Re-Design of a
Pre-Admission Facility
Interactive Quality Improvement Workshop
Richard Bowry, MD
Antoine Pronovost, MD
Patricia Houston, MD
June 18, 2012
St. Michael’s Hospital
Outline
1.
Introduction to DMAIC methodology
–
2.
Key concepts and facilitated discussion
–
3.
4.
5.
6.
7.
stem 3
Root cause analysis didactic session
Facilitated discussion: leading change… “what went wrong”
–
9.
stem 2
Process mapping exercise
Quantitative analysis, facilitated discussion
Quantitative analysis, group work
–
8.
Case study stem 1
stem 4
Didactic session: key success factors for implementing and monitoring
change
Conclusion
June 18, 2012
St. Michael’s Hospital
Disclosures
• Dr Richard Bowry
– No disclosure
• Dr. Patricia Houston
– No disclosure
• Dr Antoine Pronovost
– Has received funding from the government of Ontario
to study and improve Pre-admission facility
processes.
June 18, 2012
St. Michael’s Hospital
Objectives
• You will understand how to apply Quality
Improvement techniques to the complex
problem of redesigning a PAF
• You will become familiar with the five
stages of DMAIC
• You will become familiar with the key
principles of successful change
management
June 18, 2012
St. Michael’s Hospital
Limitations and Caveats
• We will not be providing you with a “cookbook” answer for fixing problems in your
own PAF
– Solutions take teamwork, planning and local
insights to work
• The case study is loosely based on actual
experience, but has been heavily adapted
for the purpose of this session
June 18, 2012
Introduction to DMAIC
St. Michael’s Hospital
DMAIC - Define
• Reasons for action?
• What are our targets?
• What is within our control?
• All members need to agree on the problem
• Create a purpose statement – rationale,
scope and targets
• Start an A3 style grid to monitor progress
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Define - A3
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DMAIC - Measure
• What is our baseline?
• Acknowledge our own variation / trends?
• What happens 80% of the time?
• Root cause analysis
• Prioritization matrix
• Cause Effect Diagram
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Prioritization Grid
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Cause-Effect Diagram
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DMAIC - Analyze
•
•
•
•
What does our current state look like?
Are there any wasted steps in what we do?
How would a patient experience this?
What are the root causes?
• Process mapping to identify NVA steps
• Holistic approach looking at all aspects
• Spaghetti Charts
June 18, 2012
St. Michael’s Hospital
DMAIC - Improve
• How should the future state look?
• Use rapid process improvement cycles
• Pilot and observe
• Remove unnecessary steps and create a
future state
• No need to get it perfect first time
• Implement pilots to assess impact
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St. Michael’s Hospital
DMAIC - Control
• Re-evaluate and make ongoing changes
• Monitor the new performance
• Repeat the cycle as require to further
improve
• Reevaluate the changes and re-design as
needed
• Repeat evaluation of process to assess
impact
• Ongoing performance monitoring
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Tool Matrix
June 18, 2012
St. Michael’s Hospital
Case Study Stem 1
• You have been asked to review your
preadmission facility by your CMO
because:
– Patients are unsatisfied with long wait times
– Surgeons offices are frustrated they cannot
access short-notice appointments
• These are necessary to fill time released by lastminute patient cancellations
– Staff complain of declining morale
• Anaesthesiologists are reluctant to work in clinic
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St. Michael’s Hospital
2. Facilitated discussion:
Key concepts and tools to address this problem
• Perception shift: this is a chain, not a
series of independent events
• Concepts:
– Bottleneck
– Batching
• Flow mapping: practicalities
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St. Michael’s Hospital
This is a process, not a series of
independent events
Anne M Breen, Tracey Burton-Houle, David C Aron,Applying the theory of
constraints in health care: Part 1-- the philosophy, Quality Management in
Health Care; Spring 2002; 10, 3;pg 40.
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St. Michael’s Hospital
If each step has a measurable capacity, what
determines overall throughput?
A.
Average (13)
B.
Highest cacapacity pacity (17)
C.
Lowest capacity (8)
D.
Cannot answer – need simulation model
June 18, 2012
The chain must be considered as a
whole, not as a series of independent
events
20
Local optima don’t matter !
St. Michael’s Hospital
If bottlenecks limit throughput, why not
simply eliminate them?
