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1
EFFECT OF DAILY LEVELING
OF ELECTIVE-SCHEDULED SURGERIES
ON ICU DIVERSION:
PREDICTIVE MODELING
& TRADE-OFF SCENARIOS
Alexander Kolker, PhD
Froedtert Hospital
Milwaukee, Wisconsin
Froedtert Hospital, Milwaukee WI
• Primary teaching Hospital for MCW
• Tertiary Referral Center
• Level 1 Trauma Center for SE Wisconsin
• 433 staffed acute care beds
• 23,617 admissions/ 47,176 ED Visits
• 454,780 Outpatient Clinic Visits
• Surgeries - Inpt: 9,034/ Outpt: 5,711
2
PROBLEM STATEMENT:
• Elective surgeries are usually scheduled for Operating
Rooms block-time without taking into account the
competing demand from emergency and add-on surgeries
for ICU resources:
• ICUs bed capacity and
• ICU ability to handle the patients’ flow.
This practice results in:
• increased ICU diversion due to ‘No ICU beds’
• Increased rate of medical and quality issues due to staff
over-load and capacity constraints
• decreased patient throughput and the hospital revenue
The main Goal of this work was:
• Develop a quantitative methodology to address the issue :
How many elective surgeries per day should be scheduled
given the number of unscheduled emergency and add-on
surgeries in order to reduce the percentage of time
due to ‘no ICU beds’ to an acceptable low level ?
3
What Quantitative Methodology could be used to
analyze patient flow?
There are two methodologies based on principles of
Operations Research.
QUEUING THEORY:
Applied to analyze Random demand and Fixed capacity.
Main features:
• Uses pre-determined simplified models for which closed
mathematical formulas could be derived
• Assumes that arrivals form a Poisson process, and the
service time is exponentially distributed (sometimes
uniformly or Erlang distributed).
• cannot be applied if the arrivals contain a statistically non-
random component, such as elective surgeries (which is the
primary focus of this work).
What Quantitative Methodology could be used to
analyze patient flow (cont.) ?
PROCESS MODEL SIMULATION:
Applied to mimic the dynamic behavior of a complex system over
the time.
Main features:
• Free from assumptions of the particular type of the arrival
process and the service time
• The system structure (flow map) could be of any complexity
• Custom action logic can be built in to reflect any level of
details of the actual patient flow.
• The patient flow can be visualized in time
4
Patient Throughput Process high level flow map
Take-away:
• Each subsystem is related to another subsystem, through its patients' flows in and out .
• Active management of the flows allows to control the entire system throughput
ED ICU
Inpatient
Beds OPPR
Recovery in
Procedure
area
Level (3)
Day Surgery
(level 2)
PACU
(level 1)
Home
OR
Urology
GI
MRI/CT
Cath Lab
IR
EMT diversion
Patients flow
into ED
Other
Hospitals
Level 4
Home
Elective +
Emergency +
Adds-on surgeries
Two types of variability affect the system’s patient flow:
Type 1:
• Natural process flow variability: Emergency and adds-on procedures
(surgeries)
• Statistically Random in nature
• Beyond Hospital control: cannot be eliminated (or even reduced)
• However some statistical characteristics can be predicted over a
long period of time
Type 2:
Artificial Variability: Elective-scheduled procedures
• Non-statistically random
• Driven mostly by individual priorities
• Within Hospital management system control: can be reduced or
eliminated
5
THE FOLLOWING STEPS HAVE BEEN PERFORMED:
• Collected data on the number of surgeries for 4 months time period, June 1 to
Sep 30, ’06 (same day direct ICU admissions), and drilled down
• by all Mondays, all Tuesdays, and so on.. (to analyze an aggregate effect of
all DOWs)
Then, drilled down data by:
Monday to Monday, Tuesday to Tuesday, and so on…
(to analyze the variation for the same days of different weeks that could be
smoothed )
• Using Process Model Simulation, analyzed an effect of ‘smoothing’ of the
elective cases (daily load leveling) by moving some cases from ‘busy’ Mondays to
‘light’ Mondays, from ‘busy’ Tuesdays to ‘light’ Tuesdays, and so on…
•Established the maximum number of elective cases per day that resulted in the
significant reduction of the percentage of time of ‘no ICU beds’.
