Scottish Improvement Skills

Scottish Improvement Skills
Workshop 1 Day 3 Scottish Improvement Skills Full programme Workshop 1 Day 3 Facilitator slides and notes

Aim Warmers/consolidation Lead facilitator 1. What is your process for waking up in the morning? Process mapping – own process – waking up to leaving the house. If you haven’t finished drawing your process map (waking to leaving the house routine), finish it now (2-3 mins) Exchange with partner for a few minutes, then share ideas for improving both processes: Look for potential improvements. What criteria might you want to consider? Eg time, what could be done the night before, amount of sleep. Then discuss in turn for a few minutes. Before moving on to the next slide, elicit 4 components of Profound Knowledge, and why it’s a useful framework.

System of Profound Knowledge
PRACTICE Aim Consolidate earlier learning about the System of Profound Knowledge Build confidence in understanding and applying the framework Key messages You can apply the framework to human activity in any setting Timing 30 mins Materials 1. I love Lucy video (Chocolate Factory) 2.02 minutes 2. Speakers 3. 4 flip chart sheets + pens (blue or black) 4. I Love Lucy ‘answer key’ for facilitators – this is designed to give facilitators some idea of what participants may come up with, based on previous groups; it does not need to be referred to specifically during the activity. Lead facilitator Before watching the video Split participants into 4 groups – maximum 6-7 people per group. If the cohort is larger than 30 people, it may be better to have 8 tables ie 2 tables for each lens. Assign each group one of the lenses of the System of Profound Knowledge. Watch the video looking at what you see through that lens, jotting down any key observations that you wish on note paper. After watching the video Write up on flip chart sheets everything you saw through your lens. Support facilitators If enough Support facilitators for one per table, they could facilitate this task, possibly including writing on the flip chart sheet. Debrief Plenary – one person report back from each table, then opportunity for general discussion eg on overlap of issues across different lenses. Specifically ensure there is some discussion about what outcome was achieved. If remaining in the room for further modules, put the flip chart sheets up on one wall so that participants can refer to them during other activities. Deming 2000

Day 3 Planning a test of change using the Plan-Do-Study-Act framework
Creating measurement plans Using run charts to tell an improvement story Influencing colleagues and other stakeholders Bringing it all together – sharing your knowledge By the end of today you will have a detailed measurement plan and data collection plan, you will have identified some of the main issues around managing your stakeholders. You will have developed an understanding of how data can help us in our improvement efforts.

Planning a test of change using the PDSA framework
By the end of this session you will be able to: explain all stages of the PDSA framework to others (planning, including theory and prediction; analysing results; applying learning to next cycles) use all stages of the PDSA cycle in your improvement work. Learning outcomes We’re going to look at some healthcare examples, then you’ll prepare a PDSA plan for your first test cycle.

The PDSA cycle involves consideration of all components of the System of Profound Knowledge: You are testing your theory of knowledge You need to consider how to engage people in your test You collect and analyse data, taking into account your understanding of your system. Deming 2000

ICU Length of Stay Aim Primary Driver Secondary Driver Change idea Reduce average length of stay in Central General ICU by 20% by March 2015 Assessment and management of sedation and agitation Improved management of delirium Validated sedation tool Appropriate sedation Incidence of acute cognitive dysfunction Structured daily sedation breaks Staff information leaflets Staff training sessions Improve multidisciplimary team communication Assess sedation and agitation using the RASS tool (Richmond Agitation Sedation Scale) Use ear plugs to improve sleep Use Dexmedetomidine as alternative to benzodiazipines Develop and follow guidelines for daily sedation breaks Assess for delirium using CAM-ICU (Confusion Assessment Method for ICU patients) Investigate and correct underlying causes Include delirium as part of safety brief Medical staff and family jointly keep an ICU diary Improved identification of delirium Validated delirium tool Staff education Care bundle initiated within 2 hours of diagnosis Engage with patient/family/carer Mobilise patients earlier and more frequently PRACTICE You are going to look at some completed PDSA cycles reports – ‘report’ just meaning that everything done is recorded, using a standard PDSA template. The examples come from this case study, and relate to this change idea [see highlighted boxes]

Testing change: ICU examples
Sequence the 5 PDSA reports What links do you identify from one cycle to the next? What do you notice about the scale of the tests? How many different measures were used? How did theory change through the cycles? How did qualitative data inform subsequent tests? Aim Participants can name the key components of a PDSA plan and report. Participants can explain why it is important to go through all four stages of the PDSA cycle Participants can describe the linkages between different parts of one PDSA cycle and between one cycle and the next. Timing 20 – 25 minutes This will go more quickly with a facilitator at each table. With floating facilitators, the discussion may take longer as prompt questions are fed in more slowly, and a longer plenary debrief is needed. Materials PDSAs to sequence P (dates removed and not numbered in sequence) – printed back to back PDSAs to sequence F (dates removed, numbered in sequence) – printed back to back Lead facilitator Tables of 5, preferably. Or 6-7 if necessary. Assign one facilitator to each table. If there are not enough facilitators for this, the activity can be done with support from floating facilitators. Distribute one set of 5 PDSAs around each table. If 5 participants, they get one each; if more than 5 they share. If only 4 participants, one each and one for anyone to pick up. Make sure they are not in sequence around the table – mix the sheets up before giving them out. First, each participant read their PDSA and then discuss them all, to put them in order. Once the first table has the PDSAs in the correct sequence, click again to bring up the questions. Then tables discuss the questions. End with a plenary debrief. This will need to comprehensively cover all the key messages below if tables have not had their own facilitator. If there has been a facilitator at each table, the plenary will be short – give support facilitators opportunities to feed back on key issues arising at their table; this may only need a minute or two. Support facilitators These PDSAs are all from the same ramp, for the same change idea (so the high level aim and change idea remain the same for all cycles). It is not necessary to join the tables until everyone has read their PDSA. Try not to intervene in the sequencing until it’s clear the group is stuck. Notice in particular if the group has turned the sheets over (often they don’t). When the group thinks they have the correct sequence, as them to tell you the sequence (referring to the ‘aim for this test of change’; feedback only how many are in the right place and how many are not in the right place. Facilitate the ongoing sequencing, in particular questioning assumptions they may be making because they haven’t read everything. By the end of the activity, the following issues should have been addressed. Key messages/issues for discussion (in no particular order) The difference between developing and testing change. The first PDSA cycle here is developing. Note how it involves finding out what others have done and consulting subject matter experts. Note the links between Study/Act in one cycle, and Aim and tasks for the next cycle. If you don’t pay sufficient attention to Study/Act stages, you’ll end up with the following cycles not being helpful. Note the flow through the numbered sections – Questions, Predictions, Measures and Study. This framework helps you to make sure that you don’t miss anything out. Questions are open (not ‘yes/no’ answers) – this will allow for richer learning. Tests can ‘fail’ in all components or part. This still gives valuable learning. Failure is an important part of the testing process. We learn from failure at least as much as from success. Scale can go down as well as up eg if following failure or testing something a bit different. Qualitative data is a particular feature of early tests in relation to a particular change idea. Very valuable – it can give you ideas you wouldn’t otherwise have thought of, inform future tests as well as provide a basis for collecting quantitative data later (to be looked at briefly after this activity). (less important but possibly consider at what point you might want to start using %, when numbers are small). Theory is constantly evolving. When it settles, testing under different conditions and at larger scale, you are looking to increase your confidence in the theory. From the 2nd cycle onwards, the questions and measures are the same – it isn’t necessary to have new measures every time. This will allow you to compare data from one cycle to the next. ‘Do’ refers back to ‘Tasks’ but also includes comments on anything relevant that happened that was not planned. You may also address the following: Optional – issues that may arise Predictions can be positive or negative – over a series of tests you’ll probably need both. You might remove a task, rather than always doing something new – this may help messaging re improvement work not necessarily adding to work pressure. At the end of the activity participants each keep one of the sheets as an example of a complete PDSA. If there were more than 5 people at each table, give out additional copies.

