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Lean Process Improvements for Quality Reporting
TASC 90 Webinar
August 13, 2012
Melissa P. Lin, MS, LSSBB, CPHQ
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No Shortage of Quality Reporting Requirements
• Medicare Beneficiary Quality Improvement Project (MBQIP)
• Partnership for Patients (P4P) Hospital Engagement Networks (HENs)
• 10th Scope of Work (SOW) - Integrating Care for Populations and Communities
• Healthcare-associated Infections (HAIs) via CDC
• Meaningful Use of Electronic Health Records Clinical Quality Measures
• Other state quality reporting initiatives
• Third-party reporting group initiatives
• Individual organization’s quality reporting initiatives
3
Data, data everywhere…
• How do we keep track of it all?
• How do we know we’re collecting it…
– Correctly?
– Timely?
• What does it tell us?
• How do we act based on the results?
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What is Lean?
• “Lean” coined by Massachusetts Institute of Technology
– Originated from Toyota
• A philosophy that is focused on:
– Defining value in the eyes of the patients
– Eliminating wasteful steps that add no value to the organization
– Creating flexibility and agility to meet the changing needs of the patient and the industry
– Empowering frontline by incorporating easy problem-solving tools to use daily
– Do more with less
This entire philosophy is driven by data!
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Data drives Lean.
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Best Safety
Highest Quality
Shortest Lead Time
Lowest Cost
Highest Employee
Morale
LEAN
6
How can Lean improve your hospital?
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Data, information and facts
Measurements used for reasoning, calculations or discussion
Gathering and analyzing data leads to information
Data Knowledge obtained from the investigation, study or analysis of data
Information leads to the identification of facts
Information A piece of information presented as having objective reality
Fact
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Continue improving
measurement consistency
Begin data collection
Plan for data consistency &
stability
Develop operational definitions & procedures
Clarify data collection
goals
What Data Should I Get?
• What questions are you trying to answer? • Who on the team is collecting data? • What data does the team need to answer the questions? • How will the data help
answer these questions? Meet customer needs? Improve operations?
• Is the needed data already available? If not, what data must be collected?
• How much data does the team need? Collected over what time period?
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Who Owns the Data?
• The people living and breathing the process
– E.g. The person(s) responsible for discharging AMI patients with statin prescribed
• Data stewardship
– Being responsible for data that’s worth caring for and preserving
• Data quality is a team sport
– So is process improvement
• Process owners are responsible and accountable for the quality and simplicity of data that represents and enables their processes
• Data measurements can be both formal metrics from reporting indicators and informal measures the team finds vital to the improvement process that supports a formal reporting indicator
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Formal example
• Central line-associated bloodstream infection rate
Informal examples
• Percentage of ICU patients with central lines who receive all 5 elements of the central line bundle
What Data do We Want Measured?
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Clarify Data Collection Goals
• What is the concept the team is trying to evaluate?
– Examples: length of time, volume, ease of use, uniformity, completeness, defects
• What data will allow the team to attach a value to this concept?
Continue improving
measurement consistency
Begin data collection
Plan for data consistency &
stability
Develop operational definitions & procedures
Clarify data collection
goals
12
Clarify Data Collection Goals
• Is the operational definition clear?
– Examples: Does “on-time” mean within five minutes? Two hours? Three days?
• What is your plan for collecting the data? − What procedures will be used to make the collection
precise and consistent? − How will the data be recorded?
Continue improving
measurement consistency
Begin data collection
Plan for data consistency &
stability
Develop operational definitions & procedures
Clarify data collection
goals
13
• Data is consistent if any two people who measure the same things arrive at essentially the same answer
• Data is stable when the results do not show signs of special causes of variation over time
– Will my data show whether the process is stable or unstable?
Collecting Meaningful Data
Continue improving
measurement consistency
Begin data collection
Plan for data consistency &
stability
Develop operational definitions & procedures
Clarify data collection
goals
Continue improving
measurement consistency
Begin data collection
Plan for data consistency &
stability
Develop operational definitions & procedures
Clarify data collection
goals
14
• Explain the procedures to all data collectors • Practice makes perfect!
