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Transforming End of Life Care in Acute Hospitals PM Workshop 3: Vital Signs ‘Making Measurement Better’ How well things are going and how to make it better’
The document discusses the transformation of end-of-life care in acute hospitals through effective measurement and improvement strategies. It highlights the significance of meaningful data collection, the use of various measurement traditions, and focuses on prioritizing critical issues to enhance patient care. Additionally, it outlines a structured approach to measurement, including the use of driver diagrams and the model for improvement to facilitate change in healthcare settings.
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Transforming End of Life Care in Acute Hospitals PM Workshop 3: Vital Signs ‘Making Measurement Better’ How well things are going and how to make it better’
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- 3. Insights into • Importance of measurement •Traditions of measurement • Knowing How We are Doing • Pareto principle • Structure ‐ process ‐ outcome measures • Driver diagrams • Model for improvement • Tracking variation • 7 steps to measurement
- 7. Hitting the targetbut missing the point!
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- 10. “I can makethe last stage of my life as good as possible because everyone works together confidently, honestly and consistently to help me and the people who are important to me, including my carer(s).”
- 11. The traditions ofmeasurement • eg A‐B comparison, average, huge dataset Research • eg one‐to‐many benchmarking comparision, average, large dataset Judgement • eg continual analysis of single changing process over time Improvement
- 12. Research Judgement Improvement Goal New knowledge (not its applicability) Comparison Reward / punishment Spur for change Processunderstanding Evaluating a change Hypothesis Fixed None Multiple and flexible Measures Many Very few Few Time period Long, past Long, past Short, current Sample Large Large Small Confounders Measure or control Describe and try to measure Consider but rarely measured Risks in improvement settings Ignores time based variation Over‐engineers data collection Ignores time based variation Over‐reaction to natural variation Incorrectly perceived as ‘inferior statistics’ Measurement mindsets Based on L Solberg, G Mosser and S McDonald (1997) The Three Faces of Performance Measurement: Improvement, Accountability and Research, Journal on Quality Improvement, 23 (3): 135 ‐ 147.
- 13. What mind setsare at play here? Research Improvement Judgement
- 15. What do wecollect now?
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- 20. What does itlook like? Interruptions in surgeries Tally by GPs of the causes of interruptions while with a patient. Category Count Sign script ‐ contraception 72 Sign script ‐ minor illness nurse 18 Clinical query ‐ learner 18 Clinical query ‐ NP 24 Sign script ‐ urgent 78 Chaperone 198 Equipment search 60 Admin info 312 Clinical query ‐ GP 66 Panic button 6 Cancellation msg 588 Other 72 TOTALS 1512 0 100 200 300 400 500 600 700 Count
- 21. What does itlook like? Interruptions in surgeries Tally by GPs of the causes of interruptions while seeing patients. Category Count % of Total Cancellation msg 588 38.9 Admin info 312 20.6 Chaperone 198 13.1 Sign script ‐ urgent 78 5.2 Other 72 4.8 Sign script ‐ contraception 72 4.8 Clinical query ‐ GP 66 4.4 Equipment search 60 4 Clinical query ‐ NP 24 1.6 Clinical query ‐ learner 18 1.2 Sign script ‐ minor illness nurse 18 1.2 Panic button 6 0.4 TOTALS 1512 100 0 5 10 15 20 25 30 35 40 45 % of Total
- 22. What does itlook like? 0 10 20 30 40 50 60 70 80 90 100 0 5 10 15 20 25 30 35 40 45 % of Total Cumulative %
- 23. What does itlook like? 0 10 20 30 40 50 60 70 80 90 100 0 5 10 15 20 25 30 35 40 45 % of Total Cumulative % Three categories of interruption (17%) account for 73% of the problem
- 24. What to measure? StructureProcess Outcome Avedis Donabedian ‘Outcomes remain the ultimate validators of the effectiveness and quality of medical care’ but they ‘must be used with discrimination’ The environment in which care occurs What care is delivered, and how The impact on patients and the population
- 25. What to measure? StructureProcess Outcome o Outcomes are a worthy goal o All have pros & cons o Should measure a selection of all three Veena Ralegh
- 26. What to measure? StructureProcess Outcome “Intermediate outcomes” • a common solution • properties of both process & outcome • but be careful to acknowledge it’s not ‘the ultimate outcome’
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- 28. Metrics for differentaudiences Board Service managers Project managers Frontline staff Focus on outcome Focus on process Relevant process + outcome measures Relevant process + outcome measures Higher level outcome measures Highest level outcome measures
- 29. Driver Diagrams Benefits ... build complex strategy immediate visual Different scenarios... 1. Help generate change ideas 2. Marshall a mass of change ideas 3. Survive failure / the unexpected avoid “silver bullet” thinking highlight overlooked ideas