Qualitative and quantitative data
“Why did you use the hand gel?” “Because it’s the start of my shift – I’ve just come from home.” Which of the following are occasions when you use hand gel? At the start of a shift Between patients After lunch etc On what percentage of occasions do you use hand gel at the start of your shift? 0 – – – – DISCOVERY Aim Participants use both qualitative and quantitative data and are able to make use of one to generate the other. Key messages Early tests of a change idea often produce more qualitative than quantitative data. Qualitative data is very valuable, including when it is unsolicited. Qualitative data can be used to ask new questions to generate quantitative data. At later stages in a project you might use quantitative data as a starting point to ask questions to generate qualitative data. Timing 5 minutes Lead facilitator This is one of the change ideas for the Staff Wellbeing scenario – using handgel. Each of the three questions comes up on a different click. Elicit the key messages from participants and fill in the gaps where necessary. Click 1: Is this qualitative or quantitative? Is it useful? Why? What could you do with it? Eg, if you get a number of different responses like this, you could use them to stratify, and this may help you plan changes. Eg reasons why people don’t use handgel You could either eg identify gaps – when are people not using it, or what is missing in their reasoning – what education might they need? OR, convert it to quantitative data and do a survey. Click 2: That would give you data that you could put in a Pareto chart. Eg Quantitative Qs: Which of the following is the reason why you do/not use handgel? Multiple choice – include and ‘other’ option – which will give you more qualitative data. Click 3: another example – this could be generated from either of the other two. When are you most likely to use qualitative data? Early tests of change: Either because you ask open questions – like the ‘Why’ above to a small number of people Or because the way you engage people at the start means they may be more likely to give you unsolicited feedback about a change, or about current ways of working. Welcome it, and make good use of it: To generate quantitative measures To tell more engaging stories Also use qualitative data later, but less because often dealing with much large volumes of response. Thinking about your own project: When you prepare your PDSA plan – what qualitative data would you like to collect? What will you be doing that may give people opportunities to give unsolicited qualitative data? Think about this as you plan your first PDSA cycle (see next slide)

Project work: plan a test of change
Create an aim for your first test of change: how good, by when? Start small Match your questions to your predictions and measures Plan tasks Complete who/when/where PRACTICE Aim Participants use the standard template to plan their first test of change. This is an opportunity for them to identify what else they may need to know or who they may need to involve to make the test happen. Participants do this test in the next week. Key messages Complete every section of the plan – it’s designed to guide your thinking Don’t wait to carry it out – you can start doing tests of change while you work on developing your thinking about your project in parallel (eg drafting project charter) Timing 15 – 20 minutes Materials SIS PDSA template Lead facilitator Talk through task instructions Draft your plan individually. Then share with a partner and help each other improve it. Facilitators Monitor the room, support as requested, intervene to check that participants are on the right track.

Model for Improvement The Improvement Guide Langley J et al 2009 Aim
To briefly recap the session content to support a sense of learning and accomplishment and to aid recall following the session Provide the opportunity for participants to ask any outstanding questions Timing 1- 5 minutes depending on questions asked Key messages One PDSA cycle does not stand alone. Must go through all four parts Lead Facilitator Elicit key messages about PDSA cycles eg You now have a plan for a first PDSA cycle for your project. What are you going to do with it when you go back to work? Do it > study it > act on the learning from it > start a new cycle The Improvement Guide Langley J et al 2009

For the next wee while we are in this part of the System of Profound Knowledge: Understanding Variation. Deming 2000

Planning measurement By the end of this session you will be able to:
Create operational definitions for your measures that others can follow reliably to collect data Create a measurement plan for your improvement work. Aims To create operational definitions for your project To create a measurement plan Subsidiary aim depending on how challenging participants find this module: To be able to distinguish between different types of measure (count, %, and rate) and explain how to define a % and rate Key messages Importance of planning measurement and everyone who may collect data having a shared understanding of each measure and how to collect the data Importance of defining measures early in the project. Timing 60 mins minimum for the whole module Lead facilitator Learning outcomes: Lead Facilitator read out or ask participants to read Recap from Introduction to Measurement – elicit 3 types of measure and what they are: Outcome – voice of the customer Process – voice of the system Balancing – unintended consequences

How big is your banana? DISCOVERY Aim
Participants to be able to explain the importance of a clear, shared understanding of what and how teams are measuring. Key messages We need everyone in our team to be clear about what and how we are measuring in our improvement work. Timing 25 minutes Materials For each team: Banana Ruler Paperclip String – must be long enough to go end to end twice Paper – 2 sheets Pen/pencil And: Bananas online countdown timer (a mobile phone timer doesn’t work as well as it isn’t as loud, and people tend to ignore it) Two sets of numbers on paper or card 1 to 5 or 6 (ie to label the teams who first measure, then second measure – 2 different colours may help) Flip chart sheet prepared with numbers of teams (initial then other team measuring the same banana). Allow plenty of space to write several measures eg if they measured around as well as end to end. Lead facilitator Divide the participants into teams. No more than 5-6 teams as the key messages can easily be covered in debriefing on this quantity of examples. Set up countdown timer. Support facilitators Facilitator A: give out materials Facilitator B: Give each team a number (otherwise they forget later who has which team’s banana) Instructions Measure the size of your banana. Write out instructions for another team to follow so they can repeat the measurement process. Time limit: 7 minutes Use whatever pieces of kit you like and decide what measurement(s) to take. On a separate piece of paper note down the size of the banana - do not share this with anyone at this point. After 7 minutes they stop. Faciiltator pass the banana and measurement instructions to the next team i.e. 1 pass to 2 , 2 pass to 3 etc The next team measures the ‘new‘ banana according to instructions, and writes the measurements down on the instruction sheet. Give a 4 minute time limit, using the same countdown timer. If all teams finish before 4 minutes have elapsed, cancel the timer and move on to the debrief. Debrief: Lead Facilitator Note down both measurements for each banana on a flipchart, noting where there are differences in measurement for the same banana. Facilitate discussion about these differences. Support facilitators must have an opportunity to feed in to this discussion, as it is likely that they will have noticed things the Lead Facilitator may have missed.