The focus of this work was the elective-surgeries schedule
that causes artificial (type 2) flow variability
ARE SAME DAY ADMISSIONS ELECTIVE CASES A BIG PORTION
OF THE TOTAL ICUs ADMISSIONS ?
21.2%0.76%0.4%8%12%% of Total ICU
Admissions
(1848 adm)
393148150221Elective cases
TotalCICMICNICSICICUs
21.2%0.76%0.4%8%12%% of Total ICU
Admissions
(1848 adm)
393148150221Elective cases
TotalCICMICNICSICICUs
Take-away:
• Elective cases represent a big enough portion of the total ICU admission, ~21%
• Analysis of leveling is focused on SIC and NIC.
• Because MIC and CIC elective cases percentages are very small, they are not
included in the analysis
6
Take-away:
Most elective cases appear to be scheduled on Mondays while only a few cases scheduled on Saturdays
DO W
Count
S atF riWedT ueT huM on
60
45
30
15
0
DO W
Count
S atWedT huF riT ueM on
40
30
20
10
0
1
24
464747
59
2
17
26
29
31
46
SIC by all DOWs. June 1 to Sep 30. (Total 221)
NIC by all DOW. June 1 to Sep 30. (Total 150)
Total number of elective cases scheduled by aggregated Days of Week (DOW):
SIC and NIC
M on date
byMon
9/259/189/119/48/288/218/148/77/317/247/177/107/36/266/196/126/5
9
8
7
6
5
4
3
2
1
0
T ue date
byTue
9/269/199/129/58/298/228/158/88/17/257/187/117/46/276/206/136/6
8
7
6
5
4
3
2
1
0
4
1
4
0
4
2
3
2
5
3
5
2
3
2
6
5
8
22
3
1
33
5
4
22
4
2
0
1
2
7
4
SIC by Mondays
SIC by Tuesdays
The number of elective cases for the same DOWs for different weeks
Baseline (from 8 to 0 cases): variation (std) = 1.97 cases
Baseline (from 7 to 0 cases): variation (std) = 1.68 cases
Take-away: There is a significant variation in scheduling elective cases from Mon to Mon, from Tue to
Tue. This results in straining the system on busy days and under use the system on light days
7
Wed date
byWed
9/279/209/139/68/308/238/168/98/27/267/197/127/56/286/216/146/7
7
6
5
4
3
2
1
0
T hu date
byThu
9/289/219/149/78/318/248/178/108/37/277/207/137/66/296/226/156/86/1
7
6
5
4
3
2
1
0
2
3
2
1
6
3
2
3
1
5
0
3
6
0
1
2
6
22
1
4
5
222
0
2
1
5
3
2
4
0
4
6
SIC by Wednesdays
SIC by Thursdays
Baseline (from 6 to 0 cases): variation (std) = 1.99 cases
Baseline (from 6 to 0 cases): variation (std) = 1.72 cases
ICU Process Model Simulation
Layout
Patient move
between the units:
•If NO beds in CIC
move to SIC
•If NO beds in MIC
move to CIC else SIC
else NIC
•If NO beds in SIC
move CIC
•If NO beds in NIC
move to CIC else SIC
The use of the Simulation to model Elective cases scenarios
8
The use of the Simulation to model Elective cases scenarios
Example:
wk2, Monday, adm_from=OpR, 1:05 pm
Procedure_type=ELS (Elective Surgery)
How adequate is the model ?
