Transcript Throughput and Overcrowding in the ED – So What Else is New?

Christy Dempsey, RN MBA CNOR
August 26, 2009



Understand how other areas of the hospital
directly impact the flow of patients in the ED
Demonstrate how queuing analysis and
simulation modeling can be employed inside
and outside the ED to improve flow and
increase capacity without building
infrastructure or hiring more staff
Learn how other organizations have used this
information and methodology for significant
and sustainable results
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
"Hospital chief executive officers should
adopt enterprise-wide operations
management and related strategies to
improve the quality and efficiency of
emergency care.”
“By smoothing the inherent peaks and valleys
in patient flow, and eliminating the artificial
variabilities that unnecessarily impair patient
flow, hospitals can improve patient safety and
quality while simultaneously reducing
hospital waste and cost.”
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ED and PACU boarding/overcrowding
Staff shortages - nursing and physician
The “any bed available” phenomenon
Quality concerns related to nurse:patient ratios, medical
errors, and adverse events
Frustration due to unpredictable schedules and inability
to care for patients the way physicians want to care for
them
Increasing workloads and decreasing reimbursement
OR
ED
Direct
Admits
Hospital Census
Which do we have
the most control over??

NO
◦ These are usually sick patients
◦ Sent from physician office
◦ May be scheduled through Cath Lab or other
procedural area – higher risk patients
◦ Random arrivals

NO
# of Admissions
Elective Vs. Emergent Daily Admissions
70
60
50
40
30
20
10
0
Monday
# of Emergent
Admissions
Average Emergent
# of Elective
Admissions
Tuesday
Wednesday
Day
Thursday

“ED overcrowding is caused by a complex set
of conditions that occur across hospital units
and across the entire health care system.
Inability to move admitted patients from the
ED to the appropriate inpatient unit stands
out as a major driver of ED overcrowding.”
Emergency Department Utilization and
Capacity
July 2009

YES!!
◦ Variability in the elective surgery schedule is the
culprit
◦ Totally schedulable
◦ Totally within our control
◦ Peaks and valleys in the elective schedule result in
peaks and valleys in inpatient census

Boarding
◦ ED
◦ PACU
◦ OR

Inappropriate patient placement
◦ Any bed available



Increased length of stay
Increased risk of morbidity/mortality
Increased risk of adverse events

Physicians
Frustration due to unpredictable schedules
Rounding in multiple locations
Long waits to do cases – elective and non-elective
Frequent phone calls from nurses unaccustomed to
care for their patients
◦ Longer lengths of stay result in increased risk of
complications, infection, adverse events
◦ Inability to grow, practice and revenue implications
◦
◦
◦
◦
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Unpredictable schedules
◦ OT
◦ Low workload days
◦ Staffing unfamiliar cases

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
Equipment competition
Recruitment and retention issues
Training issue for downstream nursing units
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Overcrowding
Boarding
Diversions
Safety
Quality
Liability
Burnout
Recruitment/Retention



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Lower overall utilization despite overcrowding
Loss of contracted payors
Liability
Reduced reimbursement – medical errors,
never events, boarding
Capital constraints
Duplication of human and material resources
during peaks
Wasted human and material resources during
valleys

Recognize flow is an organizational issue
 ED is at the mercy of the inpatient census