Operational definitions
Define the specific components of the measure If it is an average, specify the calculation for deriving the average If it is a score (eg patient satisfaction score), describe how the score is derived. If it is a percentage or a rate, specify the numerator and denominator Describe any special equipment needed Describe the criteria to determine concepts such as ‘accurate’ or ‘complete’. PRACTICE Aim Participants distinguish between outcome, process and balancing measures Participants generate alternative measures for the same outcome or process Key messages There is usually more than one way we could measure the same thing Range of criteria for selecting a measure eg historical data or benchmarks, ease of collection. ease of standardisation What to include in an operational definition (possibly including the difference between a percentage and a rate Timing 15 minutes Materials Case study wall cards: Aim, drivers and change ideas already on the wall Measures Blu tac or Velcro Lead Facilitator Picking up from issues arising from bananas re operational definitions, do participants have anything to add to this slide, or any questions? Facilitators Depending on the number of participants and facilitators, decide in advance which case studies will be used. All facilitators decide which measures they will use first (it is not necessary to use all the cards, if time is short); make sure at least one example of each of the following is used: count, percentage, rate. Each facilitator go to one of the case studies, with the Measures cards and blutac/Velcro For each measure card, ask participants to match it to one of the components of the driver diagram and change ideas, and put the measure on the wall beside the relevant component. Then elicit: - whether the measure is O, P or B - any issues with defining it - logistical issues eg equipment required, calibration, resources/workload - possible alternatives to this measure; in which case, which measure might be most suitable and why And a brief discussion on stratification If all is going well, highlight (elicit if possible), the difference between a percentage and a rate. If participants are struggling, consider whether or not it will be helpful to introduce this distinction.

Project work: measurement planning
Pick one of your measures Create an operational definition Consider exclusions and stratifiers Provide outline data collection method PROJECT Aim Participants become familiar with a standard measurement plan template. Participants create at least one measurement plan for their project Timing 20 minutes Materials Measurement plan examples QIHub measurement plan template QIHub data collection plan template Consider having participants do this on laptops with preloaded templates Lead facilitator Pick one of the measures that you have so far outlined for your project. Referring to the examples, as well as the case studies on the wall: Write a detailed operational definition. Best to do this on the back of the sheet, to ensure the message is clear that more space is needed for this than the hard copy template allows. Complete the other sections of the plan. Facilitators Monitor the room. In particular check that operational definitions have sufficient detail. Be ready to answer questions about other sections of the plan eg stratifiers, exclusions, sampling. Options for early finishers: Draft a measurement plan (starting with the operational definition) for another project measure Look at the data collection plan template (in particular the guidance notes), which includes more logistical issues, and start drafting.

Planning measurement: summary
Aim To briefly recap the session content: To support a sense of learning and accomplishment To aid memory of the session later An opportunity for participants to ask any outstanding questions from any part of the session Timing 2 – 5 minutes, depending on time available Lead facilitator Elicit key messages from the module eg What’s the purpose of a measurement plan? (everyone has the same understanding of what you are measuring and how) What do you need to include? How many measurement plans do you need? (as many as you have measures) When do you create measurement plans? (for any new measure, asap to ensure it’s the right measure) Who should be involved? (not just you eg people who may ‘own’ historical data, people who will be collecting data, people who can help with equipment for measurement) Planning measurement: summary

Data analysis By the end of this session you will be able to:
Explain why it is important to measure data over time Interpret run charts using rules to differentiate between random and non-random variation Use run charts to explain outcomes of improvement work to others Use and explain the importance of using a family of measures. In this session we are looking at what we do with data once we’ve collected it. This is still about Understanding Variation Learning outcomes – Facilitator read out or participants read.

Understanding Variation
Random variation – affects everyone and all outcomes over time Non-random variation – does not affect everyone or not part of the system all the time; arises because of specific circumstances. Lead facilitator Here’s a reminder of what we mean by variation. When we analyse our data, we want to find out if variation in the data is random or non-random. And if non-random, is this because of a change we’ve introduced, or for another reason?

Analysing data: before and after
‘When you have two data points, it is very likely that one will be different from the other.’ W Edwards Deming DISCOVERY Aim Participants can explain why it’s important to analyse data in time series. Key messages To understand what’s going on in our system, we need our data to tell a story. Timing 5 minutes Lead facilitator Imagine this is Vanessa’s data, for her personal project ‘new healthier me’. Here her data is in a ‘before and after’ chart. Imagine she weighed herself once a week for a few weeks before making a change (either to her calories in or calories out), and averaged the data. Then she averaged the data over a period of several weeks after making the change. Elicit: Why is this not very helpful? A: It doesn’t tell us anything about the process. It’s a snapshot, and doesn’t tell us the full story behind that snapshot. Click: As Deming says … (see quote) We get more useful information by using a time series chart. See next slides.

Data analysis: Introduction to run charts
Weight (lbs) Lead facilitator Talk through the charts, going clockwise from top right. Elicit as much of this as possible. Chart top right Imagine you are helping a friend or family member to lose weight – this is their data. How would you explain it to them? Here weight is going down, but it was going down anyway – it is not because of the change we introduced into the system. We need to find out what is causing this. If we made a before and after bar chart using this data, would look like this one. Chart bottom right Here weight did seem to go down associated with our change, but then it went up again. Again, bar chart would be the same. Chart bottom left What story is this chart telling us? Here the data is showing us non-random variation: we have a New Healthier Me. If we plotted any of these time series charts as before and after charts, they would look like the one above. So we wouldn’t know which of these stories is associated with this data – we wouldn’t know whether or not the change made had resulted in an improvement. There are many other scenarios that would produce that same before and after chart. So, when measuring for improvement we most often use a line chart with data in time series. But these charts are not run charts. To really understand our data we need to add another feature to this chart [move on to next slide].

Data tells a story: New healthier me!
Aim Participants can distinguish a run chart from a line chart (run chart must have a median line). Participants can recognise the key features of a run. Lead facilitator A time series chart like the ones in the previous slide is helpful, but to make it even more helpful, and easier to analyse to identify whether we have any non-random variation, we apply 4 rules. These rules are based on the distribution of data points around the median. What is a median? (elicit) By applying 4 rules we can find out if any variation in the data is random – also sometimes called common cause variation – or whether it is associated with specific circumstances (we hope, the change that we introduced) – also sometimes called special cause variation. [NB – try to avoid special/common cause, as these terms refer to Control charts, not run charts. But explain the terminology if a query comes up.] What is a run chart? Features – point out on the slide A run chart is a line graph with a measure (vertical or y-axis) plotted over time (horizontal or x-axis). Each time data is collected, a new data point is added to the chart. The time steps depend on how often you are collecting data. For example this may be daily or hourly if measuring wait time for a particular service, but weekly, monthly or quarterly if measuring patient satisfaction. As well as the line of data points, a run chart also includes a median line. The median is the middle value of the data points plotted, so that half are above and half are below the median line. At this stage it is not necessary to introduce the concept of baseline and extended medians. They are used in this workshop to model good practice, so that participants are exposed immediately to the median that we want them to use. The module Analysing data, interpretation of run charts introduces different types of median, and Visual display of data: using Excel to create run charts goes through how to create them. These modules are both in Workshop 2. If a participant asks about the two different blue lines, or points out that in this chart there are not ‘half above and half below’ the median line, just explain that the median here was created using the data points up to and including 25 Feb.

