allied healthcare professions service improvement projects regional event turning data into...
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Allied Healthcare Professions Service Improvement Projects
Regional Event
Turning Data Into Knowledge Resource Pack
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Company Confidential
Aims of the session
• understand how data is vital to the service improvement process
• introduce principles of measurement in relation to project outcomes
• using information for action and decision making
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Company Confidential
Information for action
Good information is the foundation of good decision-making in every
aspect of healthcare
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Company Confidential
Traditional NHS information
• target driven, standard based
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Company Confidential
From Data to Knowledge – A Continuous Process
Subjective and judgmental
Objective and analytical
Data
Information
Intelligence
Knowledge
Insight
Process Driven – ‘Black Box’
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Company Confidential
Layers of information
Information
Intelligence
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Company Confidential
Model for improvement
Testing ChangesThe Plan-Do-Study-Act (PDSA) cycle is shorthand for testing a change in the real work setting — by planning it, trying it, observing the results, and acting on what is learned. This is the scientific method used for action-oriented learning
Setting Aims Baseline your current state and set aims. The aim should be time-specific and measurable; it should also define thespecific population of patients that will be affected
Establishing MeasuresUse quantitative measures to determine if a specific change actually leads to an improvement
Selecting ChangesAll improvement requires making changes, but not all changesresult in improvement. Organizations therefore must identify thechanges that are most likely to result in improvement
Institute for Healthcare Improvement
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Company Confidential
Why aims?
• to provide a clear focus for what we are trying to achieve
• to prevent confusion arising between what we are trying to achieve
and how we are attempting to achieve it
• to enable us to be clear about whether we have achieved on what
we have set out to achieve
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Company Confidential
An effective aim statement is:
• Specific– covers the who, when and how it will be achieved
• Measurable– stated clearly written with numerical measures and stretched
targets that are not achievable with the current system• Agreed upon
– agreement by all members of the team that the end result is desirable and achievable, and supported by clinical and managerial leaders
• Realistic and relevant– is practical about what can be achieved within the time available
• Time bound– it is clear about the time-scales for delivery within the
collaborative programme
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Company Confidential
Why measure?
• measurements for judgment
– where measures are used to judge against performance
targets, other Trusts, etc
• measurements for diagnosis
– where measured are used to understand the process, see if
there is a problem and how big it is – useful early on in your
project
• measurements for improvement
– where a few specific measures linked to strategic and project
aims, demonstrate over time whether changes are making
improvements
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Company Confidential
Common mistakes
• compare this year to last year or current
performance to some arbitrary fixed past value
• using averages does not tell you about
variation
• collecting data because it always has been
collected
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Averages as a performance measure
Trust Performance
(mins)
Target
(mins)
Trust A 40.8 40
Trust B 35.95 40
Trust C 39.1 40
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0
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Trust B
Measurement over time
Trust C
Trust A
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Walter’s golden rules
• data should always be presented in a way that preserves the evidence.
• displaying data using averages and aggregates loses the richness of the individual data points.
• display the individual data points (in the NHS these are often individual patients), then provides analysis to interpret what the user sees
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Company Confidential
0102030405060708090
Day
1 4 7 10 13 16 19
Seco
nds
to
answ
er p
hon
e
Average based on first 10 days
Eight one side
Five down (or up)
Change
1st step – plot the dots
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Time
Ob
served valu
e
Statistical Process Control chart
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Why use SPC
• focus attention on detecting and monitoring process variation over
time
• distinguish special causes from common causes of variation, as a
guide to local action
• identify where real change has taken place in a process
• encourage continuous improvement
• understand capability of process to meet targets
• help engage clinicians/health care professionals
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Company Confidential
Why focus on variation
• there is variation in every process• variation creates uncertainty• there are different types of variation• there are different ways of managing variation• we need to understand causes of variation to take action to reduce
it• introducing standardised processes helps to improve quality
• the root cause of delays for patients in the care system often
variability, not volume
• the greater the variability, the more capacity we need to meet
demand
• we create the variability through the way we organise our systems
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Active wait in weeksConsultant B - Routine Inpatients
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10
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60
70
80
90
1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145
Patients admitted January to December 2004
Wee
ks w
aite
d
Patients Average (36) LCL (20)
UCL (53) 9 month target
The erratic pattern of dots on this graph is caused by wide variation in weeks waited by consecutively admitted patients. This illustrates that patients are not being seen in
turn.
The erratic pattern of dots on this graph is caused by wide variation in weeks waited by consecutively admitted patients. This illustrates that patients are not being seen in
turn.
The tight clustering of dots around the middle red line in this graph indicates minimal variation in weeks waited by consecutively admitted patients. This illustrates
that patients are being seen in turn.
The tight clustering of dots around the middle red line in this graph indicates minimal variation in weeks waited by consecutively admitted patients. This illustrates
that patients are being seen in turn.
Active wait in weeksConsultant A - Routine Inpatient
0
10
20
30
40
50
60
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115
Patients admitted January to December 2004
Wee
ks w
aite
d
Patients Average (15) LCL (0)
UCL (47) 9 month target
Variability in waiting list management
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Common cause or special cause variation
• common cause variation – predictable, consistent pattern of variation over time due to
constant causes– variation is inherent in a process– eg patterns in the data such as weekend or evening effects, or
peaks and troughs in demand
• special cause variation– unpredictable, inconsistent pattern of variation over time, can
be attributed to specific events– variation is unexpected in a process– eg an RTA, member of staff off sick, equipment
failure/maintenance
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“A phenomenon will be said to be controlled when, through the use of past
experience, we can predict, at least within limits, how the phenomenon may
be expected to vary in the future”
Shewart - Economic Control of Quality of Manufactured Product, 1931
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Turning data into intelligence - summary
• traditional measurement• understanding measurement to improve
systems• a model for - setting aims - defining measurement systems - showing improvement (achieving outcomes)• measuring, understanding and managing
variation• pitfalls to avoid
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Finally
Understanding variation is the key to managing chaos
Walter Shewart (1930)