Time period EMT data Process Model Simulation
1 month, June 1 to
June 30, '06 6% 8%
2 months:
June 1 to July 31,'06 12% 13.0%
3 months:
June 1 to August
31,'06 10% 13.0%
4 months:
June 1 to September
30,'06 8% 10.0%
% ED diversion due to NO ICU beds
Take-away:
EMT and simulated % diversion for the various time periods are close enough (in the
range of a few percentage points). Thus, the model captures characteristics of the ICU
patients’ flow adequately enough to represent the system’s behavior
9
The use of the Process Model to play scenarios
BASELINE - EXISTING NUMBER OF ELECTIVE CASES
Take-away:
When census exceeds the critical limit (2 beds short of ICU being full) ICU can be closed
due to ‘no ICU beds’. ICU stays closed until its census drops below the critical limit
ICU Census:
Elective surgeries current pattern - No daily cap
Closed due to No ICU beds: 10.5 % of time
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
0 168 336 504 672 840 1008 1176 1344 1512 1680 1848 2016 2184 2352 2520 2688 2856 3024
Hrs/ weeks
cns
Red zone:
Critical census limit exceeded
wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 wk12 wk13 wk14 wk15 wk16 wk17 wk18
The use of the Process Model to play scenarios
Phase 1. SIC cap to 5 daily cases, NIC cap to 4 daily cases
Mon date
byMon
9/259/189/119/48/288/218/148/77/317/247/177/107/36/266/196/126/5
9
8
7
6
5
4
3
2
1
0
Tue date
byTue
9/269/199/129/58/298/228/158/88/17/257/187/117/46/276/206/136/6
8
7
6
5
4
3
2
1
0
4
1
4
0
4
2
3
2
5
3
5
2
3
2
6
5
8
22
3
1
33
5
4
22
4
2
0
1
2
7
4
SIC by Mondays
SIC by Tuesdays
Cap = 5
Cap = 5
Example of shifting day to day elective cases:
Cases that exceed daily cap moved to the nearest next day
with minimum number of scheduled cases
10
The use of the Process Model to play scenarios
Phase 1. New schedule: SIC cap 5 daily cases, NIC cap 4 daily cases
Take-away: leveling to SIC 5 daily cases results in a smoother schedule. Variation (std) reduced by
~28% (Mon) and ~24% (Tue) respectively vs. baseline. This makes the system to easier handle the flow
M on date
byMon
9/259/189/119/48/288/218/148/77/317/247/177/107/36/266/196/126/5
4.8
3.6
2.4
1.2
0.0
T ue date
byTue
9/269/199/129/58/298/228/158/88/17/257/187/117/46/276/206/136/6
4.8
3.6
2.4
1.2
0.0
4
1
4
0
4
333
5
3
5
333
555
22
3
2
33
5
4
22
4
2
0
22
5
4
SIC by Mon
C A P = 5
SIC by Tuesdays
C A P = 5
Daily Cap 5 (from 5 to 0).
Variation (std)=1.42 cases
Daily Cap 5 (from 5 to 0).
Variation (std)=1.30 cases
We d da te
byWed
9/279/209/139/68/308/238/168/98/27/267/197/127/56/286/216/146/7
4.8
3.6
2.4
1.2
0.0
T hu da te
byThu
9/289/219/149/78/318/248/178/108/37/277/207/137/66/296/226/156/86/1
4.8
3.6
2.4
1.2
0.0
2
3
22
5
3
2
3
2
5
0
3
5
0
22
5
22
1
4
5
222
0
2
1
5
33
4
0
4
5
SIC by Wednesdays
C A P = 5
SIC by Thursdays
C A P = 5
The use of the Process Model to play scenarios
Phase 1. New Schedule: SIC cap to 5 daily cases, NIC cap to 4 daily cases
Take-away: leveling to SIC 5 daily cases results in smoother schedule. Variation (std) reduced by ~21%
(Wed) and ~6 % (Thu) respectively vs. baseline. This makes easier to handle the flow ….
Daily Cap 5 (from 5 to 2).
Variation (std)=1.57 cases
Daily Cap 5 (from 5 to 2).
Variation (std)=1.61 cases
11
ICU Census:
Elective surgeries daily cap: SIC = 5, NIC = 4
ICU is closed due to No ICU beds: 3.5% of time
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
0 168 336 504 672 840 1008 1176 1344 1512 1680 1848 2016 2184 2352 2520 2688 2856 3024
Hrs, wks
cns
The use of the Process Model to play scenarios
Phase 1. New schedule: SIC cap 5 daily cases, NIC cap 4 daily cases
Take-away:
leveling SIC and NIC daily cases (re-scheduling elective cases for another DOW with the same block-time)
results in reduction of the overall time when census exceeds the critical limit. This, in turn, results in
reduction ED diversion down to 3.5%.