Introduction to run charts
A ‘time series’ chart tells a story. Baseline data helps us to see whether a change is an improvement. Any changes made are shown on the chart. Summarise some key points from the previous example. As you could see from these charts: We need a time series chart – to tell a story We need baseline data – data collected before the change. We need to know on the chart when a change was made (and what that change involved) We also need to indicate anything else we are aware of that happened at a particular time that may have influenced the data. Before we look at the 4 rules, let’s see why it’s called a run chart.

What is a run? Vanessa’s Weight
Aim Participants can correctly count the number of runs in a run chart Key messages A run is a series of data points all on the same side of the median line. Either all above the median, or all below the median. The minimum number of data points in a run is 1. Data points on the median do not break a run, but are not counted as part of a run. Timing 15 minutes for all ‘counting runs’ section Material How many runs? Lead facilitator In advance of the session decide which of the two ways of counting runs you prefer to highlight. Try to use only one method in the session. Depending how participants get on with this, it may be necessary to introduce the second approach. Option A: This slide series is based on circling data points on the same side of the median line. Option B: Count the number of times the run line (the black line) crosses the median, and add 1. If using this approach, it would be better to remove the red circles from the following slides, and replace them with circles around the points where the run line crosses the median line. Participants often ask ‘why add 1?’: imagine you have an unsliced loaf of bread and you cut through it once ie you have one slice + the rest of the loaf. The cut is the equivalent of ‘crossing the median’. If you cut twice, you get two slices + the rest of the loaf. Talk through key messages. Q: how many runs are there in this chart? Allow a couple of minutes for participants to think through this alone and/or help each other in pairs/small groups. Click three times to bring up circles round the three runs in this chart. When you are analysing data in a run chart, it’s important always to be clear from the start whether ‘good’ is going up or going down. PRACTICE This slide is for Vanessa’s outcome measure: weight. The worksheet has three run charts, one for each of the change ideas that she tried. Count the runs – start doing this individually, then compare answers in pairs. Debrief using the following slides.

How many runs (1) ? Debrief: show the run chart, elicit the number of runs, then show the next slide which has the answer.

How many runs (1) ? Four runs.
4 data points on the median line – these are not ‘useful’ points. If a query comes up about what ‘useful’ means, (try to avoid this until introducing the run chart rules): There are rules that we use to help us understand whether the variation in our chart is random or non-random. Three of these rules are based on the statistical probability of data points being distributed in particular ways around the median. Because these data points are on the median line, they cannot be considered to be distributed on one side or the other of the median, so they do not contribute to application of these rules.

How many runs (2) ?

How many runs (2) ? Five runs.
No data points on the median these are all useful data points

How many runs (3) ?

How many runs (3) ? Six runs.

Run charts: signals that identify non-random variation
Six or more data points in a run (all above or all below median) Five or more consecutive data points all increasing or decreasing Too many or too few runs An ‘astronomical’ data point A shift A trend See table Consider DISCOVERY Aim Participants can identify whether there is any non-random variation by applying the four run chart rules. Key messages We apply these rules to find out if there are any signals of non-random variation. There is a signal of non-random change if any one of the four rules occurs. If more than one rule occurs, this strengthens the signal. All four rules should be applied to a run chart. The signal provides evidence of improvement if the change is in the desired direction. When a signal is identified, the improvement team should investigate to understand what caused the signal. Timing 25 minutes Materials Run chart rules Run charts: apply the rules Lead facilitator Don’t talk through the rules at this point – this slide provides a summary for reference later. The first three rules are based on the statistical probability of data points being distributed in particular ways.

Run charts: Rule 1 – a shift
6 or more consecutive data points either all above or all below the median line. Data points that fall on the median do not break a shift. A shift is always a run, but a run is not necessarily a shift. A shift is a run with at least 6 data points. A run is not a shift if it has 5 data points or fewer.

Run charts: Rule 2 – a trend
5 or more consecutive data points all going up or all going down. If two or more consecutive data points have the same value, only count one of them. A trend can cross the median line. Because the median line is not an issue with a trend, a data point on the median line can be counted when identifying whether or not there’s a trend. This chart includes two sections that our everyday use of the word might be considered as ‘trends’, but they are not: Feb – July 2012 – in this series of data points, some of them are lower than the one before Oct 2012 – Mar 2013 – this includes two ‘pairs’ of data points, where 2 data points have the same value, so in this series of data points we only count 4, not six, from top to bottom.

Run charts: Rule 3 (a) Too few or too many runs
Rule 3: too many or too few runs For this rule you need to use a data table. Calculate the number of useful data points. Find this number in the first column of the table. Count the number of runs. If the number of runs is below the lower limit or above the upper limit in relation to the number of useful data points, the chart is signalling non-random change. This chart has 16 useful data points, and 4 runs See next slide.

Run charts: Rule 3 (b) Total useful data points Total data points
For the chart below: Useful observations Lower number of runs Upper number of runs 15 5 12 16 13 17 18 6 14 19 20 Calculate the number of ‘useful’ observations (subtract the number of data points on the median from the total). Find this number in the first column. Count the number of runs. If this number is below the lower limit or above the upper limit, the chart is demonstrating special cause. 21 7 22 23 24 8 25 26 9 27 10 28 29 30 11 Total useful data points Total data points For 16 useful data points, the lower limit for random variation is 5 runs, and the upper limit for random variation is 13 runs. The chart has 4 runs, which is below the lower limit. This signals non-random variation. Because this rule is based on runs, and a shift is a type of run, Rule 3 and Rule 1 (shift) often occur together. When they do occur together, this provides a stronger signal of non-random variation.

Run charts: Rule 4 Rule 4: an astronomical data point
Unlike the other rules, this one is subjective, not based on statistical probability. This is a data point that is an obviously different value from the ones around it. Anyone analysing the chart would agree that it is very unusual. To identify an astronomical data point, the chart must include data points both before and after the data point in question. Astronomical data points are often an indicator of a person-dependent process. For example if a staff member is absent, or a new member of staff has not been adequately briefed on the process.

