Transcript Specialty Access: Beyond the front door

Principles for Flow
Improvement

#1: Understand, measure and
achieve a balance between upstream
and downstream demand
Basic Dynamic
Door to
doctor time
Decision to
admit to admit
Ready to
Tx to tx
Length of Stay
Decision to
Dx to dx
System demand
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Look at all the streams of demand (all the kinds of
work) into each step
Different demand streams will have different units of
measure
Demand = volume of demand X LOS (time)+parking
Look at the variation (volume, arrivals and handling
time) of demand in each stream
Look at the supply set aside against each demand
stream both within the step and between demand
streams (allocation)
Supply is time of various converging components
Measure the variation of supply
Compare demand to all the supply- all lines need to
balance
Measurement for Each Step
Demand:
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Volume
External/internal
Who
Where
What
Variation/range in
volume, in arrivals
and in handling
time
D= Volume X
LOS
(time)
Supply:
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
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
Volume
Competing venues
Variation/range
Volume X Time
Converging
components
Delay:
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
How long
Variation in
delays
Metrics at each step
Input
 Throughput
 Output
 Output for one step is input for the
next step

WORLD
PRIMARY CARE
ED
Diagnostic
Tests
SPECIALTY CARE
Pr
ep
ar
eC
PREP
lin
ic
SURGERY
ICU/CCU
Advanced Care
MENTAL
HEALTH
HOSPITAL FLOOR
PACU
HOME
Other Places
MMA MEASURES
WORLD
D
D
D
S
S
PRIMARY CARE
D
D
S
ED
D
D
Diagnostic
Tests
S
D
SPECIALTY CARE
S
Pr
ep
ar
eC
D
lin
PREP
ic
S
D
D
S
D
S
D
S
SURGERY
ICU/CCU
Advanced Care
D
HOSPITAL FLOOR
S
PACU
D
Demand at each step
Supply at each step
Variation at each step
Wait time for each step
Process time at each step
S
HOME
Other Places
MENTAL
HEALTH
FLOW DIAGNOSTIC MEASURES
WORLD
PRIMARY CARE
ED
Diagnostic
Tests
SPECIALTY CARE
Pr
ep
ar
eC
lin
PREP
ic
S
(beds)
LOS
SURGERY
ICU/CCU
Advanced Care
MENTAL
HEALTH
HOSPITAL FLOOR
PACU
LOS for patients who went to NH
(activity) = demand
* Admissions
* Discharge patient days
* Discharges
* CMI
* Potential Bed Turns (365/LOS)
* Un + Adjusted Bed Turns
* Utilization–Unadjusted bed turns/
potential bed turns A/S
Adjusted bed turns = CMI admissions +
OBs + SDCs / functional beds
HOME
Other Places
Unadjusted bed turns = admissions + OBs
+ SDCs / functional beds
Delay for the step: input
In some steps, demand= arrivals so
there is no delay (for ED)
 Between and within other steps,
there is a delay (from ED to floor)

Delay within the step (throughput)

ED
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
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Each floor or service


LOS
Decision to discharge to discharge
wasted capacity (defect)
Throughput for a specific patient stream
(CHF)
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
cycle time
Door to doctor time
ED
Hospital LOS
Lead Time in ED
Door
To
Doctor Time
Cycle 1
Length of Stay in ED
Cycle 2
Decision to
Admit to
Admission
Cycle 3
ED
Admit
Process
Hospital LOS
Days in Hospital
Cycle 1
Decision to
Discharge to
Discharge
Cycle 2
Day 1
Day 2
Cycle 3
Day 3
ED Lead time
Emergency Department Lead Time
100
80
60
40
Door to Discharge
Door to Doctor
1/05
11/04
9/04
7/04
5/04
3/04
1/04
11/03
9/03
7/03
5/03
3/03
1/03
11/02
0
9/02
20
7/02
Median minutes
120
Decision to Admit
ar03
-0
3
ay
-0
3
Ju
l-0
3
Se
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No 3
v03
Ja
n04
M
ar0
M 4
ay
-0
4
Ju
l-0
4
Se
p0
No 4
v04
Ja
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M
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M 5
ay
-0
5
Ju
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5
Se
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No 5
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Ja
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M
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M 6
ay
-0
6
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6
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No 6
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M
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n
Days
Hospital Overall Length of Stay
Monthly ALOS
4.5
4.3
4.1
3.9
3.7
3.5
3.3
3.1
January 2003 - January 2007
LOS
Days
LOS Delay Days 2005-2006
350
300
250
200
150
100
50
0
y
og
l
o
di
r
Ca
M
i
ed
ne
i
c
B
O
YN
G
o
rth
O
ds
e
P
ry
e
rg
Su
From 7/1/05 to 6/30/06 Top 10 DRG’s/specialty
Calculated from Days > Benchmark TIMES volume
Sum = 850 days/year
Within the step (throughput)
Measures
Length of Stay
•Diagnostic admission
•Time from presentation to w/u complete
•Time from order for diagnostic test to
information
•Treatment admission
•Time from admission to treatment complete
•Time from arrival for pneumonia to first
antibiotic start
Delay after the step: Output
Measures

