Quality Improvement Methodology – Next Steps

1. Quality Improvement Methodology – Next Steps

2. Purpose of this Session Consider the components of a learning system in your own improvement activity : 4. Sequential testing of new theories 6. Planning for spread at scale What stage are you at in relation to these aspects of the improvement journey? What do you need to do next to ensure that your tests are scaled up and spread correctly?

3. Aim Measures Changes Testing & Implementation The Improvement Guide, API

4. Cycles of Tests Build Confidence Changes that will result in improvement Learning from data Proposals, theories, hunches, intuition

5. Startsmall • 1 child • 1 day • 1 family • 1 setting Move to 3,5,7…. as confidence grows

6. Years Quarters Months Weeks Days Hours Minutes You can only learn as quickly as you test! Drop down next “two levels” to plan test cycle!

7. Components of a Learning System • System level measures • Explicit theory or rationale for system changes • Segmentation of the population • Learn by testing changes sequentially • Use informative cases: “Act for the individual learn for the population” • Learning during scale-up and spread with a production plan to go to scale • Periodic review • People to manage and oversee the learning system • From Tom Nolan PhD, IHI

8. Sequential Testing & Scale Up Aim Achieve improved communication process for HV and SW handovers using standardised format Data/learning/adapting Implement Cycle 6: Test all handovers-1 week Cycle 5: other team HVs/SWs- 1 day Cycle 4:Test with all handovers-1 HV/1week Cycle 3: Test with 5 more families/ same HV (Mon/Tu) Hunch/Theory A structured handover will ensure accurate information sharing between prof teams Cycle 2: Test with 3 family handovers/ same HV (Wed) Cycle 1: Test developed handover form 1 HV/1 SW 1 family (Mon)

9. Learning Through Sequential Testingat your tables discussion time Where are you currently? Can you describe an example of the following? Multiple tests with different people/under different conditions? Learning/data captured to describe your testing journey?

10. Testing v Implementation Testing – Trying and adapting existing knowledge on small scale. Learning what works in your system. Multiple tests in a variety of conditions………………… Implementation– Making the change a part of the day-to-day operation of the system. Permanent change Would the change persist even if its champion were to leave the organisation? Avoid implementation until confident that processes are robust

11. How Many Tests?

12. Components of a Learning System • System level measures • Explicit theory or rationale for system changes • Segmentation of the population • Learn by testing changes sequentially • Use informative cases: “Act for the individual learn for the population” • Learning during scale-up and spread with a production plan to go to scale • Periodic review • People to manage and oversee the learning system • From Tom Nolan PhD, IHI

13. A Framework for Spread Leadership • Topic is a key strategic initiative • Goals and incentives aligned • Executive sponsor assigned • Day-to-day managers identified Measurement and Feedback Social System • Key messages • Communities • Technical support • Transition issues Set-up • Target population • Adopter audiences • Successful sites • Key partners • Initial spread plan Better Ideas • Develop the case • Describe the ideas Communication (awareness & technical) Knowledge Management Institute for Healthcare Improvement

14. Things to Consider Checklists For Spread • Leadership, Better Ideas & Set Up • General Communication & knowledge transfer eveloping Measurement, Feedback and Knowledge Management Systems

15. Take a strategic approach to scaling up

16. Increasing uptake of Healthy Start Vitamins

17. Are You Ready for Scale up & Spread?Table discussion time In the context of your current improvement activity: Have you been testing a theory so that it could be considered ready for implementation in the area you are working? and/or… Do you have a strategy for moving to scale up and spread to other sites/teams?

18. The Seven Spreadly Sins Step 1 Start with large pilots Step 2 Find one person willing to do it all Step 3 Expect vigilance and hard work to solve the problem Step 4 If a pilot works then spread the pilot unchanged Step 5 Require the person and team who drove the pilot to be responsible for system-wide spread Step 6 Look at process and outcome measures on a quarterly basis Step 7 Early on expect marked improvement in outcomes without attention to process reliability To be clear DO NOT DO THIS AT HOME!!! Institute for Healthcare Improvement

19. Sustaining Improvement • Having the correct measures to provide assurance that new processes are reliable • Measuring compliance or satisfaction through regular and random sampling of the population • Understanding the variation that exists in your data

20. What measures? Outcome measures – directly relates to the overall aim what is the result? how is the system performing? Process measures – are the processes that contribute to the aim performing as planned? Balancingmeasures – assessing from different dimensions unanticipated consequences, other factors influencing the outcome

21. N T V R If we don’t understand the variation that lives in our data, we will be tempted to… O I A A I • Deny the data as it doesn’t fit with our view of reality • See trends where there are none • Try to explain natural variation as special events • Blame and give credit to people for things over which they have no control • Distort the process that produced the data • Kill the messenger!

22. Common Cause Variation

23. Measurement of Improvement Define measures that will measure the impact of the Improvement work over time They will guide your progress through and beyond testing to implementation and monitoring for continuous improvement. Different ways of measuring e.g., • Percent compliance with process • A count of correct attempts/number of attendances • Verbal feedback /surveys

24. Why a run chart and not just a graph or table? Run Chart Prompt sticker used on all referrals Testing screening tool with 3 Families Prompt sticker tested in case referral Median = 84% Testing screening tool with 5 families Testing screening tool with 1 family Testing with all Families Month HC Data Guide

25. Non-Random Rules For Run Charts A Shift: 6 or more A Trend: 5 or more Too many or too few runs An astronomical data point Source: The Data Guide by L. Provost and S. Murray, Austin, Texas, February, 2007:

26. What is this data telling you? Month Data 11 1 3 4 5 9 10 4 11 7 10 10 7 6 22 4 2 2 1 3 4

27. What is this data telling you? Astronomical Point Shift Median = 5 Trend Shift Month Median 1 1 2 2 3 3 4 4 4 4 5 6 7 7 9 10 10 10 11 11 22

28. Measurement Principles • Develop aims before measuring • Design measures around aims ‘How Good By When’ • Be clear on your operational definitions • Establish a reliable baseline • Track progress over time using annotated run charts • Teams need measures to give them feedback that the changes they are making are resulting in improvement • Need to understand common cause and special cause variation to ensure we don’t over/under react to situations

29. Appreciation of a System • Complex system of interaction between people, procedures and equipment • Success depends on integration, not performance of individual parts Theory of Knowledge • Change is prediction of improvement based on knowledge of the system • Learning from theory, experience Operational definitions are the basis for improvement with PDSA cycles for learning Psychology • Interaction of people with systems • Motivation & will of individuals & teams • Situation awareness/decision making • Managing stress and fatigue • Helps planning for change management Improvement Understanding Variation • Variation is to be expected – everything we measure varies We make decisions based on interpretation • Data over time – data story of what has been happening Aims & Values

30. What’s the Scope of Change? System Targeted for Implementation(Defined by Aim) Single-unit prototype: segments As you move from pilot testing to implementation to spread, your population of interest will need to be adjusted. Spread to Total System(Additional units, sites, organisations)

31. Thank You