Total number of cases over the same time period stays the same !!
Red zone:
Critical census limit is exceeded fewer number of
times for shorter period
wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 wk12 wk13 wk14 wk15 wk16 wk17 wk18
The use of the Process Model to play scenarios
Phase 1. New Schedule: SIC cap to 4 daily cases, NIC cap to 4 (or 3) daily cases
Take-away: leveling to SIC 4 daily cases results in even smoother schedule. Variation (std) reduced by
~40% (Mon) and ~35 % (Tue) vs. baseline. This further helps the system to easier handle the flow….
M on date
byMon
9/259/189/119/48/288/218/148/77/317/247/177/107/36/266/196/126/5
4
3
2
1
0
T ue date
byTue
9/269/199/129/58/298/228/158/88/17/257/187/117/46/276/206/136/6
4
3
2
1
0
4
1
4
0
4
33
4444444444
22
3333
44
22
4
2
0
2
3
44
SIC by Mon
C A P = 4
SIC by Tuesdays
C A P = 4
Daily Cap 4 (from 4 to 2).
Variation (std)= 1.18 cases
Daily Cap 4 (from 4 to 2).
Variation (std)= 1.09 cases
12
We d date
byWed
9/279/209/139/68/308/238/168/98/27/267/197/127/56/286/216/146/7
4
3
2
1
0
T hu date
byThu
9/289/219/149/78/318/248/178/108/37/277/207/137/66/296/226/156/86/1
4
3
2
1
0
2
3
2
3
4
3333
4
0
3
4
0
2
3
4
222
44
222
0
22
4
3
44
0
44
SIC by Wednesday s
C A P = 4
SIC by Thursdays
C A P = 4
The use of the Process Model to play scenarios
Phase 1. SIC cap to 4 daily cases, NIC cap to 4 (or 3) daily cases
Take-away: leveling to SIC 4 daily cases results in even smoother schedule. Variation (std) reduced
by ~39% (Wed) and ~23 % (Thu) vs. baseline. This further helps the system to easier handle the flow.
Daily Cap 4 (from 4 to 2).
Variation (std)= 1.21 cases
Daily Cap 4 (from 4 to 2).
Variation (std)= 1.33 cases
ICUs census:
Elective surgerydailycap:SIC =4, NIC = 4
ICU closed due to No ICU beds:1.5% of time
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
0 168 336 504 672 840 1008 1176 1344 1512 1680 1848 2016 2184 2352 2520 2688 2856 3024
hrs/wks
cns
The use of the Process Model to play scenarios
Phase 1. SIC cap 4 daily cases, NIC cap 4 (or 3) daily cases
Take-away: Additional leveling SIC and NIC daily cases (re-scheduling elective cases for
another DOW with the same block-time) results in further reduction of the overall time when
census exceeds the critical limit. This, in turn, results in further reduction of the closure
time down to 1.5%. Total number of cases over the same time period stays the same !!
Red zone:
Critical census limit exceeded
even fewer number of times and for shorter period
wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 wk12 wk13 wk14 wk15 wk16 wk17 wk18
13
Did we find the best solution to implement by daily capping at
four cases per day ?
NOT YET…..
• Not all patients can wait too long for their surgery, sometimes up to 2
months !!
• Surgeons and physicians requested modeling scenarios in which
‘bumping’ was not further than 2 weeks apart (2 weeks waiting)
•Therefore Phase 2 scenarios have been considered:
• ‘Bump’ extra cases to the next available day with the same block-time for a different
week which is not further than 2 weeks apart.