Output for one step is input for the
next
ED: Decision to discharge to admit
 Flow: Decision to discharge to
discharge
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Delay after the step (output)
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ED
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Transfers or direct admits
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
decision to admit to admit
decision to admit to admit
Each floor or service
decision to discharge to discharge
 discharge appointment measures
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Demand Measures
Emergency Department
Demand
Number of patients
Average Visits by Day of Week
90
80
70
60
50
60
To
80
40
30
20
10
0
Monday
Tuesday
Wednesday Thursday
Friday
Jan-Dec 2004
Jan-Dec 2005
Saturday
Sunday
Emergency Department
Number of patients
Average Visits by Day of Week
60
To
80
90
80
70
60
50
40
30
20
10
0
Monday
Tuesday
Wednesday
Thursday
Jan-Dec 2004
Friday
Saturday
Sunday
Jan-Dec 2005
Average Visits by Time of Day
Number of patients
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour
Jan-Dec 2004
Jan-Dec 2005
Emergency Department
Hospital
Demand FOR ED
Demand FOR Hospital
Number of patients
Average Visits by Day of Week
90
80
70
60
50
40
30
20
10
0
Monday
Tuesday
Wednesday
Thursday
Jan-Dec 2004
Friday
Saturday
Sunday
Jan-Dec 2005
Overall ED Admits (as a percent of all ED Visits)
25%
Average Visits by Time of Day
20%
Number of patients
6.0
15%
5.0
4.0
10%
3.0
2.0
5%
1.0
0.0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Jan-06
Oct-05
Eight to Ten Percent are Admitted
Jul-05
Apr-05
Jan-05
Oct-04
Jul-04
Apr-04
Jan-04
Oct-03
Jul-03
Apr-03
Jan-03
Oct-02
Jul-02
Apr-02
Jan-02
Oct-01
Jul-01
Jan-Dec 2005
Apr-01
Jan-Dec 2004
0%
Jan-01
Hour
Emergency Department
Hospital
Demand FOR Hospital
Overall ED Admits (as a percent of all ED Visits)
25%
20%
15%
10%
5%
0%
Jan-06
Oct-05
Jul-05
Apr-05
Jan-05
Oct-04
Jul-04
Apr-04
Jan-04
Oct-03
Jul-03
Apr-03
Jan-03
Oct-02
Jul-02
Apr-02
Jan-02
Oct-01
Jul-01
Apr-01
Jan-01
Admissions from Bellin's ED
0.70
Avg # of pts per hour
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0
1
2
3
4
5
6
7
8
9
10 11
12 13 14 15 16
Hour
2004
2005
17 18 19 20 21
22 23
0.4 to 0.6
Per hour
From
Noon To
Midnight
Admit Pattern
Within the Day
Average Visits by Time of Day
Number of patients
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour
Jan-Dec 2004
Jan-Dec 2005
Scorecard of Key System Measures:
Outcomes
How well we match demand to supply (velocity)
Bed turns
 Adjusted bed turns
 Potential bed turns
 Utilization
 LOS (throughput)
 LWBS and diversions (defects)

