead Data (without outlier employee)
  27. 33. 12 Runs expect to find between 8 and 18 runs
  28. 36. On Death, Dying & Data DENIAL ANGER BARGAINING DEPRESSION ACCEPTANCE
  29. 37. On Death, Dying & Data DENIAL “ The data are wrong” ANGER “ The data are right, but it’s not a problem” BARGAINING “ The data are right; it is a problem; but it is not my problem.” DEPRESSION “ This feels too hard to do” ACCEPTANCE “ I accept the burden of improvement ”
  30. 38. Stages of Facing Reality: “To live divided no more” <ul><li>“The data are wrong” </li></ul><ul><li>“The data are right, but it’s not a problem” </li></ul><ul><li>“The data are right; it is a problem; but it is not my problem.” </li></ul><ul><li>“ I accept the burden of improvement” </li></ul>
  31. 39.
  32. 40. Crimson Bead Company
  33. 43. “ Every system is perfectly designed to achieve the results that it achieves” Berwick: central law of improvement BMJ 1996 312:619-622
  34. 44. Discussion
  35. 45. Oversight
  36. 46. Oversight <ul><li>Project-level e.g. </li></ul><ul><li>% AMI patients getting evidence-based care </li></ul><ul><li>% Pneumonia patients getting evidence-based care </li></ul><ul><li>Time to answer call light on 5 West </li></ul><ul><li>System-level e.g. </li></ul><ul><li>Hospital mortality rate </li></ul><ul><li>Cost per admission </li></ul><ul><li>Adverse drug events per 1000 doses </li></ul><ul><li>Patient satisfaction scores </li></ul>Lesson #3 Execution
  37. 47. Projects Connected to Big Dots * Mortality Rate * Cost per Admission * Adverse Events * Functional Outcomes * Patient Satisfaction * 3 rd Available Appointment * Voluntary Turnover * Condition-specific, clinical process indicators * Preventive care measures * Office visit cycle time * ER to bed placement time * PACU to bed placement time * ICU to bed placement time * Bed to LTC placement time * ICU mortality * Catheter related BSI * Average ventilator days per patient * Adverse events/ICU day * Surgical Site Infection Rate * Percent of un-reconciled medications * Staff reporting positive safety climate * Percent of turnover in first year * Employee loyalty
  38. 48. A Senior Leader Perspective on Projects The Project: e.g., Ventilator-Acquired Pneumonia Spreading and Sustaining This Improvement Spreading and Sustaining These Design Concepts: “A Place Where…” <ul><li>Changing the Organization: </li></ul><ul><li>HR </li></ul><ul><li>IT </li></ul><ul><li>Finance </li></ul><ul><li>Leadership Processes </li></ul><ul><li>Business Strategy </li></ul><ul><li>Environmental Strategy </li></ul>
  39. 49. Issues at Each Tier (Examples) Tier 1: Big Dot Tier 2: Portfolio Tier 3: Projects <ul><li>Aims of strategic importance to the system as a whole </li></ul><ul><li>“ Big Dot” measure of progress </li></ul><ul><li>Executive, Board and Senior Leader engagement </li></ul><ul><li>Vision and the associated structural changes </li></ul><ul><li>Strong linkage to finance </li></ul><ul><li>Learning and mitigation of risks </li></ul><ul><li>Managing the learning, the politics, and the risks </li></ul><ul><li>Understanding “drivers” and causal linkages </li></ul><ul><li>Outcomes of consequence tracked over time </li></ul><ul><li>Middle Management key </li></ul><ul><li>“ Connecting the Dots” – putting the learning together </li></ul><ul><li>Continual readjustment of portfolio </li></ul><ul><li>Strong linkage to finance </li></ul><ul><li>Some structural changes (e.g., job roles) </li></ul><ul><li>Team organization and capacity matter </li></ul><ul><li>Process and outcome tracked over time </li></ul><ul><li>Leaders remove obstacles </li></ul><ul><li>Change concepts help </li></ul><ul><li>Ability to run PDSA cycles </li></ul><ul><li>Temporary infrastructures facilitate progress </li></ul>