Applying the rules Change PRACTICE Material
Run charts: apply the rules Lead facilitator Now you are going to apply the four rules to find out if there are any signals of non-random variation. The charts we are looking at are from the Porter Productivity case study. Example in plenary to prepare for the following task. This chart is for the outcome measure: Porter Productivity. Elicit whether this meets each of the 4 rules in turn, then – is there evidence of non-random variation? Rules: 1 – yes 2 – no 3 – no (18 useful data points, 7 runs – this is between the upper and lower limits) 4 – no Click to show circle around the shift. Now work on the three charts – process measures. Participants work individually, then compare notes in pairs or small groups Facilitators It is likely that participants will have questions, so after they’ve had a few minutes to get started, be available to support individuals or groups. Debrief using the following slides.

Applying the rules (1) Change
Debrief: elicit whether there is a signal of non random variation for each rule. 1 – no 2 – no 3 – no (8 runs, 15 useful data points – assuming that 3 data points are on the median line) 4 - no

Applying the rules (2) Change 1 – no 2 – no 3 – no 4 – no
Some people might suggest that is an astronomical point. It isn’t really, given that the data points above the median go nearly as far up as this goes down. However, it still may be worth investigating. In this case, do you want the data to go up or down? Presumably down, so it may be helpful to know why the number of jobs cancelled was lower on that date. Some people may think there’s a trend – there are several ‘almost’ trends here, but either they don’t have enough data points (only 4), or the consecutive data points going in one direction are interrupted (on ), by one data point going in the other direction.

Applying the rules (3) Change 1 – yes (click to bring up red circle)
2 – no 3 – no (7 runs, 18 data points – this is between the upper and lower limits) 4 – no

Applying the rules (4) Looking at all these together, we can see the ‘basket of measures’ or ‘family of measures’, with 3 process measures and 1 outcome measure – the outcome measure is titled in blue. Given what we learned by applying the rules to the charts, which is the process that is having an impact on the outcome? (requests providing all required information) – next click highlights this with a circle. There is one signal of non-random variation, so we would want to find out more about this. How were we able to get a higher percentage of requests providing all the required information? What might we need to do next to ensure this process is followed more systematically, that it becomes standard practice? The run chart rules give us information about our variation. That is not where things end. It’s the beginning of understanding our system better, and will help us to embed practices that give us the outcomes we are aiming for.

Baseline data How urgent is a change?
Is it necessary to identify whether the system has any non-random variation before introducing a change? What is the source of historical data? If there is existing data, make use of it. If there is no existing data, decide whether to start collecting data before introducing the change. DISCOVERY Aim Participants can name criteria for deciding whether or not baseline data is required before introducing a change into the system. Key messages Make use of existing or easily collected baseline data Importance of having baseline data to make sense of data after introducing a change. Timing 10 minutes Lead facilitator Why do we need baseline data? - To see if a change is an improvement – for comparison - To find out whether there is any existing non-random variation in the system, and to help us decide if we need to do anything about that What obstacles might there be to getting it? A gatekeeper – finding out the source of the data and getting access to it The urgency of a change – we may need to make a change urgently, and can’t wait until we’ve collected baseline data You can’t make sense of new data after a change without having some data before the change. But in most contexts any change is unlikely to have an immediate impact, so you can often use the first few data points as your baseline. If available, do use baseline data.

Project work: baseline data
Does baseline data exist somewhere? If so, how can you access it? If you are going to collect it, how long will you collect baseline data for before introducing a change? Why? PROJECT Participants spend a few minutes thinking about baseline data in relation to their own project.

Data analysis: Introduction to run charts: summary
Data tells a story Look for signals of non-random variation Rules: Shift Trend Too few or too many runs Astronomical point Baseline data Aim To briefly recap the session content: To support a sense of learning and accomplishment To aid memory of the session later An opportunity for participants to ask any outstanding questions from any part of the session Timing 2 – 5 minutes, depending on time available Lead facilitator Elicit key messages relating to each of the bullets eg Why doesn’t ‘before and after’ data help us to understand our system? What is the minimum number of data points in a shift? Can they cross the median line? (6, no) What is the minimum number of data points in a trend? Can they cross the median line? (5, yes) Too few or too many runs for what? (to be a system with only random variation) What’s the difference between Rule 4 and the other rules? (subjective) Why is it important to have baseline data? If you don’t have any baseline data, do you have to wait until you have collected some before you can introduce a change? (no – depends on context and the urgency of improvement)

Now looking at People issues Deming 2000

Influencing colleagues and other stakeholders
By the end of this session you will be able to: Apply frameworks to identify key stakeholders and gain understanding of their needs Plan for positive influencing using reflection, advocacy and inquiry. Lead facilitator Learning outcomes – read out or ask participants to read. This is about drawing on different tools (that many people find useful) to identify how to influence different people. This will sometimes be related to people’s professional role (eg there’s plenty of evidence that different professional groups have different ways of communicating); it also depends on the individual.

Stakeholder analysis: RACI matrix
Responsible Accountable Consulted Informed DISCOVERY Aim Participants to be able to identify key stakeholders and use the RACI matrix to systematically plan and review allocation of roles Participants who have already used a RACI matrix have the opportunity to say so. Key messages This is a tool that many people find useful. Benefits include: Clarity of roles so less potential for role conflict It can help you ensure that no individual is either overloaded or not included in comparison with others It can help plan communications Timing 20 minutes for RACI matrix section Materials RACI matrix Lead Facilitator You’ve started thinking about the stakeholders in your project, both in terms of who’s doing what in your first test of change, and more generally. The RACI matrix can help you to think about the roles of your stakeholders in more detail.

Title of project, or change idea, or test of change
RACI matrix: template Title of project, or change idea, or test of change Name Task 1 Task 2 Task 3 Task 4 Task 5 Task 6 Lead facilitator Explain how a RACI matrix works. You can use it to analyse your stakeholders at different levels in your project such as: The whole project A change idea A PDSA cycle It’s important that you don’t mix these up in the same matrix. Start by writing in the title of the project/change idea/PDSA cycle Across the top write names of people involved – or if you don’t have names yet, use something more general about the people – this will be a reminder that you need to find someone for that role. You should include anyone with any kind of stake or interest in the project including eg people who are gatekeepers of data you need, not just people who have an active role in carrying out project tasks. Down the first column write the tasks to be carried out. The type of tasks that you include here will vary depending on the level that you are using the matrix at. For example a task at project level may be: communications with service users; a task at PDSA level may be: invite service user reference group to monthly meeting.