Adjusted bed turns
120
100
80
60
40
20
0
FQ1
04
FQ2
04
FQ3
04
FQ4
04
FQ1
05
FQ2
05
Adjusted bed turns
FQ3
05
FQ4
05
FQ1
06
FQ2
06
Adjusted goal (>90)
FQ3
06
FQ4
06
FQ1
07
Unadjusted bed turns
10
9
8
7
6
5
4
3
2
1
0
12/1/2006
10/1/2006
8/1/2006
6/1/2006
4/1/2006
2/1/2006
12/1/2005
10/1/2005
8/1/2005
6/1/2005
4/1/2005
2/1/2005
12/1/2004
10/1/2004
#2: Eliminate any backlogs of
work
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Initial step into ED has no BL but all other steps
have a BL (delayed workload)
Stabilize the wait, then eliminate BL
To stabilize the wait, reduce the variation
Eliminating BL may move the workload and the
wait time deeper into the system
Use standard BL reduction strategies
#3: Reduce the queues from
one entity to another
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Workload channeled into more and narrower
queues increases the risk of adversity due to
variation
“Priority” is often a euphemism for more queues
Priority 
 variation
Segment/route for “different” queues
Segment in front of the constraint, not beyond
Reduce the Queues
Concept: Bank and Grocery Store
 Are these appropriate queues?

1 line for each phlebotomist?
 “In-patients first” in Imaging?
 Preadmission unit?
 Discharge lounge?
 Separate OR for emergencies?
 “Fast Track” in ED?
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0
4/5/2004
3/22/2004
3/8/2004
2/23/2004
2/9/2004
1/26/2004
1/12/2004
25
12/29/2003
12/15/2003
12/1/2003
11/17/2003
11/3/2003
10/20/2003
10/6/2003
9/22/2003
9/8/2003
8/25/2003
8/11/2003
7/28/2003
7/14/2003
6/30/2003
6/16/2003
6/2/2003
5/19/2003
5/5/2003
4/21/2003
4/7/2003
3/24/2003
3/10/2003
2/24/2003
2/10/2003
1/27/2003
1/13/2003
(Wait Time min)
Lab
Banker’s
Queue
Implemented
20
15
10
5
#4: Develop contingency plans
to manage variation
Measure the variation
 Determine common from special
cause
 Have a plan
 Use a tool or two

Variation
Within Day
Between Day
Measure Approach:
Variation

Natural/Unplanned
Run Chart
 Statistical Process Control
 Queuing Formulas
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
Artificial/Planned
Run Chart
 Statistical Process Control
 Modeling

Tools and Theories to manage
variation

UK formula: low demand +80% of the variation

Erlang’s formula
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Standard queuing models
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Demand-Capacity Tool
Wait Time
Wait Time Variability
Compared to D/S ratio
80%
Demand / Supply
100%
Supply variation in surgery:
the 5 Why's
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Bolus of elective admissions with a delay
Surgeon(s) worked in clinic a lot
Surgeon catching up after being away
 Had to catch up/make up “on call” first
 Generated immediate surgery
 Had to do operations now and did before leaving
Call and surgery can’t wait
Clinic waits
Surgeon(s) does not fill block time
Loses block time
Back to office
Generates workload and bolus
#5: Reduce demand
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Some volume of demand can be reduced by
correct routing ( error proofing)
Impact of volume of demand can be reduced by
Service Agreements
Demand can be reduced by reduction of LOS
Admit the Right Patients
% Admissions Not Meeting Criteria 2006
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
A
C
E
G
I
K
M
O
Q
S
U
W
Y
AA
Other way to Decrease
Demand
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Increase reliability: Take on Clinical
Care
VA ICU Collaborative
 Increase safety and reliability


Decrease those patients off service:
One study shows mortality increases
25% for patients “off service”.
Hospital within a hospital
Hospitalist
Hospitalist Change
And
Joint N-P Rounds
Yearly savings estimated at 300 bed-days of care!
Decreasing Demand by
Shortening LOS
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Have a senior clinician review each
admission critically to have a clear
workup or treatment plan and timeline
Multidisciplinary Rounds Daily
Round twice a day
Rounds checklist
Plan discharge at time of admit
Eliminate waits for any ancillary services
#6: Increase supply
If there is a D- S mismatch, then
add supply- permanently or
temporarily with flex
 Can “add” supply by subtraction
 TOC: identify supply constraint in
entire flow and at each step in the
flow and take away the
“unnecessary” workload

Supply Components

Supply Components
Patient
 Provider of care
 Staff
 Beds
 Information
 Equipment
 Supplies

Increase Supply
Link admissions to discharges
 Do a “wasted inpatient bed” study

Snapshot measure
 Physically confront each bed
 Note if bed empty of full
 Note reason for empty bed
 Tabulate % of time bed wasted
 Track over time