• Keep daily load leveling at a higher level (five) for easier practical implementation
M o n d a t e
byMon
9 /2 59 /1 89 /1 19 /48 /2 88 /2 18 /1 48 /77 /3 17 /2 47 /1 77 /1 07 /36 /2 66 /1 96 /1 26 /5
8
7
6
5
4
3
2
1
0
M o n d a t e
byMon
9 /2 59 /1 89 /1 19 /48 /2 88 /2 18 /1 48 /77 /3 17 /2 47 /1 77 /1 07 /36 /2 66 /1 96 /1 26 /5
8
7
6
5
4
3
2
1
0
4
1
4
0
4
2
3
2
5
3
5
2
3
2
6
5
8
4
1
4
0
4
2
3
2
5
3
5
2
3
2
6
5
8
S I C b y M o n d a y s
S I C b y M o n d a y s
B u m p e d e x t r a c a s e s t o m i n i m i z e o v e r a l l v a r i a b i l i t y
B u m p e d e x t r a c a s e s w i t h i n 2 w e e k s a p a r t
4
5
Two ways of ‘bumping’ extra cases for load-leveling (Monday to Monday example)
Take-away:
• Load leveling 5 cases/day with bumped extra cases within 2 weeks apart resulted in a relatively
small reduction of diversion: from 10.5% (no load leveling-baseline) to only about 8%.
• This low effectiveness was because bumping within only 2 weeks apart resulted in a higher flow
variability (std=1.59 cases) than the previous bumping aimed at minimizing the overall flow
variability (std= 1.42 cases). Note, that the no load-leveling (baseline) flow variability was the
highest, std =1.97 cases.
• Thus, load leveling to 5 elective cases/day with bumped extra cases within 2 weeks apart
was not effective enough alone.
14
Because load leveling to 5 elective cases/day with bumped extra cases within 2
weeks apart was not effective enough alone, Phase 3 has been considered:
Phase 3:
Exclude ICU admissions for patients who stayed less than 24 hrs.
Combine with Phase 2.
PlasticGynTrauma-GenOrthoTransplantAnesthesiaGUVascularGeneral
6
5
4
3
2
1
0
service
Count
11
22
33
5
66
Services that meet ICUs admitting exclusion criteria
Exclusion criteria: patients stayed less than 24 hrs
General and Vascular patients most frequently
stayed less than 24 hrs
Phase 3 Take-Away:
• Dramatic reduction in ICU closure time due to 'No ICU beds':
down to about ~ 1 %.
Summary of simulated ‘what-if’ scenarios
SIC
&
NIC
cap
5
SIC
&
NIC
cap
5
SIC
&
NIC
cap
4
SIC
cap
5, NIC
cap
4
SIC
cap
6, no
NIC
cap
current pattern-baseline
12
10
8
6
4
2
0
%ICUDiversion
1.0
8.0
1.5
3.5
8.9
10.5
ICU
adm
ission
criteria
applied
Take-away:
Smoothing the number of elective cases over time (daily load leveling) is a
very significant factor which strongly affects ICU time closure due to ‘no ICU
beds’.
Move ‘extra’ cases
within 2 weeks
Predicted % of time
ICU was closed
15
SUMMARY
•There is a significant variation of the number of scheduled elective cases
between the same days of the different weeks (Monday to Monday,
Tuesday to Tuesday, and so on..)
• Smoothing the number of elective cases over time (daily load leveling) is
a very significant factor which strongly affects ICU closure time due to ‘no
ICU beds’.
• Using Process Model Simulation, it has been demonstrated (Phase 1)
that daily load leveling of elective cases to not more than 4 cases per day
will result in a very significant reduction of closure time due to ‘no ICU
beds’
(from ~10% down to ~1%).
However this solution (based on max reduction of the overall variability)
requires ‘bumping’ extra cases up to 8 weeks ahead.
Not all patients could wait that long for their elective surgeries.
SUMMARY (cont)
An alternative solution (Phase 2) has been simulated:
‘bumping’ extra cases to NOT more than 2 weeks apart and keeping daily
leveling at 5 cases/day.
However this solution was not effective enough alone: ICU closure time
was down from~10% to only ~8%.
• Therefore another alternative solution (Phase 3) has been simulated:
combining Phase 2 and exclusion ICU admissions for patients stayed
less than 24 hrs in ICU (ICU admission criteria applied).