Pulmonary Rehabilitation Programme: Patient Education Materials
RACI matrix: example Pulmonary Rehabilitation Programme: Patient Education Materials John Sue Kiran Alex Lyn Anna Write material A R C I Arrange design and print Distribution to GP practices Data collection AR Data analysis Reporting Material RACI matrix Lead facilitator Talk through title, people and tasks Analysis should include: What is the spread of roles/tasks – is one person overburdened? Have you got 2 people doing the same thing? Is that necessary? Helpful? Unhelpful? What else are people doing? Highlight potential for role conflict or role ambiguity. There must be only 1 x A on each row – where the buck stops. Elicit: Does this matrix flag up any possible problems? Eg John and Kiran both Accountable for the same things. This may generate discussion eg who should it be, John or Kiran? Consider also what Kiran is responsible for. If any participants say they have used a RACI matrix before: Elicit how it helped them. PROJECT Now draft your own. Right now do this for your project as a whole. You can expect to continually revise/update, but start here. In debrief – You can also use a RACI matrix for PDSA cycles. With the smallest PDSA cycles you may feel that you don’t need it, but as you bring in more people it will help you to assign tasks fairly and ensure that everyone with an interest is at least consulted or informed. And you need to make sure it doesn’t just sit in a file after you create it. It needs to be communicated.

Planning communications
Stakeholders Purpose of communication Key messages to be communicated Timing of communication How to communicate Who is responsible for communication. DISCOVERY Aim Participants able to use a communications plan to prepare for communications, from the beginning of their project. Key messages Importance of planning communications right from the start The RACI matrix can help you to identify who you need to communicate with. Then use the communications plan matrix to work out how to communicate with each stakeholder, in detail. Timing 15 minutes Lead facilitator Elicit options for each item 1 Stakeholders - by name if known. (In the department, organisation, partner organisations, patients?) does the change have implications across multidisciplinary teams? 2 Purpose of communication (What do you want them to do?) eg patients to answer survey questions, colleagues on team to change their way of working, senior colleague to make resources available (people or funds); other departments or partners to change way of working in relation to a patient pathway 3 Key messages to be communicated (To do this, what do they need to know? What data will be most effective/support your case? This will be different for different people) 4 Timing of communication (Stages of project, specific times, frequency) 5 How are you going to communicate? Formal or informal? (eg presentation, written report, , flyer) 6 Who is responsible? (Who will do the communicating? What do you need them to know?) Develop your first communications plan when you first start planning your project ie when working through the answers to the 3 questions. Issues relating to stakeholders come up in that discussion anyway, so this is a way to formally capture those issues, and not have to start from scratch with this plan later. Review your communications plan periodically eg When starting work on a new change idea When initiating a new PDSA cycle When colleagues leave or join the department in which the project is taking place, or when any of the stakeholders listed in the plan are no longer available. PROJECT Material Communications plan Spend 5 – 10 minutes drafting your communications plan. List stakeholders first, then focus in detail on the first couple of stakeolders. Debrief: how will this help you in your project?

Influencing: Ladder of Inference
DISCOVERY Aim Participants to draw on reflection, advocacy and inquiry strategies to improve communications and working relationships. To be able to identify potential rungs on the ladder that may interfere with effective communications, and adjust communications accordingly. Key messages Tools are available to help you prepare for and take part in potentially difficult conversations. The more often you use them, the easier it will become. Timing 45 mins Material Video Speakers Lead facilitator Elicit: Thinking about your RACI matrix and Communications Plan, do they include any people who you anticipate difficult communications with? Now we’re going to look at a framework for improving our communications so that we can influence people more effectively. We’re going to watch a video that gives a brief outline of the Ladder of Inference framework, and then an example of the ladder in action. Watch the first 2 minutes of the video, which gives an overview of the Ladder. (after overview – pause video on image of driver in car) Elicit: Do you drive a car? What happens when you’re in a busy car park looking for a space? What happens to your stress levels? Watch the video and see which of these things happen to this driver. OR, if it’s been very difficult to elicit ideas from participants: ‘see if what this driver experiences reminds you of a situation that you have been in’ Debrief Pause video at after the woman reaches the top of her ladder, just before the man enters shot. Elicit – compare what they saw happening against their own experiences. We’ve seen what the woman experienced there. What do you think was going on for the man? Elicit briefly. Now view the rest of the video to see which of those ideas were correct here. Video – view to about 5.22, where woman and family pass on the street. Move on to pair discussion on next slide without a debrief at this point.

Your Story Something similar has happened to all of us.
What’s your story? Aim To engage participants through personalisation To generate familiar examples that participants can use later to apply the framework to – only in brief here, as they will return to the same story later Lead facilitator Ie not a driving story, but a story of a failure of communication, talking at cross purposes, misunderstandings. Maybe you found yourself having to apologise afterwards. Or you realised after a conversation had finished what the other person was actually talking about. Lead facilitator might wish to give a brief example of something they experienced. In pairs, max 5 mins for both.

Example Hand hygiene Aim
Participants to have an example of how the Ladder of Inference might apply in the workplace. Lead facilitator Now we’re going to look at a healthcare example.

Example: Hand Hygiene In a corridor near the door to a clinic. A handwash gel dispenser is on the wall by the door. Liz approaches the door. Chris is just beyond the door, along the corridor, holding a clipboard. Liz uses the gel dispenser and rubs a dose of gel into her hands. She steps towards the door. Chris says ‘Excuse me, can I …’. Liz says ‘Sorry, I’ve been bleeped,’ and goes t through the door. Here’s the observable data. (participants to read)

Actions Beliefs Conclusions Assumptions Meanings Data I select
Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions From Liz’s perspective, this is what’s going on

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions Chris is standing in the corridor holding a clipboard. The gel dispenser is on the wall by the door. From this slide on, facilitator read quickly, linking rungs briefly with eg ‘from that data, Liz selects …

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions A clipboard means collecting data. We’re supposed to use the hand gel before going through the door. Chris is standing in the corridor holding a clipboard. The gel dispenser is on the wall by the door.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions It’s an audit on use of handgel. A clipboard means collecting data. We’re supposed to use the hand gel before going through the door. Chris is standing in the corridor holding a clipboard. The gel dispenser is on the wall by the door.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions There’ll be trouble if people don’t use the gel. It’s an audit on use of handgel. A clipboard means collecting data. We’re supposed to use the hand gel before going through the door. Chris is standing in the corridor holding a clipboard. The gel dispenser is on the wall by the door.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions They treat us like children. They don’t trust us to do the right thing. There’ll be trouble if people don’t use the gel. It’s an audit on use of handgel. A clipboard means collecting data. We’re supposed to use the hand gel before going through the door. Chris is standing in the corridor holding a clipboard. The gel dispenser is on the wall by the door.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions I’ll show them. I won’t give anyone a reason to find fault. I’ll use the gel – as usual. I won’t talk to her about it. They treat us like children. They don’t trust us to do the right thing. There’ll be trouble if people don’t use the gel. It’s an audit on use of handgel. A clipboard means collecting data. We’re supposed to use the hand gel before going through the door. Chris is standing in the corridor holding a clipboard. The gel dispenser is on the wall by the door. Facilitator read quickly, linking rungs briefly with eg ‘from that data, Liz selects …