13
13
13
13
13
• Because in real life, systems need flexibility:
– Ability to catch up = excess capacity
– Need for excess capacity increases with system
complexity/variability
June 18, 2012
St. Michael’s Hospital
So what do you do with bottlenecks?
• Identify the bottleneck
• Elevate the bottleneck
• Design the process around the bottleneck
– Unload the bottleneck
– Keep the bottleneck busy all the time
• This means non-bottleneck resources MUST
sometimes be IDLE.
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St. Michael’s Hospital
Batching: a very special effect on bottlenecks
• Batching refers to the processing of many
units in a single group, for example:
– I change all the ceiling light bulbs at the same
time because I need a stepladder (hard to
get)
– Painting all similar colours together (trim, then
walls, then contrast wall)
– Porters delivering multiple samples to the lab
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Batching: advantages and disadvantages
Pro
Cons
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Process mapping: putting it all together
Lather
Repeat
Rinse
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Flow Mapping: Common Concerns
• What if I don’t get it right the first time?
• How do I keep people focused?
– How do I frame the hypothesis?
• How much technical stuff do I need to
know to participate or lead this
discussion?
June 18, 2012
St. Michael’s Hospital
What if I don’t get it right the first time?
• Don’t worry… you won’t get it right the first
time – That’s part of the plan…
• It’s an iterative process, and you’ll likely
need a few drafts.
• It’s a group process, and much benefit
comes from team discussion:
“Oh so that’s what happens when the patient
leaves my care…”
June 18, 2012
St. Michael’s Hospital
Basic approach to frame the process
• Set clear ‘start’ and ‘end’ points
• Follow a single patient through a standard
encounter
• Use Post-It notes on large paper
background
• Transcribe draft into clean computer after
meeting
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St. Michael’s Hospital
How many fancy symbols do you need to master?
Terminator
Defines start/end of process
(only 2 per map)
Activity
This is where work happens
Decision
“a fork in the road”, best phrased as yes/no
question
Flow Line
Connect steps
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St. Michael’s Hospital
3. Process Mapping exercise
• Please use this time to develop a process
map in small group settings
• Use the data from case study stem 2 (next
slide) as a starting point for your process
map
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St. Michael’s Hospital
Case study stem 2: Clinic details
• 60 patients are seen daily;
• Patients are registered, then seen by a nurse,
then by a family doctor;
• 50 % of patients seen by an anaesthesiologist;
• Subgroups (orthopaedic and cardiac surgery)
patients also receive group teaching;
– Other patients receive DVD-based teaching;
• Most patients receive bloodwork, and EKG +/- xray investigations while in clinic.
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Define – Process Mapping Exercise
• Three groups
• Map the current state
• Brief Presentation of processes found
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4. Quantitative analysis: Facilitated discussion
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Initial Thoughts
• Quick Fix approach vs Root Cause
Analysis
– Bottlenecks
– Local optima vs global optimum
– Non-value add activity
– Batching
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Define – Process Mapping Exercise
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Define – Process Mapping Exercise
• Lessons Learned
– Conventions in mapping
– Importance to map out whole process
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Measure
• Sources of Data
• IT/IM Resource
• Presentation of information
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5. Quantitative analysis Group work
Case Study Stem #3
• Quantitative Data to be provided in the
following slides/handouts. Please review
and discuss implications of quantitative
data.
June 18, 2012
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Stem #3: Quantitative Data (Continuation)
• Re-Design of a
RN Wait Time
• Pre-Admission Facility
RN Encounter
Time
Mean
13.7 min
32.5 min
Median
10 min
30 min
Standard Dev.
9.9 min
12.9 min
Resource
Availability
8 Nurses
Throughput
14.8
patients/hours
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St. Michael’s Hospital
Stem #3: Quantitative Data (Continuation)
FMD Wait Time
FMD Encounter
Time
Mean
21.5 min
7.6 min
Median
20 min
6 min
Standard Dev.
17.1 min
3.9 min
Resource
Availability
1 FMD
Throughput
7.9 patients/hour
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St. Michael’s Hospital
Stem #3: Quantitative Data (Continuation)
Anaesthesia (AN) Wait
Time
AN Encounter
Time
Mean
27.6 min
12.3 min
Median
20 min
10 min
Standard Dev.