Reasons for Inpatient Bed
Not in Productive Use
Reasons for Unused Bed
#
1. Pt. receiving care (elsewhere) (OK)
2. Pt. in discharge process
(OK)
3. Pt. disch complete, waiting to go
4. Bed needs to be cleaned
5. Bed held for surgical
6. Bed held for admission/transfer
7. Bed contains a body
8. Bed out of service (why?)
9. Bed empty. No demand today
One Hospital Results
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Hospital had high turns and high
utilization
Looked closer at capacity
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Found 25% of capacity was “wasted” (bottom
7 of 9 reasons on study)
Most common reason pt. waiting to go home
Second: Bed waiting to be cleaned
Third: Held for surgical
Identify the System
Constraint
•
The constraint is the rate limiting step
•
Can only go as fast as slowest step or the slowest step
within the step
•
Any step or service that is 100% full to capacity will be
the constraint
•
This is the last place where there is a system delay
•
This is where there is a demand–supply mismatch and
a delay
•
Take work away from the constraint
•
Balance at the constraint (may have to reduce demand,
increase supply or improve the process delay)
•
The constraint shifts
Potential Constraints
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Dependent on the specific patient flow
map
Rate limiting step in that specific flow
Last place where there is a significant
delay
Examples: surgery, test/procedure, ICU,
hospital bed, office appointment, ED
The constraint moves
Example: Surgery OR
Supply at Surgery
A. Surgeon
D
B. Room
C. Equipment
F
S
B
C
D
E
D. Technician
E. Anesthesiologist
F. Hospital Bed
A
Harmonic convergence
of components
Surgeon as Constraint
Value stream for customer
 Competing venues for
surgeon/dilution
 Decision about venues
 Limit the demand/enhance the
supply at each competing venue

OR Itself
Hours of operation
 OR utilization during those hours
 Percent utilization
 Block time
 Industrial models (85%)
 Downstream constraint (bed) may
make OR appear to be the constraint
or increase wait to OR

Big System Flow/Cancer
External
Demand
Test
PC
Test
Test
SC
MDT
Surgery
Follow
up
Radiation
Discharge
Follow
up
Chemo
Bed
Oncology
Test
MDT
MDT
C
• 1-7 day variable wait
Findings
The constraint was not surgery
 The octanes at SC office were very
poor creating an office anti-dilutional
effect
 Surgeon not in the OR
 Long wait for surgery
 OR’s open
 MDT caused 2 delays

Changes





Used principles
Changed MDT ( false demand + timing)
Looked at value: Surgery
Moved surgeons to the OR
Identified constraint (front door)

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

Changed octanes
Initial/total
Surgery/initial
Created linkage
Other Potential Constraints





ED (wait time to get out, wait time to get
in)
ICU ( wait time to get in, wait time to get
out)
Beds ( wait time to get in, wait time to
get out)
Dependencies
Discharge venues ( wait time to get in)
#7: Synchronization of all supply
components to the demand
Synchronize the Work
What is synchronization?
 “Gap” between possible and actual
time of occurrence of:

Admission
- Tests
 Discharge
- Procedure
 Rounds
- Operation
 Medicine Passes

Increase Supply “Discharge by 11 is absurd.
Story of Freddie
 Problem:

It assumes everyone is out there
with their nose pressed to the glass
wanting to come in at 11, which is absurd.”
- IHI Faculty
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
1
3
5
hour
7
9
11
13
Total Admissions
15
17
19
21
23
Total Discharges
VA FY ’05 # Admits/Discharges
by Time of Day
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
1
3
5
hour
7
9
11
13
Total Admissions
15
17
19
21
23
Total Discharges
Actual and Modeled Discharges vs. Admissions
VA Nationwide 2005
16.0%
N
O
O
N
14.0%
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
0.0%
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Hour of Day
Admit % by Hour
Discharge % by Hour
Model Discharge % by Hour
23
24
Multidisciplinary Rounds:
Where?
Multidisciplinary Rounds
Outcomes
Reduced mortality
 Improve clinical care



Improved Efficiency


Vent days, infections, readmits,
decubiti ulcers, prevent DVT,
Reduced length of stay/increase
throughput
Increase patient/staff satisfaction
Daily Goals Sheet
M
Pain?
Test/Proc?
Activity?
Med Chg?
Lines/tube
s?
Spiritual?
Nutrition?
Disch
T
W
TH
F
Sa
#8: Predict and anticipate
needs
Communication strategies
 Command Center Philosophy
 Tools/Information System

Allows view of workload flow
retrospectively and in real time
 Allows for a required interventions