Manage uncontrollable variability
 ED admissions
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Reduce/eliminate controllable variability
 Smooth elective hospital admissions
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Assure transparent and credible data
Involve physicians
Make progress
The Cause?
Controllable (Artificial) Variability
Types of Variability
 Uncontrollable (Natural)
– Random but often predictable
– Manageable but cannot be eliminated
– Example: emergent/urgent ED volume
 Controllable (Artificial)
– Non-random
– Caused by management practices such as
scheduling, staffing practices
– Example: elective surgery schedule
 Combine the hard science of rigorous data
collection and analysis with the soft science
of change management and operations
expertise
 Collaboration between physicians and
hospital leadership
 Culture must change—if you always do what
you’ve always done, you’ll always get what
you’ve always gotten!
Add-On
Add-On Mean
Scheduled
Da te
Scheduled Mean
Thu,3/29/07
Tue,3/27/07
Fri,3/23/07
Wed,3/21/07
Mon,3/19/07
Thu,3/15/07
Tue,3/13/07
Fri,3/9/07
Wed,3/7/07
Mon,3/5/07
Thu,3/1/07
Tue,2/27/07
Fri,2/23/07
Wed,2/21/07
Mon,2/19/07
Thu,2/15/07
Tue,2/13/07
Fri,2/9/07
Wed,2/7/07
Mon,2/5/07
Thu,2/1/07
Tue,1/30/07
Fri,1/26/07
Wed,1/24/07
Mon,1/22/07
Thu,1/18/07
Tue,1/16/07
Fri,1/12/07
Wed,1/10/07
Mon,1/8/07
Thu,1/4/07
Tue,1/2/07
Count
Real-Life Variability in the OR
Add-On and Scheduled OR Cases By Date
WellStar Kennestone Hospital
Non-Holiday Weekdays, 1/2/2007-3/30/2007
90
80
70
60
50
40
30
20
10
0
Inappropriate Patient Placement
Destination Units for Post-op Patients from PACU:
Orthopedic Inpatients Only
Pre-Project: WellStar Kennestone Hospital
Missing
6%
Other
9%
7W
8%
7S
11%
7N (Ortho)
66%
Three Typical “Fixes”
 Build and staff to peak demand in EDs, ORs and
in downstream units; tolerate overspending on
staff and material expenses, underutilization
during non-peak times
 Staff below the peaks; tolerate ED diversions,
nursing overloading and medical errors
 Staff for averages and try to flex up or down to
manage unpredictable demand; tolerate the
same negative effects
The Real Solution
Smooth artificial variability and provide
resources to meet patient-driven
(vs. schedule-driven) peaks in demand
3-step process
Step 1
Step 1
Separate Scheduled from Unscheduled
OR Flow
Step 1 implementation
• Collect and analyze data on emergent/urgent (add-on)
cases, including arrival patterns and urgency
• Apply queuing theory to determine capacity needed to
accommodate add-on cases within clinically acceptable
wait times
• Adjust plan based on physician and hospital input
• Allocate resources to meet the separate demands of
scheduled and unscheduled volumes
Step 2
Step 1
Separate Scheduled from Unscheduled
OR Flow
Step 2
Smooth flow of
scheduled patients
Step 2 implementation
• Evaluate daily case variation in scheduled cases by surgical
service as well as by destination units
• Work in collaboration with surgical practices to redesign
the OR schedule to smooth daily case volume based on
destination unit
• Smoothing should take into account clinic schedules,
surgeons’ teaching and other responsibilities, hospital case
mix, and size of destination units
Step 3
Step 1
Separate Scheduled from Unscheduled
OR Flow
Step 2
Smooth flow of
scheduled patients
Step 3
Determine Bed
and
Staffing needs
Step 3 implementation
• Apply simulation models to determine the number of beds and
staff needed to achieve a desired level of service
• Maximize throughput by streamlining the discharge process and
addressing length of stay issues
• Implement process improvement in downstream units including
admission and discharge processes, ED specific flow
improvements, hospitalist and medicine specific flow
improvements
Queuing Theory
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Mathematical tool used to determine
capacity needed to handle random
arrivals with constrained resources
Used in industry since the early 1900’s
Relevance to improving patient flow
newly recognized
Can be applied to any procedural area
with a mix of elective and add-on cases
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Arrivals are random – ED volume and
urgent/emergent OR cases
Average service time - ED visit lengths or
urgent/emergent surgical case duration
+ room turnover time - can be calculated
Number of servers (ED treatment rooms/
physicians, OR, cath labs) is limited

Optimum number of treatment or operating
rooms for add-on (urgent/emergent) cases

Optimum number of ED physicians

Average wait time by triage or urgency class


Percent of time an ED, ED physician, or OR
will be available immediately for an
emergency patient
Utilization rates of ORs and ED rooms or
physicians

Patient arrivals
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Triage level of patient arrivals
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Average visit length – door to door