− Have everyone practice using the forms and/or the procedures before “real” data collection begins
• Have someone who knows what to do watch and instruct beginning data collectors
− Daily huddles to review a test of change is a perfect time to check in with data collectors
Begin Data Collection
Continue improving
measurement consistency
Begin data collection
Plan for data consistency &
stability
Develop operational definitions & procedures
Clarify data collection
goals
15
• Are measurements stable? • Are measurements consistent? • Analyze the data
− What was the answer to our question(s)? − Does it show us what we need to improve?
Continue Improving Measurement Consistency
Continue improving
measurement consistency
Begin data collection
Plan for data consistency &
stability
Develop operational definitions & procedures
Clarify data collection
goals
16
Presenting Data for Analysis
Adapted from NHS Improvement Network
Number of Patients Treated per Week
1 2 3 4 5 6 7 8 9 10
216 239 233 226 288 227 238 228 287 229
11 12 13 14 15 16 17 18 19 20
242 233 286 228 239 232 237 285 232 235
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Presenting Data for Analysis
200
210
220
230
240
250
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Pat
ien
ts T
reat
ed
Weeks
Adapted from NHS Improvement Network
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Presenting Data for Analysis
Source: Carey, RG. Improving Healthcare with Control Charts. Milwaukee, WI: ASQ Quality Press, 2003.
• Average CABG mortality before and after implementation of new protocol. Did the improvement reduce mortality rate?
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
2008 2009
Intervention begins in Jan 2009
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0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
Jan
-98
Feb
Mar
apr
May Jun
Jul
Au
g
Sep
Oct
No
v
Dec
Jan
-99
Feb
Mar
Ap
r
May Jun
Jul
Au
g
Sep
Oct
No
v
Dec
Presenting Data for Analysis
• Average CABG mortality before and after implementation of new protocol.
• Decreased mortality was not due to the new protocol which may have had an unintended negative effect
Intervention begins in Jan 2009
2008 Avg. = 5%
2009 Avg. = 4%
Median 4.5%
Source: Carey, RG. Improving Healthcare with Control Charts. Milwaukee, WI: ASQ Quality Press, 2003.
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Displaying Data: The Run Chart
• Line graphs that plot any type of data over time
• Help you see trends
• Help you determine when a change might have occurred
– Common cause vs. special cause variation
• Requires paper and pencil, no statistics!
Me
asu
red
Ou
ptu
t
Time
Run Forrest RUN!
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• Monitor progress and display data for staff members
– Data Wall
– Keep it simple – one graph, one message
– Report regularly to Improvement Team
• We are what we repeatedly do, make improvement a regular do!
• Check in from time to time to ensure that the metric is still useful or the benchmark is still ambitious enough
– Do we need to 5S our metrics?
Excellence is not one act, it is a habit. - Aristotle
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5S – A Lean Tool
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SUSTAIN
• Discipline
• Established procedures become habit
SORT
• Organize
• Eliminate waste
STRAIGHTEN
• Everything has a place
• Keep everything in its place
SHINE
• Cleanliness
• Keep clean and tidy
STANDARDIZE
• Adherence
• Maintain the first three pillars
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The Results of 5S
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Value
Value Stream Map
Eliminate Waste
Flow
Strive for Perfection
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Five Principles of Lean
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Final Thoughts
• The five major steps of data collection can lead you to useful information and decision-making
• Good data collection can be ruined by bad data analysis
• Data doesn’t belong to just one person, it belongs to everyone involved in the process it measures
• Without baseline data analysis, how do we know that a change is an improvement?
– Improvement of the process (Lean) and the underlying data must go hand-in-hand
• A useful metric today can become a useless metric tomorrow
– Check metrics from time to time
“Data are like garbage. We need to know what we are going to do with the data before we actually collect them.” – M. Twain
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Melissa P. Lin, MS, LSSBB, CPHQ
Consultant
207-221-8273 (O)
mlin@stroudwater.com
Any Questions?
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