Czar or Czarina
Flow system scorecard
Retrospective data
 Monitors ongoing performance
 Can be converted to run charts and
SPC graphs
 Explicit focus on delay: input,
throughput and output
 Additional focus on matching and
velocity measures

DEPARTMENT
Overall 2007
MEASURE
Routine Access/Input
Throughput/Cycle Time
4.0
Measure
Goal
Actual
Measure
Goal
Actual
Emergency
Department
Door to Doctor
30 min
30.0
Visit Cycle Time
105 min
131.0
3rd Next
Available
Appointment
3 days
0.2
Admit To In
Room
120 min
132.0
3 days
2.2
Admit To Unit
Cut
60 min
41.5
60 min
19.8
15 min
10.9
3.0 hrs
88.0
1.0 hr
74.1
Surgery 6
Surgery West
Chest X-ray
Vascular Studies
Order To Exam
start
Exam Start To
Complete
ACD
Number of Parked
Patients > 30 minutes
0
0
2.1 days
2.3
1 South
Time Patient M eets
Aldrete Score To
Time Patient
Transferred
15 min
#DIV/0!
3.9 days
3.7
0
0
2.7 days
2.9
4 Medical
Number of
Parked
Patients > 30
minutes
0
6
3.7 days
3.6
Maternity
Direct Admit
N/A
2.7 days
3.4
8 Ortho
Time Patient M eets
Aldrete Score To
Time Patient
Transferred
15 min
#DIV/0!
3.3 days
3.0
Pediatriacs
Number of Parked
Patients > 30 minutes
0
0
2.1 days
1.9
2 South
ALOS
DEPARTMENT
Overall 2007
MEASURE
Routine Access/Input
4.0
Measure
CSSU
Direct Admit
8 Short Stay
Discharge Order
To Flo o r
Goal
Throughput/Cycle Time
Actual
Measure
Goal
Actual
N/A
Time Patient Arrives
Until Time Patient
Goes For Procedure
90 min
137.0
15 min
Time Patient Arrives
Until Discharge
Criteria M et
90 min
Housekeeping
Time P atient
Leaves Ro o m To
Time
Ho usekeeping
No tified
7.5 min
17.0
Time
Ho usekeeping
No tified To Time
Ro o m Clean
32.5 min
GI Lab
3rd Next A vailable
A ppo intment
3 days
#DIV/0!
A dmit To Start Of
pro cedure
39.1
#DIV/0!
Predicted Orthopedics Demand
Common and Special Cause Variation
BY DAY (Tuesday)
Common
Special
Green
Yellow
Orange
Red
Census
0-8
9-11
11-12
>12
Unplanned
0-7
8
9
>9
Transfers
0
1
2
>2
Electives
0-5
5-7
8
>8
0
0
1
>1
Planned
Parked
Demand/Supply Tool
Green
Yellow
Orange
Red
#REF!
70 or less
71 - 77
78 - 84
85 or more
# REF!
0
1-2
3-4
5 or more
# REF!
# REF!
# REF!
0-2
0-1
0
3-5
2
1
6-9
3
2
10 or more
4 or more
3 or more
Total Cares
Frequently Monitored
Patients
Complex Teaching
Isolation Patients
Uncovered 1:1
# REF!
10
20
30
40
# REF!
0-1
2
3
4 or more
# REF!
# REF!
# REF!
0-1
0 -1
0
2
2
1
3
3
2
4 or more
4 or more
3
Low Complexity
Moderate Complexity
High Complexity
# REF!
# REF!
# REF!
10
0-3
0-2
20
4-6
3
30
7
4
40
8 or more
5 or more
Observed
Data
DEMAND
Census
Admissions
Unplanned
Planned
Transfers into Dept
Electives
Parked
Acuity
Discharges
Demand/Supply Tool
Green
Yellow
Orange
Red
#REF!
3.5 or fewer
3.6 - 4.0
4.1 - 4.5
4.6 or more
#REF!
4.5 or fewer
4.6 - 5.0
5.1 - 5.5
5.6 or more
#REF!
5.5 or fewer
5.6 - 6.0
6.1 - 6.5
6.6 - 7.0
#REF!
6.5 or fewer
6.6 - 7.0
7.1 - 7.5
7.6 - 8.0
# REF!
70 or less
70-75
75-80
80 or More
# REF!
3
2
1
0
# REF!
2
1
0
0
Hospital-Wide
Computer Network
# REF!
None
IV's Used
# REF!
0-5
6
7
>8
# REF!
# REF!
3
2
2
1
0
1
0
0
Observed
Data
SUPPLY
Staffing
Primary RN Staffing
Day & PM Shifts
Primary RN Staffing
Night Shift
CNA Staffing
Day & PM Shifts
CNA Staffing
Night Shift
Beds
Total Beds Used
Isolation Rooms
Female Rooms
Information
System Down
1 hour
< System Down
2.5 hours
1- System Down
2.6
hours or more
Equipment
Supplies
Isolation Rooms
Female Rooms
Key to D/S Tool: Interventions
(Contingencies)