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- 31. AIM – an improved system Primary driver 1 Primary driver 2 How much? By when? Secondary driver 1 Secondary driver 2 Secondary driver 3 Secondary driver 4 Secondary driver 5 CP1 CP2 CP3 CP4 CP5 CP6 CP7 How much? By when? How much? By when? AIM PRIMARY DRIVERS SECONDARY DRIVERS CHANGE PROJECTS Driver Diagrams Planning& presenting change CauseEffect Drives
- 32. Driver Diagrams Weight loss example Pedometer Gym work out3 days Squash weekends No pub weekdays Take packed lunch Low fat meals Buy only 1 sandwich Water bottle for work bag Fruit for dessert Put away the large glasses Put cycling days in diary Cycling kit out night before Get rid of Oyster cardBe more active during the day Do sport Drink less alcohol Substitute lower calorie foods Eat lessReduce calories in Increase calories out Take stairs 2 stone weight loss in 6/12
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- 40. What to measure? Addmetrics to your driver diagram Structure Process Outcome Balance How much? By when? What is our baseline? How do we get it? 1. Identify measures for your aim, a primary driver and a secondary driver using each of these four prompts 2. For each measure record your answer to these two questions 3. For each measure record your answer to these two questions
- 41. How much &how often? There is no precise science to guide decisions about how many metrics to use, or how often… How many different things are you monitoring consciously? How frequently? Straight motorwayReversing round a corner
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- 43. Meeting rooms Real world Approve Design Design DesignDesign Implement ! Courtesy of Patient Safety First Opinion
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- 45. Change through smallsteps Change ... • with a clear purpose • you can learn from (without fear of failure) • which is less exhausting • with fewer unintended consequences • which builds engagement and optimism
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- 50. “We’re pressed fortime, so we’ll be jumping to conclusions” We’re pressed for time, so we’ll be jumping to conclusions
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- 53. How do youlook at your data ?
- 54. “If I stickmy right foot in a bucket of boiling water and my left foot in a bucket of ice water, on the average, I’m pretty comfortable.” The Problem with Averages
- 55. The Problem Aggregated datapresented in tabular formats or with summary statistics, will not help you measure the impact of improvement efforts. Aggregated data can only lead to judgment, not to improvement.
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- 57. Did things improve? Whatwill happen next? Should we do something? Smoking Cessation :Percentage of smokers who have quit smoking after 4 week programme INTERVENTION
- 58. 1C-58 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 10/3/2007 10/17/2007 10/31/2007 11/14/2007 11/28/2007 12/12/2007 12/26/2007 1/9/2008 1/23/2008 2/6/2008 2/20/2008 3/5/2008 3/19/2008 Change made here Did things improve? Whatwill happen next? Should we do something? Smoking Cessation :Percentage of smokers who have quit smoking after 4 week programme
- 59. Protocol introduced Letter from Clinical Director Pharmacy included % surgical patientsreceiving Prophylactic Antibiotics 0 10 20 30 40 50 60 70 80 90 100 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 %reconciled Run Chart
- 60. Waiting time results 70 35 0 10 20 30 40 50 60 70 80 Avg Before Change AvgAfter Change WaitTime(min.)
- 61. Waiting time results0 10 20 30 40 50 60 70 80 90 100 date Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Change Made CycleTime(min.) 0 10 20 30 40 50 60 70 80 90 100 date Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Change Made CycleTime(min.) Unit 1 Unit 3 0 10 20 30 40 50 60 70 80 90 100 date Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Change Made CycleTime(min.) Unit 270 35 0 10 20 30 40 50 60 70 80 Avg Before Change Avg After Change WaitTime(min.)
- 62. If We Have3 Numbers in Sequence: How Do You Report These? Downward Trend Upward Trend Some Recovery Setback Collapse Dramatic Recovery Each pattern has an equal 1:6 chance Lets Get Scientific! Data has no meaning without a Context
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- 64. 0 20 40 60 80 100 120 Consecutive trips Min. My TripTo Work Mean Common Cause Special Cause
- 65. Statistical Process Control (SPC)Charts: No of Admissions 0 200 150 100 50 250 Performance Report – Number of Admissions Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 No Admissions Median Lower Limit (66.5) Upper Limit (222.4) 65
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- 67. 2 Ways ToImprove A Process If controlled variation (Common Cause) • process is stable • variation is inherent to process • therefore, process must be changed i.e. Redesign If uncontrolled variation (Special Cause) • process is unstable • variation is extrinsic to process • cause should be identified and “treated”
- 68. 7 steps tomeasurement https://www.youtube.com/watch?v=Za1o77jAnbw&list=PL_V1d0Y94nv4u2yCCDnApxa 9ykKmSG1oE
- 69. Insights into • Importance of measurement •Traditions of measurement • Knowing How We are Doing • Pareto principle • Structure ‐ process ‐ outcome measures • Driver diagrams • Model for improvement • Tracking variation • 7 steps to measurement
- 70. What do youneed from your data?