This solution was effective enough (closure time was less than 1%).
It was considered for the practical implementation as a pilot project.
• There is a trade-off between these two solutions:
from the practical standpoint the higher level-loading (5 cases/day) would
be easier to implement than the lower level-loading (4 cases/day).
However the former assumes that ICUs admission criteria are strictly
followed while the later does not require exclusion from the current ICU
admitting pattern
16
• All three components affect the flow of patients that the system can handle.
• A lack of the proper balance between these components results in the
system’s over-flow and closure/diversion
• Process Model Simulation methodology provides the only means of
analyzing and managing the proper balance in the complex systems
OVERALL METHODOLOGY SUMMARY
• Patient Throughput flow is an example of the general dynamic Supply &
Demand problem. This is not a one-time snapshot. System’s behavior
depends on time.
• There are three basic components that should be accounted for in this type
of problems:
• The number of patients (or, generally, any items) entering the system at
any point of time
• The number of patients (any items) leaving the system at any point of
time after spending some time in the system
• Limited Capacity of the system which limits the flow of patients through
the system
APPENDIX
17
WHAT IS THE PROCESS MODEL ?
•It is a computer model that mimics the dynamic behavior of a real
process over the time in order to visualize and quantitatively analyze
its performance in terms of:
•Cycle times
•Throughput capacity
•Resources utilization
•Activities utilization
•It is a tool to perform ‘WHAT-IF’ analysis and play different
scenarios of the model behavior as conditions and process
parameters change.
This allows to make experiments on the computer model, and test
different solutions (changes) for their effectiveness before going to
the floor for the actual implementation.
WHAT ARE THE BASIC ELEMENTS OF THE PROCESS MODEL?
•Flow chart of the process: Diagram that depicts logical flow of a process
from its inception to its completion
•Entities: Items to be processed: patients, documents, customers, etc.
•Activities: Tasks performed on entities: medical procedures,
document approval, customer check out, etc
•Resources: Agents used to perform activities and move entities: service
personnel, operators, equipment, nurses, physicians.
•Connections:
•Entity arrivals: Define process entry points, time, and quantities of the
entities that enter the system to begin processing
•Entity routings: Define directions and logical conditions flow for entities
18
WHAT INFORMATION (DATA) IS REQUIRED TO FEED THE MODEL ?
•Entities quantities and arrival time: periodic, random, scheduled, daily
pattern, etc
•The time that the entities spend in the activities. This is usually not a
fixed time but a statistical distribution. The wider the time distribution
the higher the variability of the system behavior.
•The capacity of each activity, i.e. the max number of entities that can be
processed concurrently in the activity.
•The size of input and output queues for the activities
•The routing type or the logical conditions to take a specified routing.
•Resource Assignments: their number and availability, and/or resources
shift schedule

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hcm4-a-kolker

  • 1. 1 EFFECT OF DAILY LEVELING OF ELECTIVE-SCHEDULED SURGERIES ON ICU DIVERSION: PREDICTIVE MODELING & TRADE-OFF SCENARIOS Alexander Kolker, PhD Froedtert Hospital Milwaukee, Wisconsin Froedtert Hospital, Milwaukee WI • Primary teaching Hospital for MCW • Tertiary Referral Center • Level 1 Trauma Center for SE Wisconsin • 433 staffed acute care beds • 23,617 admissions/ 47,176 ED Visits • 454,780 Outpatient Clinic Visits • Surgeries - Inpt: 9,034/ Outpt: 5,711
  • 2. 2 PROBLEM STATEMENT: • Elective surgeries are usually scheduled for Operating Rooms block-time without taking into account the competing demand from emergency and add-on surgeries for ICU resources: • ICUs bed capacity and • ICU ability to handle the patients’ flow. This practice results in: • increased ICU diversion due to ‘No ICU beds’ • Increased rate of medical and quality issues due to staff over-load and capacity constraints • decreased patient throughput and the hospital revenue The main Goal of this work was: • Develop a quantitative methodology to address the issue : How many elective surgeries per day should be scheduled given the number of unscheduled emergency and add-on surgeries in order to reduce the percentage of time due to ‘no ICU beds’ to an acceptable low level ?