  40. 51. Project Level Measure (Tier 3) <ul><li>Family assistance </li></ul><ul><li>May 05 to Oct 06: 17 months of NO VAP’s </li></ul><ul><li>IHI Mentor Hospital </li></ul><ul><li>Bundled orders with opt out </li></ul><ul><li>30 degree head of bed elevation marked on walls with tape </li></ul><ul><li>Now spreading to floor beds post extubation </li></ul>
  41. 52. “ One Patient, One List” Project Level Measure (Tier 3)
  42. 53. Project Level Measure (Tier 3) <ul><li>% meds unreconciled:admission 25%  3% </li></ul><ul><li>% meds unreconciled:transfer 12%  4% </li></ul><ul><li>% pre-admit meds unreconciled 19%  1% </li></ul><ul><li>% of patients with ANY unreconciled </li></ul><ul><li>meds decreased from 36%  3% </li></ul><ul><li>Discharge….still testing </li></ul>
  43. 55. Driver Diagrams
  44. 56. What Changes Can We Make? Understanding the System for Weight Loss “ Every system is perfectly designed to achieve the results that it gets”
  45. 57. How Will We Know We Are Improving? Understanding the System for Weight Loss with Measures Measures let us • Monitor progress in improving the system • Identify effective changes
  46. 59. AIM Primary Driver Secondary Driver
  47. 60. <ul><li>At your tables write down 4-6 primary drivers for your project </li></ul><ul><li>For each primary driver, come up with 2-3 secondary drivers </li></ul><ul><li>If you have time, write a few small tests of change for each secondary driver </li></ul>
  48. 61. Report Out
  49. 62. Tying it together
  50. 63. Transforming Care at the Bedside (TCAB) Med-Psych Workgroup Clinical Informatics ED Safety Central Line Infection Team Multidisciplinary Rounds Rapid Response Team Office Practice Team Perinatal Impact Team Total Joint Team VAP Prevention Team Perioperative Care Medication Reconciliation Team
  51. 65. System Level Aims
  52. 66. System Level Aims Primary System Aims Additional System Level Aims Zero Hospital acquired infections Patient overall satisfaction to be >90% Readmission rate to decrease by 30% Planned System Level Aims to begin by 2010 Eliminate inequality in at least ten improvement /operational areas by 25% Reduce Ambulatory Care Sensitive Admissions (ACS) to CCRMC by 15% Patient engagement on every innovation and improvement team by January 1, 2010 Develop a formal process for engagement of ethics expertise in operations and quality improvement.
  53. 67. Prophylactic Antibiotics One Hour Prior to Incision
  54. 68. Hours of Behavioral Restraint Use
  55. 69. Inpatient Psychiatry: Discharge Care Planning
  56. 70. VAP per 1000 Ventilator Days Ventilator Days were 777 in 2006 and 645 in 2007
  57. 71. VAP per 1000 Ventilator Days
  58. 72. Number of VAPs and Ventilator Days
  59. 73. CCRMC 30 Day Readmission Rates
  60. 74. Heart Failure Discharge Instructions Given
  61. 75. Heart Failure Discharge Instructions Given
  62. 76. Aiming for Perfect Care <ul><li>Discharge Instructions </li></ul><ul><li>Evaluation of LVS Function </li></ul><ul><li>ACEI or ARB for LVSD </li></ul><ul><li>Adult Smoking Cessation Advice/Counseling </li></ul>
  63. 77. Percent of Patients Who Received All Heart Failure Interventions at CCRMC
  64. 78. Percent of Patients Who Received All Heart Failure Interventions at CCRMC All-or-Nothing Measurement
  65. 79. Why the time is now
  66. 83. Who will if not you?
  67. 84. What can you do by next Tuesday?
  68. 85. Thank you <ul><li>Anna Roth, CEO </li></ul><ul><li>Contra Costa Regional Medical Center </li></ul><ul><li>[email_address] </li></ul><ul><li>safetynethospital.blogspot.com </li></ul>
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