21.9 min
5.7 min
Resource Availability 1-2 AN
Throughput
4.9 patients/hour (1 AN)
June 18, 2012
St. Michael’s Hospital
Stem #3: Quantitative Data (Continuation)
AN wait time by scheduled time of day
Patient Ready Time vs.
AN Wait time
6:00
4:48
3:36
Wait time
2:24
Acceptable Wait Time
1:12
0:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
Patient Ready Block
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Throughput balancing: find the bottleneck
Nurse
Throughput:
14.8 patients/hr
FMD
Throughput: 7.9
patients/hr
AN Throughput:
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Measure – Data Interpretation
•
•
•
•
•
Wait-time and value-add times
Satisfaction
Capacity analysis
Scheduling
Variability
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6. Root Cause analysis
June 18, 2012
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Analyze – Root Cause Analysis
Rework, competing
priorities, and
interruptions at triage
slows down the overall
process and adversely
affects staff and patient
satisfaction.
Excessive waiting time
along with a confusing
process for patients
affects patient
satisfaction within the
ED.
Multiple
Competing
Duties
Merging of
Patient
Information
Triage/Wtg
Rm Traffic
Directed by
RN
Lack of
Consistent
Triage Process
Multiple
Phone Calls,
Interruptions
Data Entry
Many ways
to get info
for Pt Reg
Multiple Entry
Points for ED
Patients
Redundancy
in Validation
of Patient
Information
Gaps in Patient
Education
Redundancy in
information gathering
along with seeking out
information through
different channels,
causes delays and
frustration for staff and
patients. There is an
increased risk for errors.
Repetitive
Collection
of Pt Demo
Continuous
EDIS vs ADT
Reconciliat’n
Multiple
Competing IT
Systems
Patient
Registration
Seeking
Add’l Info
Lack of
Standardized
Forms
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St. Michael’s Hospital
Analyze – Theory of Constraints
1. Identify the Constraint
2. Exploit the Constraint
3. Subordinate everything
to the Constraint
4. Elevate the Constraint
5. Repeat for the new
Constraint
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Analyze – Computer Simulation
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7. Facilitated discussion
Case Study Stem #4: Le denouement
• Suggestions are implemented, but results are
not anticipated
– Wait times increase
– Throughput decreases
• Morale deteriorates significantly
– Staff, especially RN’s leave their positions leaving
unfilled vacancies
– Much finger-pointing/blaming ensues
June 18, 2012
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8. Key success factors for implementing and monitoring
change
June 18, 2012
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Improve – Stakeholder Engagement
•
•
•
•
•
•
•
Engage in issues that matter
Use Engagement to drive decisions
Engage the right stakeholders
Engage empowered representatives
Seek shared values
Agree on the rules of engagement
Manage expectations –provide adequate
resources
June 18, 2012
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Improve – Stakeholder Engagement
• What stakeholders need:
– Fairness
– Listen
– Build Trust
– Be open
– Be accountable
– Evaluate
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Improve – Change Management
•
•
•
•
•
•
•
Establishing a Sense of Urgency
Forming a Powerful Guiding Coalition
Creating a Vision
Communicating the Vision
Empowering Others to Act on the Vision
Planning for and Creating Short-Term Wins
Consolidating Improvements and Producing
Still More Change
• Institutionalizing New Approaches
Kotter, Leading Change 1996
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Control - Sustainability
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Improve – Unintended Consequences
• Balanced Scorecare
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Improve – Measuring Success
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Control – Control Charts
CTAS 1-3 Performance (percentage met EDLOS < 8 hours)
CTAS 4-5 Performance (percentage met EDLOS < 4 hours)
Apr '08 to Aug '10
100
95
89
90
85
82
80
76
75
70
65
60
55
CTAS 1-3
52
CTAS 4-5
Aug-10
Jul-10
Jun-10
May-10
Apr-10
Mar-10
Feb-10
Jan-10
Dec-09
Nov-09
Oct-09
Sep-09
Aug-09
Jul-09
Jun-09
May-09
Apr-09
Mar-09
Feb-09
Jan-09
Dec-08
Nov-08
Oct-08
Sep-08
Aug-08
Jul-08
Jun-08
May-08
50
Apr-08
%
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Conclusion
•
•
•
•
•
DMAIC Methodology
Stakeholder Engagement
Leading Change
Measuring Success
Importance of Value Add
June 18, 2012
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Appendix
• The following slides can serve to
supplement case discussion.