Created by the front line team
Exist for every single box/criteria
Impact of intervention determined by escalating
color criteria
Thresholds for criteria set by SPC, common and
special cause variation
Have the same level of impact across all
“departments”
Continually updated and developed
Allow a standard response to change in
workloads
Often result in “buddy” departments
#9: Optimize environment
Optimize the Environment

Lean
Big system flow: special issues

Linkages and formulas

Intersections
Formula




Demand for OR time = Supply of the OR
time
The delay is thus stabilized
Patients per month x octane (surgical
cases per 100 patients) = OR time per
month/OR time per case
Adjust by 85% (myth of 100% utilization)
Linkage or Ratio of Schedule
A
C
4:4:1:1
Linkage of Ratios

Why?
Work Backwards to Office



Stabilize the wait time in the OR
( the constraint)
Work backwards to the office
Three variables:



Days in office
Appointment lengths/ types
Octane
Octanes
Goals
Initial(new):initial+return
 Returns : surgery
 Surgical cases:new(initial

• Surgical Yield
High
Low
High
Linkage Formula
OR sessions/week X cases per OR
session =
Office sessions per week X
appointments per session X octane
(surgical yield)
(adjusted to 85%)
Links surgery (ultimate value and
constraint) to office that feeds the
constraint
 Allows measurement and monitoring
 Allows earlier identification of
problems

Linkage
Surgery to office
 Procedure to office
 Test to office
 Bed to ED
 ICU to surgery
 Bed to ICU
 Other dependent services

Big System
Flow/Intersections
External
Demand
Test
PC
Test
Test
SC
MDT
Surgery
Follow
up
Radiation
Discharge
Follow
up
Chemo
Bed
Oncology
Test
MDT
Intersections
Radiation Oncology
 Demand
• External.internal
• Stratification into who, what, where in each stream
• Variation within each stream

Supply

What is the constraint
•
•
•
•

Machine
Technician
Physician
Process
Delay
• For all competing components

Decisions
• Who goes first
Intersections
Intersections of demand streams are
common
 Demand is competing
 The competitors are blind
 Demand and supply are matched
but what is the model?
 How are decisions made
 What is the true constraint

Lesson 1
Value stream
 Delay is key
 We must measure demand and
variation at each step
 Do not confuse activity with demand
 Variation creates queues
 Do not use averages
 Constraints governs the speed

Lesson 2
Aiming for 100% utilization and
setting the supply at average
demand will result in waits and a
waiting list
 Set supply at minimum demand +
80% of the variation

Lesson 3







Demand/supply
Use principles at each step
The system is linked
Carve-outs worsen system performance
Extra capacity comes from process
redesign
May have to increase actual resource but
only after measurement
We may solve a problem but if it is not the
right problem we just move the wait
High Leverage Changes at
Each Step









Balance upstream and downstream demand and supply
for all services
Eliminate any backlogs of work
Reduce the queues from one entity to another
Develop contingency plans to address all variation
Reduce demand
Identify and manage each supply constraint
Synchronize the work
Predict and anticipate needs
Optimize the environment: equipment, staff and space
Ten Flow Rules
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)
Follow the customer (patient).
The goal is to eliminate all wait and delay.
The patient’s journey through the system is often complex but at its core is a
series of value steps interspersed with long waits.
Each step is a demand-supply matching step.
The perspective of the customer (demand) is different than the perspective of
the supply (resource) : the patient experiences a series of waits while the
resource sees single isolated waits.
Queues (wait times) result from:
a) demand-supply mismatch which has to be solved by reducing
demand or enhancing supply
b) queues are formed by system design which requires redesign, or
c) queues are formed by variation which needs to be measured and
addressed
Measurement of demand, supply, activity, wait time and variation in demand
and supply at each step is crucial
Involve all staff in measurement at each step.
Look at steps beyond the constraint to improve flow.
There are a set of principles that, if applied appropriately at each step, will
reduce the waits.