Example: Hand Hygiene In a corridor near the door to a clinic. A handwash gel dispenser is on the wall by the door. Liz approaches the door. Chris is just beyond the door, along the corridor, holding a clipboard. Liz uses the gel dispenser and rubs a dose of gel into her hands. She steps towards the door. Chris says ‘Excuse me, can I …’. Liz says ‘Sorry, I’ve been bleeped,’ and goes t through the door. That was Liz’s ladder. Now, Chris had access to the same observable data. Let’s have a look at Chris’s ladder. (no need to read this slide again, unless participants ask to)

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions From Chris’s perspective, this is what’s going on.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions Liz is approaching the door. She’s using the hand gel.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions Using the hand gel is good practice. Liz is approaching the door. She’s using the hand gel.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions She knows why she uses it. Using the hand gel is good practice. Liz is approaching the door. She’s using the hand gel.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions She can tell me why she uses it. She knows why she uses it. Using the hand gel is good practice. Liz is approaching the door. She’s using the hand gel.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions She won’t mind me asking her about it to help develop ideas for my improvement project. She can tell me why she uses it. She knows why she uses it. Using the hand gel is good practice. Liz is approaching the door. She’s using the hand gel.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions I’ll stop her and go through the survey questions with her. She won’t mind me asking her about it to help develop ideas for my improvement project. She can tell me why she uses it. She knows why she uses it. Using the hand gel is good practice. Liz is approaching the door. She’s using the hand gel.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions Aim Participants to be able to list the rungs on the ladder and explain the principles behind each rung. Lead facilitator The ladder builds rung by rung, without the reflexive loop. Talk through the principles of how the ladder works. As far as possible try to elicit this from participants – facilitator fill any gaps (try to make this reasonably quick – 5 minutes for the whole ladder).

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions In everything we do every day we selectively filter from the observable data. This isn’t entirely a bad thing – if we consciously identified all the sensory data available, we would be completely overwhelmed by it.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions We then add meanings to the data we’ve selected. This is where we start to interpret what the data is telling us. Meanings might be cultural and/or personal. We match the data to familiar patterns, based on our mental models that have developed based on our past experiences. These may include other experiences that we’ve had with this person or with other people in similar situations. We usually have emotions attached to those experiences or models, and these come back to us now.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions We fill in any gaps in the current situation with assumptions based on past experiences, mental models, and responding to the emotions we feel. If we don’t know something, we just make an assumption to fill the gap.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions Based on those assumptions, we draw conclusions. We reach those conclusions using our preferred decision-making approach. For example this may be based on logic, or values, or experience. This is also where we create emotional reactions.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions We adopt beliefs about the world based on our conclusions.

Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions Finally we speak or take other actions based on our beliefs.

Actions Beliefs Conclusions Reflexive Loop Assumptions Meanings
Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions Reflexive Loop Data I select: Why do we select data? In everything we do every day we selectively filter from the observable data. This isn’t entirely a bad thing – if we consciously identified all the sensory data available, we would be completely overwhelmed by it. How do we select data, ie on what basis? (eg past experience with this person, with other people, current salience) Meanings: We then add meanings to the data we’ve selected. This is where we start to interpret what the data is telling us. Meanings might be cultural and/or personal. We match the data to familiar patterns, based on our mental models that have developed based on our past experiences. These may include other experiences that we’ve had with this person or with other people in similar situations. We usually have emotions attached to those experiences or models, and these come back to us now. Assumptions: Why do we make assumptions? (gaps in our knowledge) We fill in any gaps in the current situation with assumptions based on past experiences, mental models, and responding to the emotions we feel. If we don’t know something, we just make an assumption to fill the gap. Conclusions: Based on those assumptions, we draw conclusions. We reach those conclusions using our preferred decision-making approach. For example this may be based on logic, or values, or experience. This is also where we create emotional reactions. Beliefs: We adopt (or adapt) beliefs about the world based on our conclusions. Actions: Finally we speak (or not speak), or take other actions (or not take action) based on our beliefs. Then highlight the reflexive loop – it comes up in two clicks: Our Beliefs affect what Data we select next time, so the ladder is short-circuited; and we ‘leap to conclusions’ without awareness of everything that’s going on in our head in between. The emotions associated with our recent conclusions will feed into future meanings. Finally, add some general issues about the model: Elicit: which rungs are visible to other people? All the rungs take place inside our head, apart from the Observable Data and Action. We don’t usually remember where our deepest attitudes come from, or the meanings that we add, because that information is lost to our memory, after years of leaps of abstraction from bottom to top, reinforced by the reflexive loop. So, to improve the conversations we have, we need to make our own thinking processes more visible, to find out what the differences are in our perceptions and thinking (compared with the person we are having the conversation with), and what we have in common.

Your Own Example What action did you take, or what did you say?
What beliefs was that action based on? What conclusions led you to those beliefs? What assumptions did you make? What meanings did you add? What data might have been available that you selected out? PRACTICE Lead facilitator Return to the same personal examples that you discussed in pairs earlier. Talk through those same experiences again, and work out what was happening at different rungs on the ladder. You may find it helps to start from the top of the ladder and work down. Re Meanings: you may like to consider eg meanings that you took from previous conversations with the same person, or similar conversations with others. Very quick debrief: Which rungs stand out for you as an issue? In your discussion, as you were listening to your partner’s story and helping to understand their own ladder of inference, what did you do? Eg did you challenge them, ask questions, how did you support them in thinking through it?

Actions Beliefs Reflection Conclusions Advocacy Inquiry Reflexive Loop
Observable Data Data I select Meanings Assumptions Conclusions Beliefs Actions Reflexive Loop Reflection Advocacy Inquiry DISCOVERY Key messages Use these three types of strategy in relation to different rungs on the ladder, to improve communications and working relationships. Lead facilitator There are three things we can do to prevent us racing up the ladder in a way that is unhelpful. reflect, as you’ve just been doing From there, you can think about what thoughts and feelings it may be helpful to make more visible. 2. How might it help to reveal parts of your ladder to the other person? What assumptions might they be making about you? What could you tell them about your thoughts and feelings that may help? 3. What would you like to know about in the other person’s ladder? Think particularly about the assumptions that you may tend to make. What would make it unnecessary for you to make those assumptions? Eg finding out about what data they’ve selected, what meanings they’ve added, what conclusions they’ve reached etc PROJECT You’ve identified a range of stakeholders in your project. Some are people who you need to work with you on project activities eg collecting data, testing protocols. Others are people who may be in a position to control access to resources. Think now about the people involved – identify one of these people who you are anticipating having to have a difficult conversation with in order to take your project forward. What rungs on the ladder do you think will be the biggest problem in that conversation? What could you do to improve things? 10 mins to reflect, make notes. Or to discuss and get advice from others. Depending on the mood in the room, and whether participants are more likely to want to chat or reflect, it may be helpful here to designate different areas on the room (or breakout rooms) to be used for reflection/individual work, or for discussion. Debrief: Benefits to you, your colleagues, team members and project.