June 18, 2012
St. Michael’s Hospital
Theory of Constraints asserts that in the real world a
‘balanced plant’ will self-destruct
Statistical variability: Throughput at each step
varies around a mean
+
Dependent events: a downstream process cannot
occur before its upstream precursor
=
Small gaps build up to infinity unless there is
reserve capacity
June 18, 2012
St. Michael’s Hospital
Consider the famous example of a group of hikers
• Scouts are heading on a 5 mile hike
• They must walk single file
– They cannot pass each other (dependent
events)
• Each hiker walks at a similar pace, but
there is some variation
– Each time a scout stumbles or slips, he loses
some ground
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St. Michael’s Hospital
Diagram of the ‘Goldratt’ hike
Direction of hike
Start
SSSSSSSSSSSSSSSSSSSSSSS
After 1 hour
SS
SSSS SS SS SSS SSSS SSS
S
S S
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Conclusions from the hiking example
1. Over time, the scouts will continue to
spread;
2. To keep the group compact, one must
place the slowest hiker (bottleneck) at the
front.
June 18, 2012
St. Michael’s Hospital
So how do you identify bottlenecks?
• In the hiker example, you look for a large
gap in front of a scout
• In a plant, you might look for a large pile of
inventory in front of a particular station
• In a hospital, you could look for a large
number of (angry looking) patients in a
waiting room
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Operational management requires awareness
of two key elements
• Variability: Statistical variation and dependent
events
• Bottlenecks: Bottlenecks are neither good nor
bad
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Batching: a very special effect on bottlenecks
• Batching refers to the processing of many
units in a single group
• All units have the same start/finish times
• Batching is highly effective when setup
costs/setup time are high
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Batching cupcakes:
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As a cupcake-baker,
batching is great because:
• I mix one batch of batter, drop it into
moulds, place in the oven, and I’m done;
• I only have to run the oven once (lower
energy costs );
• This is a ‘locally optimal’ solution.
June 18, 2012
St. Michael’s Hospital
As a cupcake-decorator,
batching is terrible:
• At first, I have no work to do while the
cupcakes are baking
• Then I suddenly have 20 cupcakes to
decorate.
June 18, 2012
St. Michael’s Hospital
How does this come together?
• Assume baking a batch of 20 cakes takes
– 15 minutes prep + 45 minutes baking
• Assume decorating takes 5 minutes per
cake
• How long would it take to make a single
batch of 20?
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St. Michael’s Hospital
Answers:
A. 5 minutes/cake x 20 = 100 minutes
B. 3 minutes/cake x 20 = 60 minutes
C. 60 minutes + 5 minutes/cake x 20 = 160
min
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Answer is D 160 minutes
• This results in cupcake cycle time of
160/20
= 8 minutes per cake
• That doesn’t seem so bad…
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When was the first cupcake ready?
• 60 + 5 = 65 minutes
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When was the last cupcake ready?
60+100 = 160 minutes
Time for 10th cupcake
60+(10x5) = 110 minutes
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Why might this be a problem?
• Assume cupcakes are shipped from the
kitchen in batches of 20:
– What if a walk-in client wants to pickup 6
cupcakes:
• It takes almost 3 hours for the first (and last) cake
to be ready
– What if the cupcakes sell best when they are
fresh (< 45 minutes from the oven)
June 18, 2012
St. Michael’s Hospital
What are possible solutions?
• Have the cake-decorator start/finish 1
hour after the cake-baker
• Have a cake ‘reserve’ for the decorator
– ‘buffer’ in operations
– parallel in health care: waiting room for
patients
• Make smaller batches
– The ultimate small batch is a single unit
– Might reduce batch size after decoration
June 18, 2012
St. Michael’s Hospital
What is the product at the end of the 8-hour day?
Baking
• 8 hours/batch x 20 cakes/batch = 160 cakes
Decorating
• 7 hours (1 lost hour) x 12 cakes/hour = 84
cakes
Total
• 84 finished cakes
• 76 ‘waiting’
June 18, 2012