Inputs
◦
◦
◦
◦
◦
◦
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Arrival rates by hour
Acuity of arrivals
Average service rate
Room turnover between patients
Staffed shifts
Desired waiting times
Outputs
◦ Waiting time for each acuity
◦ Utilization rates of rooms
◦ Outputs by shift
# Classes
5
5
5
5
5
5
Start Class
A
A
A
A
A
A
End Class
E
E
E
E
E
E
# Servers
40
41
42
43
44
45
Day Type
WD
WD
WD
WD
WD
WD
Start Time
7
7
7
7
7
7
End Time
15
15
15
15
15
15
TOT
5
5
5
5
5
5
0.335
0.335
0.335
0.335
0.335
0.335
Arr Rate 1
0.14
0.14
0.14
0.14
0.14
0.14
Arr Rate 2
1.89
1.89
1.89
1.89
1.89
1.89
Arr Rate 3
6.17
6.17
6.17
6.17
6.17
6.17
Arr Rate 4
3.19
3.19
3.19
3.19
3.19
3.19
Arr Rate 5
0.47
0.47
0.47
0.47
0.47
0.47
Wait 1(Immediate)
4.5
4.4
4.3
4.2
4.1
4
Wait 2 (Emergent)
5.3
5.2
5
4.9
4.7
4.6
Wait 3 (Urgent)
13.5
12.7
11.9
11.2
10.6
10
Wait 4 (Less Urgent)
76.3
63
53.2
45.8
40
35.4
Wait 5 (Non-urgent)
256.7
185.4
141
111.4
90.7
75.5
0.33
0.4
0.46
0.51
0.57
0.62
88.46
86.3
84.24
82.28
80.41
78.63
Results
Service Rate
% Avail
Util %
# Classes
5
5
5
5
5
5
Start Class
A
A
A
A
A
A
End Class
E
E
E
E
E
E
# Servers
40
41
42
43
44
45
Day Type
WD
WD
WD
WD
WD
WD
Start Time
3
3
3
3
3
3
End Time
11
11
11
11
11
11
TOT
5
5
5
5
5
5
0.335
0.335
0.335
0.335
0.335
0.335
Arr Rate 1
0.17
0.17
0.17
0.17
0.17
0.17
Arr Rate 2
2.62
2.62
2.62
2.62
2.62
2.62
Arr Rate 3
6.11
6.11
6.11
6.11
6.11
6.11
Arr Rate 4
3.95
3.95
3.95
3.95
3.95
3.95
Arr Rate 5
Results
Service Rate
0.39
0.39
0.39
0.39
0.39
0.39
Wait 1
4.5
4.4
4.3
4.2
4.1
4
Wait 2
5.7
5.5
5.4
5.2
5.1
4.9
Wait 3
16.8
15.5
14.4
13.5
12.6
11.9
Wait 4
319.8
190.5
132.6
100.1
79.5
65.3
Wait 5
8590.7
1833.9
819.2
470.4
308.1
219
0.03
0.09
0.15
0.21
0.26
0.32
98.75
96.34
94.05
91.86
89.77
87.78
% Avail
Util %
# Classes
5
5
5
5
5
Start Class
A
A
A
A
A
End Class
E
E
E
E
E
# Servers
18
19
20
21
22
Day Type
WD
WD
WD
WD
WD
Start Time
11
11
11
11
11
End Time
7
7
7
7
7
TOT
5
5
5
5
5
0.335
0.335
0.335
0.335
0.335
Arr Rate 1
0.09
0.09
0.09
0.09
0.09
Arr Rate 2
1.26
1.26
1.26
1.26
1.26
Arr Rate 3
2.28
2.28
2.28
2.28
2.28
Arr Rate 4
0.8
0.8
0.8
0.8
0.8
Arr Rate 5
0.05
0.05
0.05
0.05
0.05
Results
Service Rate
Wait 1
9.9
9.3
8.8
8.4
8
Wait 2
12.7
11.8
11.1
10.4
9.8
Wait 3
31.5
27.2
23.8
21.2
19
Wait 4
92
70.3
56
46.1
38.9
Wait 5
142.4
101.9
77.5
61.4
50.3
2.04
2.34
2.61
2.86
3.08
74.25
70.34
66.83
63.64
60.75
% Avail
Util %

