LEARNING BY DOING

1. LEARNING BY DOING An improvement simulation exercise Brant Oliver Suzie Miltner QSEN National Forum May 2016

2. Learning Objectives After completing this simulation exercise, participants will be able to: • describe the IHI Model for Improvement, including the Plan-Do-Study-Act Cycle; • conduct simple PDSA cycles in a simulated environment; • create simple data displays for performance measurement; and • describe and interpret Run Charts.

3. In this exercise we will simulate the model for improvement… IHI (2004)

4. The PDSA Cycle 4. ACT 1. PLAN • Objective (goal) • Outcome predictions • Implementation plan (who, what, where, when, how) • Measurement plan • What changes • are to be made? • Next cycle? • Action based on • prior results 3. STUDY 2. DO • Complete data analysis • Compare to • predictions • Summarize what • was learned • Carry out the plan • Document problems • and unexpected • observations • Begin data • analysis

5. Simulation Exercise: Mr. Potato Head • Credits: • Original program: Institute for Healthcare Improvement (IHI), Cambridge, MA (2004) • Adapted by Steve Harrison, Sheffield MCA, Sheffield, UK (2013) • Adapted for collaborative simulation with real time measurement dashboard and registry (B. Oliver, 2015, 2016) & playbook (M Godfrey (2015). A scene from “Toy Story” (Pixar Studios)

6. Imagine that building Mr. Potato Head is improving the quality of diabetes care in a primary care setting...

7. Your Evidence Based Practice GuidelinePotato Head

8. What we aim to achieve… • “Build it right” (adhere to the evidence based practice guideline) • “Build it fast” (optimize access to care) • “Do it consistently” (optimize reliability) • “Continuously improve” (optimize value)

9. Microsystem Teams for the PDSA Simulation… • Surgeon • Timer • Recorder • Observer

10. We will simulate a microsystem level improvement collaborative… • 1 Baseline cycle and successive PDSA cycles • Simulate rapid cycle improvement in separate microsystems • Track performance (building speed and accuracy score) using Run Charts and descriptive displays • Cascade measures and simulate an improvement collaborative- compare gender, balanced measures • Benchmarking • Playbooks

11. 3 1 Improvement Ramp A P S D A P D S A P S D PDSA Successive PDSA cycles for Improvement Speed & Accuracy Hospital Admissions 2 Hospital Admissions

12. Measuring performance over time using Run Charts 1) Median Performance level 2) Range (Precision) 3) Type of Variation (Common or Special)

13. Shifts A SHIFT is eight (8) or more consecutive points above or below the median.

14. Trends A TREND is seven (7) or more consecutively increasing or decreasing points.

15. Common Cause Variation caused by chance causes, by random variation in the system, resulting from many small factors. Example: Variation in work commute due to traffic lights, pedestrian traffic, parking issues. Special Cause Variation caused by special circumstances or assignable cause not inherent to the system. Example: Variation in work commute impacted by flat tyre, road closure, heavy frost/ice. Statistically significant Types of Variation 23

16. Application – Responding to Variation Identify the Cause: If Positive: “Maximize, optimize, replicate, or standardize.” If Negative: “Minimize or eliminate” Special Cause Variation Reduce Variation (Increase Precision): Make the process even more reliable. Sub-Optimal Average Performance: Redesign process to get a better result. Common Cause Variation 24

18. Benchmarking helps to empower improvement collaboratives…

19. Potato Head “Best Practice” Flow Diagram to Standardize for Playbook

20. “Potato Head Playbook”