Influencing colleagues and other stakeholders: summary
RACI matrix Communications plan Ladder of Inference: Reflection Advocacy Inquiry Aim To briefly recap the session content: - to support a sense of learning and accomplishment - to aid memory of the session later An opportunity for participants to ask any outstanding questions from any part of the session. Timing 1 – 5 minutes, depending on time available Lead Facilitator Elicit what content was covered for each of the bullets eg: What does RACI stand for? When should you start drafting your communications plan? What are the rungs on the Ladder of Inference? What are we trying to achieve by using advocacy and inquiry strategies? (make our ladders more visible to others) What does advocacy involve? What does inquiry involve?

Optional Only do this if the mood in the room is reasonably positive. Materials Post it notes – medium sized square Timing 5 minutes Lead Facilitator We started on Day 1 with this Wordle: some of the qualities that may be needed to achieve transformational change. Now we’d like to create a Wordle based on words that you give us. On Day 1 you talked about the strengths you bring to QI. You’ve been with us for nearly 3 days – what new skills do you have, or what skills do you now feel more confident in using? Everyone write down 2 skills on separate post-its – each item a word or short phrase (2-3 words). We’re going to collect them in and create a Wordle to show you [at the end of the day or send later, depending on resources available]

Knowledge sharing Make a note of a particular challenge you are facing in your improvement work ‘Challenge owner’ describe your situation Others give advice, from your own experience Challenge owner note down ideas – action you could take in future based on this advice Speed Consulting Aim Participants to gain confidence in sharing their knowledge, talking about improvement Participants feel empowered being asked for advice and learn from each other Participants clarify their own thinking by articulating responses to other participants’ challenges Key messages Participants have a lot of experience and knowledge that others would value, so share it. You know more than you realise you know. Timing 10 minutes for each round, plus set up and debrief time 5 minutes. Materials None Lead facilitator Everyone jot down a particular challenge that you are facing in your improvement work. We are going to give you an opportunity to ask for advice from your colleagues. Round 1 Arrange participants so they are sitting in groups of 4 – 6. Depending how long they have already been sitting/working with the same group, it may be a good idea to reorganise the groups here. However, if it’s a large cohort, and there will be several rounds, it’s probably better to have people stay where they are for the first round. The ‘challenge owner’ take up to 2 mins to describe the situation Others give advice – based on your own experience (not theory) Challenge owner note down the ideas – document what you would do next time (not what you did before) About 6 – 7 minutes Rearrange the groups (eg by numbering?) and repeat. This time a different person is the challenge-owner. Continue to repeat while there is still energy in the room. It is not necessary to continue until every participant has been challenge owner, as some participants will find that they benefit from advice given to other participants who face similar challenges. Plenary debrief (1-5 minutes): How did it feel being the challenge-owner getting the advice? How did it feel being the advisors?

People Connect People Connect is a social directory for Scotland’s health and social care, enabling users to search online and connect with colleagues from across Scotland who are working in similar areas of interest or who have specific expertise. A tag has been developed specifically for 'Scottish Improvement Skills' so that you can add this to your profile on People Connect in order to identify you as having done the course. You can use this tag to communicate and see your colleagues who are on your cohort, as well as people on future cohorts. Material There is a separate short guide on how to set yourself up on People Connect and how to add the 'Scottish Improvement Skills' tag to your profile.

People Connect

Project charter A summary of what you expect to achieve from the improvement project Helps to maintain focus Answers the 3 questions Identifies appropriate team members Practice Stage 2 (after all relevant tools and techniques have been introduced) Aim To individually review workshop content and raise any questions needed to be able to prepare charter. Key messages Participants now have the knowledge and skills to prepare a project charter for their workplace improvement project. Timing 5 – 10 mins Materials – as before Project charter checklist Project charter example: staff wellness (this relates to one of the 4 case studies that are developed on the wall through the workshop) Lead facilitator Have you ticked all items in the checklist? Spend a few minutes (time permitting) to skim through folder/learning materials, recapping what’s been covered, make sure you can find materials relating to all items on the checklist, and jot down issues to work on/develop to complete your project charter once back at work. Raise any questions now about what we’re asking you to do. Optional activity 1 Read through the example Project Charter in detail, and write the numbers on the checklist next to the lines in the example that match that item. Participants work individually, then discuss in pairs to fill any gaps. Plenary debrief – any issues arising from this? (it is not necessary to go through each item). Optional activity 2 Participants find all the project materials they have developed: tick off on the checklist what they have completed, and write the checklist number on the material itself. Note any items not yet ready to include in a project charter. NB It is likely that most participants will need to revise all their project materials, working with relevant project team members. Assume that is the case for all items on the checklist. What is ‘complete’ in this case means what they feel ready to discuss with team members.

Learning Journal Reflections on learning 10 - 15 minutes Aim
See all below Materials Will be sent by next week Lead facilitator For this workshop it may take a little longer, given the 3 days of learning. After the one day workshops and webex will take less time. Guidelines for completing your Learning Journal Studies have shown that reflection upon your learning is key to a full learning experience. For this reason, we are requesting you to keep reflective journals as part of your professional development. This document contains five separate journal sections: one for each of the workshops and the online learning events. Please complete the appropriate section in the days following each session. For Workshop 1, complete one section for the whole workshop, not one per day. How long will it take? As a rough guide, each journal entry should take approximately 5-10 minutes. You may take more or less time depending upon your time constraints and the amount of detailed information you wish to include. Feel free to add comments but the minimum requirements are included in the template. What should I write? We are trying to access your experiences and thoughts during and after the workshops. Don’t worry if you discover your answers overlap or if you feel one question has already been answered in response to another. Try to write something, no matter how brief your response may be to each question. If you find that you have nothing to comment on in certain sections leave it blank. But could this be telling you something important about your learning and application of your learning? You are not limited to space provided in the template, each section expands to accommodate different amounts of information. When do I submit them? The Learning Journal should be submitted within one week of the end of the session, to Confidentiality All information completed in your Learning Journals is strictly confidential. It is used only by the Course Facilitator to evaluate the implementation of the course. There is no requirement to identify yourself personally if you choose not to (but it is helpful to us). If you have any questions or concerns about your Learning Journal, please don’t hesitate to discuss this with one of the course facilitators.

Workplace learning Online learning modules and videos
Ongoing project work Online learning events Project charter Materials Workplace learning See handout for details of online modules and videos In the next few days you will receive an from us with soft copies of the templates you’ve been using.

Project work By the time your return for Workshop 2, you will have:
Completed your project charter Collected data to help you identify your priority for change Collected baseline data Carried out several tests of change Collected qualitative and quantitative data

References and further resources
Provost Lloyd P & Murray S (2011) The Health Care Data Guide: Learning from Data for Improvement Jossey-Bass Senge, Peter M, 1994 The Fifth Discipline Fieldbook: Strategies for Building a Learning Organisation Nicholas Brealey Publishing Point out that the Healthcare Data Guide includes the table for run chart Rule 3 – a much bigger one that goes to fewer and more data points.

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