Arrival patterns change
◦ New hospital or closure of an ED increases volumes
◦ Flu season

Treatment times change
◦ Additional physician or nursing staff
◦ Reduction of boarding allows for a reduction in
average treatment time
# Urgency Classes (ESI groups) Included
5
5
5
5
5
5
Start Class
1
1
1
1
1
1
End Class
5
5
5
5
5
5
# Treatment Rooms
40
41
42
43
44
45
Day Type
WD
WD
WD
WD
WD
WD
Start Time
11p
11p
11p
11p
11p
11p
End Time
7a
7a
7a
7a
7a
7a
Service Rate
150 mins
150 mins
150 mins
150 mins
150 mins
150 mins
TOT
5
5
5
5
5
5
Arr Rate 1
0.17
0.17
0.17
0.17
0.17
0.17
Arr Rate 2
2.62
2.62
2.62
2.62
2.62
2.62
Arr Rate 3
6.11
6.11
6.11
6.11
6.11
6.11
Arr Rate 4
3.95
3.95
3.95
3.95
3.95
3.95
Arr Rate 5
0.39
0.39
0.39
0.39
0.39
0.39
Wait 1-Immediate
3.9
3.8
3.7
3.6
3.5
3.5
Wait 2-Emergent
4.8
4.6
4.5
4.3
4.2
4.1
Wait 3-Urgent
11.1
10.4
9.8
9.2
8.8
8.3
Results
Wait 4-Less Urgent
53.3
45
38.7
33.8
29.8
26.7
Wait 5-Not Urgent
156.5
119.2
94.3
76.8
64
54.3
% Avail
0.43
0.5
0.56
0.61
0.67
0.72
Util %
85.51
83.42
81.44
79.54
77.73
76.01

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Trade-offs between waiting time and
resources applied
Hard science vs soft science balance

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Active involvement by project committee of
physician leaders, top hospital management
Timely review of questionable urgency/acuity
classifications
Performance monitoring

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

Average wait time by triage/urgency class
Compliance with maximum wait time by
triage/urgency class
Treatment room/physician utilization
Availability of a room when a level one
(emergency) case arrives

Boarding days/times

Appropriate patient placement in downstream units



Frequency of Re-evaluation
◦ Quarterly under normal circumstances
◦ Immediately if major issues
Triggers for Change
◦ Non-elective volume increases or decreases
◦ New services or surgeons with non-elective cases
◦ Expansion or contraction of ED or OR capacity
Trade-offs related to Changes
◦ Staff availability
◦ Resource constraints
Demonstrated Results:
Physician Satisfaction
Physician Satisfaction Increase
Ease of Admitting
Patients Ease of admitting
7.1
patients
Access to
transcriptions*
3.9
Sched inpatient
tests/therapy
3.6
Sched outpatient
tests/therapy*
3.4
Pharmacy*
3.3
Info re hos changes infl
pers pract*
3.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
The most noticeable shifts (scores which changed >= +/- 3.0
points) tended to involve patient flow issues.
10.0
Reduction of Variability
Highest volume day (69 cases) is 1.6 times the lowest volume day
(42 cases) vs. Substantial variability in elective surgery cases
before: highest volume day (82 cases) is 2.15 times the lowest
volume day (38 cases)
BEFORE
AFTER
Wellstar-Kennestone
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http://www.rwjf.org/pr/product.jsp?id=45929&c
http://www.acep.org/uploadedFiles/ACEP/Membersh
ip/Sections_of_Membership/intnatl/news/2008Boardi
ngReport.pdf
http://www.referenceforbusiness.com/encyclopedia/
Pro-Res/Queuing-Theory.html
http://www.hhnmag.com/hhnmag_app/jsp/articledis
play.jsp?dcrpath=HHNMAG/Article/data07JUL2008/0
80715HHN_Online_Eitel&domain=HHNMAG
Litvak E, Long MC, Cooper A, McManus M. Emergency
department diversion: Causes and solutions.
Academic Emergency Medicine. 2001;8(11):11081110.
Christy Dempsey, RN MBA CNOR
SVP of Clinical Operations
Press Ganey Associates, Inc
417-877-7666
[email protected]