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1 800 547 6024 | +44 141 552 6888
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Memorial Health System
Discrete Event Simulation
Todd S. Roberts, MBA, CLSSMBB
System Director, Operations Improvement
Memorial Health System
May 15, 2013
Systems thinking is the ability to see
things as a whole (or holistically),
including the many different types of
relationships between the diverse
elements of a complex system
Necessary component of “learning
organizations”
Takes cause-and-effect thinking to a
higher level and encourages the user
to see not just the linear causal
connections but also the web of
causal interconnections that come
into play in real systems
“The Fifth Discipline”
“Adjusting the system or process
inputs to produce the best possible
average response with minimum
variability”
System Optimization
The sensitive
dependence on initial
conditions, where a
small change at one
place can result in
large differences to a
later state.
Butterfly Effect
Three types of failures in complex systems:
– Procedural
• Failure to adhere to/execute a defined process
• Single, obvious mistakes
• Special-cause variation
• Plan, Do, Check, Act or corrective action
– Engineered
• People, process, materials
• Common-cause variation
• Defined processes
• Lean Six Sigma projects
– System
• Complex interactions between processes and risk factors
• Difficult to understand and pinpoint cause and effect relationships
• Discrete event simulation
Failure in Complex Systems
Simulated floor design and throughput for new
Rapid Clinical Examination provider model for a
70,000 annual visit, Level I trauma center
emergency department at a 507 bed, tertiary, urban,
academic medical center
Simulation was constructed using floor layout
schematic and provider resource models based
upon historic hourly ED arrival (Poisson) and service
distribution rates (exponential) for high, mid, and low
acuity patients as well as admitted vs. discharged
dispensation
ED Flow Redesign Project
Goals of the simulation model were as follows:
– Determine the most efficient model for routing
patients through the system (high acuity patients to
main ED, low acuity patients to rapid clinical
examination)
– Determine the number of provider resources
necessary for staffing based upon patient distribution
– Determine primary macro factors affecting length of
stay for all patients
– Identify process constraints and bottlenecks
– Identify factors contributing to increased patient wait
time and patients leaving without treatment (LWOT)
ED Simulation Goals
Determined the appropriate routing model for
patients to the main ED and the Rapid Clinical
Examination process
The provider mix was adjusted to accommodate
peak volumes throughout the day in an effort to
minimize wait times and LWOTS
A number of Lean Six Sigma projects were
chartered based upon the findings of the Simulation
model, including time from imaging complete to
discharge, lab turnaround time, and CT utilization
and turnaround time
Simulation Results
EMERGENCY DEPARTMENT RAPID
CLINICAL EXAMINATION MODEL
POST-IMPROVEMENT DATA
UCL
285.2
CL
208.9
LCL 132.5
104
154
204
254
304
354
404
01/01/2013
02/01/2013
03/01/2013
04/01/2013
05/01/2013
06/01/2013
07/01/2013
08/01/2013
09/01/2013
10/01/2013
11/01/2013
12/01/2013
13/01/2013
14/01/2013
15/01/2013
16/01/2013
17/01/2013
18/01/2013
19/01/2013
20/01/2013
22/01/2013
23/01/2013
24/01/2013
25/01/2013
26/01/2013
27/01/2013
28/01/2013
29/01/2013
30/01/2013
31/01/2013
01/02/2013
02/02/2013
03/02/2013
04/02/2013
05/02/2013
06/02/2013
07/02/2013
08/02/2013
09/02/2013
10/02/2013
11/02/2013
12/02/2013
13/02/2013
14/02/2013
15/02/2013
16/02/2013
17/02/2013
OverallLOS
Date
Overall Length of Stay
16%
Improvement
UCL
231.8
CL
154.7
LCL 77.6
49
59
69
79
89
99
109
119
129
139
149
159
169
179
189
199
209
219
229
239
249
259
269
279
289
01/10/2012
06/10/2012
11/10/2012
16/10/2012
21/10/2012
26/10/2012
31/10/2012
05/11/2012
10/11/2012
15/11/2012
20/11/2012
25/11/2012
30/11/2012
05/12/2012
10/12/2012
15/12/2012
20/12/2012
25/12/2012
30/12/2012
04/01/2013
09/01/2013
14/01/2013
19/01/2013
23/01/2013
28/01/2013
02/02/2013
07/02/2013
12/02/2013
17/02/2013
22/02/2013
27/02/2013
04/03/2013
09/03/2013
14/03/2013
19/03/2013
24/03/2013
29/03/2013
03/04/2013
08/04/2013
12/04/2013
17/04/2013
22/04/2013
27/04/2013
RCELOS
Date
Rapid Clinical Examination Length of Stay
