The document discusses using data for improvement, accountability, and research in healthcare. It outlines three aspects of using data: improvement aims to improve care through testing changes, accountability focuses on comparison and evaluation of current performance, and research aims to generate new knowledge through controlled testing and hypothesis testing. The document also discusses run charts and control charts as tools to analyze data over time and distinguish common and special cause variation.

1. Theory Burst Using Your Data for Real Joanne Watson Faculty member The Hospital Pathways Programme A partnership between The King’s Fund Point of Care and The Health Foundation

2. Engaging Heart & Minds ‘ If you want to build a ship do not gather men together and assign tasks. Instead teach them the longing for the wide endless sea’. (Saint Exupery, Little Prince)

3. Run Charts & the Principles of Statistical Process Control How do you know that a change is an improvement?

5. Data for Improvement, Accountability and Research in Health Care “ The Three Faces of Performance Measurement: Improvement, Accountability and Research.” Solberg, Leif I., Mosser, Gordon and McDonald, Susan Journal on Quality Improvement . March 1997, Vol.23, No. 3. Aspect Improvement Accountability Research Aim Improvement of care Comparison, choice, reassurance, spur for change New knowledge Methods: Test Observability Test observable No test, evaluate current performance Test blinded or controlled Bias Accept consistent bias Measure and adjust to reduce bias Design to eliminate bias Sample Size “ Just enough” data, small sequential samples Obtain 100% of available, relevant data “ Just in case” data Flexibility of Hypothesis Hypothesis flexible, changes as learning takes place No hypothesis Fixed hypothesis Testing Strategy Sequential tests No tests One large test Determining if a change is an improvement Run charts or Shewhart control charts No change focus Hypothesis, statistical tests (t-test, F-test, chi square), p-values Confidentiality of the data Data used only by those involved with improvement Data available for public consumption and review Research subjects’ identities protected

6. Clinical Research Study

7. Clinical Research Study Sample

8. Improvement Study

9. Improvement Study Sample

10. Two fundamental questions that data can help answer What will happen in the future? Why are things working as they are (or NOT)?

11. Cycle time results for units 1, 2 and 3

12. Cycle time results for units 1, 2 and 3 Unit 2 Unit 1 Unit 3 Unit 2

13. Dynamic View of a Process Run chart Tells the story over time Predicts the future Spread Centre Sequence

14. Control Chart Points equally likely above or below the centre line Shows different types of variation

15. Control Chart Points equally likely above or below the centre line Shows different types of variation

16. Two types of variation Common Cause Is inherent in the design process Reflects ‘business as usual’ state of the process Is due to ordinary causes Affects all outcomes of the process Results in a ‘stable’ distribution which is predictable Also known as random variation Special Cause Due to irregular or unnatural causes that are not inherent in the design of the process Reflects a ‘different mode’ of the process Affects some but not necessarily all of the process Results in an ‘unstable’ process that is not predictable Also known as non-random variation

17. Common Cause Variation Points equally likely above or below the centre line No shifts or trends

18. Special Cause Variation Intentional- when we are trying to change the system Unintentional – when the system is out of control

19. Reading Run Charts What will happen in the future? Why are things working as they are (or NOT)?

20. Shift - 6 or More Consecutive Data Points All Above or All Below the Median

21. Trend - 5 or More Consecutive Points Up/Down

23. HPP