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## Patient Flow Collaborative Learning Session 3

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**Patient Flow Collaborative Learning Session 3**Welcome 8th February, 2005 Level 12 Conference room , 555 Collins Street, Melbourne**Patient Flow Collaborative Learning Session 3**Rochelle Condon Service Improvement Lead Patient Flow Collaborative 8th February, 2005**Welcome**• Dedicated day for Project Coordinators and Data Analysts • Sessions on; Measurement for Access Bed Management**Housekeeping**• Mobile phones/pagers to silent/vibrate • Rest rooms • Fire alarms and exits**Housekeeping**• Work in partnership – no one knows all the answers • Support people – Clinical Innovations Team**Agenda**MEASUREMENT FOR ACCESS 9.10 – 9.30 Statistical Process Control Charts Prue Beams 9.30 – 9.45 Program Measure Interpretation Prue Beams 9.45 – 10.30 Measurement for improvement Prue Beams and performance - Southern Health WIES Management System - HDM Exception report - Sameday Surgery Basket**Agenda**MEASUREMENT FOR ACCESS 10.30 – 10.45 Morning Tea 10.45 – 11.30 Capacity and Demand Prue Beams - Variation Mgmt Case Study and - Templating Bernadette - Elective Information Systems McDonald 11.30 – 12.00 Discussion Prue Beams 12.00 – 12.45 Lunch**Agenda**BED MANAGEMENT 12.45 – 1.30 Bed Management Trevor Rixon - Victorian Programs 1.30 – 2.15 Bed Management Penny Pereira - UK Programs 2.15 – 2.30 Afternoon Tea 2.30 – 3.15 Discussion on Bed Penny Pereira Management Innovations and Trevor Rixon 3.15 – 3.20 Next Steps and Close Rochelle Condon**Learning Session 3**Measurement for Access Prue Beams – Data Consultant**Setting the Scene…**Sustainability • PFC data support will cease Jul05 • What is the plan for your organisation at this time? • Health services need to internalise this type of analysis so process improvements can continue to be measured • Making it Mainstream • Supply resource information for future reference and create networks**Setting the Scene…**Measurement for Improvement and Performance • What data do we need to identify and measure process improvements? • What data do we need to assist us in our performance management?**Setting the Scene…**Capacity and Demand • What data do we need to identify the variation in our processes? • What data do we need to help us match capacity to demand?**Statistical Process Control Charts**Revisiting what we have learnt**Outcomes from this session**• You will: • Have reinforced your understanding of the two types of variation • Be able to construct and interpret a simple SPC (XmR) chart • Know when to recalculate its process limits • Have planned your next steps in continuing the use of SPC analysis in your organisation post Jul05**So what are we going to cover?**• A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Managing variation using SPC • Available tools and references**x**x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Variation is inherent in all processes BETTER x x WORSE**Existing reports**Traditional ways of reporting performance ignore or seek to filter out this variation**Where have we come from?**• Compare to some arbitrary fixed point in the past • the average (median) waiting time of those on the list, at 2.97 months, fell slightly over the month, and remains lower than at March 1997 (3.04 months). • Show percentage change this month and to some arbitrary fixed point in the past • the number of over 12 month waiters fell this month by 3,800 (7.4%) to 48,100, and are now 24,000 (33%) below the peak at June 1998**Every picture tells a story . . . Does it!?!**Looks pretty – but what is it telling us?**Variation comes in 2 flavours**• Some processes display controlled variation (common cause) • stable, consistent and predictable • inherent in the process • While others display uncontrolled variation (special cause) • pattern changes over time • special cause variation/“assignable” causes • How do we know which is which?**Identifying Controlled Variation…**• Stable, consistent pattern of variation • “Chance” / constant causes**and here?**What happened here? Identifying Uncontrolled Variation… • Pattern changes over time • “Assignable” / special causes**Common Cause Variation**What type of variation is present in each of these pumpkins?**Special Cause Variation**How about this one?**Special Cause warning…**Two dangers to beware of: • Reacting to special cause variation by changing the process • Ignoring special cause variation by assuming “its part of the process”**So what are we going to cover?**• A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Available tools and references**The SPC (XmR) chart**• XmR stands for X moving Range • The ‘X’ represents the data from the process we are monitoring • eg number of delayed discharges, % cancelled operations • The moving Range describes the way in which we measure the variation in the process**A typical SPC (XmR) chart**Range Upper process limit Mean Lower process limit**What Can SPC Do For Me?**• Shows just how much variation is normal • Helps forecast performance • Indicates whether process can meet targets • Shows how to intervene in a process to improve it • Identifies if a process is sustainable • Identifies when an implemented improvement has changed a process • and it has not just occurred by chance • Reduces data overload**So what are we going to cover?**• A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Managing variation using SPC • Available tools and references**Constructing the chart…**There are 5 steps to creating your chart: • Plot the individual values • Derive the moving range values • Calculate the mean (X) and plot it • Calculate the average moving range (R) • Derive upper and lower limits from this and plot them**Example data set…**• Table 1 is an example of what the data should look like • Table 2 is an example of what the formula should look like • Average, Lower limit and Upper limit should only have the formula in the first row and the value pasted for the entire dataset.**Some points to note…**• The chart is designed to be applied to one process • A minimum of 21 data points is required • The moving range describes the way in which we measure the variation in the process • The difference in the Moving Range is always positive • Deriving the process limits • Calculate limits as mean + 3 sigma**So what are we going to cover?**• A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Managing variation using SPC • Available tools and references**Rules for Special Causes…**Rule 1 • Any point outside of the control limits Rule 2 • A run of 7 points all above or below the centre line, or • A run of 7 points all increasing or decreasing Rule 3 • Any unusual patterns or trends within the control limits Rule 4 • The number of points within the middle third of the region between the control limits differs markedly from two-thirds of the total number of points**Point above the Upper Limit**Point below the Upper Limit Special Causes – Rule 1 Rule 1 • Any point outside of the control limits**7 points above the line**7 points below the line Special Causes – Rule 2 Rule 2 • A run of 7 points all above or below the centre line**7 points in an upward direction**7 points in an downward direction Special Causes – Rule 2 Rule 2 • A run of 7 points all increasing or decreasing**Cyclic pattern**Trend pattern Special Causes – Rule 3 Rule 3 • Any unusual patterns or trends within the control limits**Considerably less than 2/3 of the points fall in this zone**Considerably more than 2/3 of the points fall in this zone Special Causes – Rule 4 Rule 4 • The number of points within the middle third of the region between the control limits differs markedly from two-thirds of the total number of points**So what are we going to cover?**• A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Managing variation using SPC • Available tools and references**When to change the limits…**If you can answer yes to all of these questions: • When one of the 4 rules has been broken • Have you seen the process change significantly – i.e. is there an assignable (special) cause present? • Do you understand the cause for the change in the process? • Do you have reason to believe that the cause will remain in the process? • Have you observed the changed process long enough to determine if newly-calculated limits will appropriately reflect the behaviour of the process?**Significant points above the mean, these are now used to**recalculate the limits Start of process change If you can answer Yes…change limits