26% Overall
Improvement
UCL
356.2
CL
259.6
LCL
163.0
130
155
180
205
230
255
280
305
330
355
380
405
430
455
480
505
530
01/01/2013
04/01/2013
07/01/2013
10/01/2013
13/01/2013
16/01/2013
19/01/2013
22/01/2013
25/01/2013
28/01/2013
31/01/2013
03/02/2013
06/02/2013
09/02/2013
12/02/2013
15/02/2013
18/02/2013
21/02/2013
24/02/2013
27/02/2013
02/03/2013
05/03/2013
08/03/2013
11/03/2013
14/03/2013
17/03/2013
20/03/2013
23/03/2013
26/03/2013
29/03/2013
01/04/2013
04/04/2013
07/04/2013
09/04/2013
12/04/2013
15/04/2013
18/04/2013
21/04/2013
24/04/2013
27/04/2013
30/04/2013
MainEDLOS
Date
Main ED Length of Stay
16%
Improvement
UCL
4.83%
CL
1.27%
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
10.00%
01/10/2012
06/10/2012
11/10/2012
16/10/2012
21/10/2012
26/10/2012
31/10/2012
05/11/2012
10/11/2012
15/11/2012
20/11/2012
25/11/2012
30/11/2012
05/12/2012
10/12/2012
15/12/2012
20/12/2012
25/12/2012
30/12/2012
04/01/2013
09/01/2013
14/01/2013
19/01/2013
23/01/2013
28/01/2013
02/02/2013
07/02/2013
12/02/2013
17/02/2013
22/02/2013
27/02/2013
04/03/2013
09/03/2013
14/03/2013
19/03/2013
24/03/2013
29/03/2013
03/04/2013
08/04/2013
12/04/2013
17/04/2013
22/04/2013
27/04/2013
LWOTas%ofTotalVolume
Date
Left Without Treatment (LWOT) as % of Total Volume
53%
Improvement
ED Admitted Patient Average Length of Stay (minutes) 2nd
consecutive month below 200 minutes & 1st month below 100
minutes
UCL
211.908
CL
125.017
LCL
38.125
4.70
104.70
204.70
304.70
404.70
504.70
604.70
01/01/2013
02/01/2013
03/01/2013
04/01/2013
05/01/2013
06/01/2013
07/01/2013
08/01/2013
09/01/2013
10/01/2013
11/01/2013
12/01/2013
13/01/2013
14/01/2013
15/01/2013
16/01/2013
17/01/2013
18/01/2013
19/01/2013
20/01/2013
21/01/2013
22/01/2013
23/01/2013
24/01/2013
25/01/2013
26/01/2013
27/01/2013
28/01/2013
29/01/2013
30/01/2013
31/01/2013
01/02/2013
02/02/2013
03/02/2013
04/02/2013
05/02/2013
06/02/2013
07/02/2013
08/02/2013
09/02/2013
10/02/2013
11/02/2013
12/02/2013
13/02/2013
14/02/2013
15/02/2013
16/02/2013
17/02/2013
AdmitRequesttoCheckoutAverage
Date
ED Admit Request to Checkout Average
50%
Improvement
Emergency Department Patient Satisfaction over 80th%ile for 3nd
consecutive month & 2nd consecutive month at 98th%ile or above .
2nd quarter FY 2013 99th%ile .
– May 2012 – RCE Launch
– July 1, 2012 – RCE Fully implemented 7
days/week
– August 17, 2012 – RCE Red Flag criteria change
(based on Simulation)
– January 21, 2013 – 4th lane of RCE added
(Based on Simulation)
– April 9, 2013 – ED facilities remodeled to support
process flow
Key Process Changes
Simulate flow for all aspects of architectural
design proposal for $31 million dollar operating
room expansion project, including pre-op
admission, transport to OR, OR time, and post-
anesthesia care units (PACU) for admitted and
outpatient surgery
Test assumptions for capacity based on an
expansion of 5 operating rooms (and pre-
op/PACU beds) and increased volumes of 15%
over the next 5 years
OR Renovation Design Simulation
2 Elevators
3 Elevators
Operating Room Opportunity Cost
= $54/minute
Identified process bottlenecks and determined that
with a surge of patients transported to the OR for
first and second-case starts, that two elevators from
the pre-op holding area to the ORs is not adequate
for flow, and will lead to staff, physician, and patient
dissatisfaction while increasing overall variation by
30 minutes per case throughout the day.
Decision was made to add a third elevator to the
design to satisfy flow demand
The discovery of downstream increase in variation
could not have been achieved and recognized using
static waiting line models.
Simulation Results
Lean Six Sigma projects have been chartered to
streamline scheduling processes and OR room
turnover processes to further reduce variation
and increase capacity
Studies conducted for projected increased
volume year over year have allowed the building
of adequate facilities for the next 20 years
Next Steps
Requires deep process understanding (avoid
tampering)
Creates a shared visual understanding of the
process for all parties
Allows for observational analysis and
modification without physical intervention in a
complex environment (offline trial and error)
Supports improved decision-making through
management by fact
Discrete Event Simulation Benefits
Contact: roberts.todd@mhsil.com
(217) 757-7782

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