  • 3. 3 What Quantitative Methodology could be used to analyze patient flow? There are two methodologies based on principles of Operations Research. QUEUING THEORY: Applied to analyze Random demand and Fixed capacity. Main features: • Uses pre-determined simplified models for which closed mathematical formulas could be derived • Assumes that arrivals form a Poisson process, and the service time is exponentially distributed (sometimes uniformly or Erlang distributed). • cannot be applied if the arrivals contain a statistically non- random component, such as elective surgeries (which is the primary focus of this work). What Quantitative Methodology could be used to analyze patient flow (cont.) ? PROCESS MODEL SIMULATION: Applied to mimic the dynamic behavior of a complex system over the time. Main features: • Free from assumptions of the particular type of the arrival process and the service time • The system structure (flow map) could be of any complexity • Custom action logic can be built in to reflect any level of details of the actual patient flow. • The patient flow can be visualized in time
  • 4. 4 Patient Throughput Process high level flow map Take-away: • Each subsystem is related to another subsystem, through its patients' flows in and out . • Active management of the flows allows to control the entire system throughput ED ICU Inpatient Beds OPPR Recovery in Procedure area Level (3) Day Surgery (level 2) PACU (level 1) Home OR Urology GI MRI/CT Cath Lab IR EMT diversion Patients flow into ED Other Hospitals Level 4 Home Elective + Emergency + Adds-on surgeries Two types of variability affect the system’s patient flow: Type 1: • Natural process flow variability: Emergency and adds-on procedures (surgeries) • Statistically Random in nature • Beyond Hospital control: cannot be eliminated (or even reduced) • However some statistical characteristics can be predicted over a long period of time Type 2: Artificial Variability: Elective-scheduled procedures • Non-statistically random • Driven mostly by individual priorities • Within Hospital management system control: can be reduced or eliminated
  • 5. 5 THE FOLLOWING STEPS HAVE BEEN PERFORMED: • Collected data on the number of surgeries for 4 months time period, June 1 to Sep 30, ’06 (same day direct ICU admissions), and drilled down • by all Mondays, all Tuesdays, and so on.. (to analyze an aggregate effect of all DOWs) Then, drilled down data by: Monday to Monday, Tuesday to Tuesday, and so on… (to analyze the variation for the same days of different weeks that could be smoothed ) • Using Process Model Simulation, analyzed an effect of ‘smoothing’ of the elective cases (daily load leveling) by moving some cases from ‘busy’ Mondays to ‘light’ Mondays, from ‘busy’ Tuesdays to ‘light’ Tuesdays, and so on… •Established the maximum number of elective cases per day that resulted in the significant reduction of the percentage of time of ‘no ICU beds’. The focus of this work was the elective-surgeries schedule that causes artificial (type 2) flow variability ARE SAME DAY ADMISSIONS ELECTIVE CASES A BIG PORTION OF THE TOTAL ICUs ADMISSIONS ? 21.2%0.76%0.4%8%12%% of Total ICU Admissions (1848 adm) 393148150221Elective cases TotalCICMICNICSICICUs 21.2%0.76%0.4%8%12%% of Total ICU Admissions (1848 adm) 393148150221Elective cases TotalCICMICNICSICICUs Take-away: • Elective cases represent a big enough portion of the total ICU admission, ~21% • Analysis of leveling is focused on SIC and NIC. • Because MIC and CIC elective cases percentages are very small, they are not included in the analysis
  • 6. 6 Take-away: Most elective cases appear to be scheduled on Mondays while only a few cases scheduled on Saturdays DO W Count S atF riWedT ueT huM on 60 45 30 15 0 DO W Count S atWedT huF riT ueM on 40 30 20 10 0 1 24 464747 59 2 17 26 29 31 46 SIC by all DOWs. June 1 to Sep 30. (Total 221) NIC by all DOW. June 1 to Sep 30. (Total 150) Total number of elective cases scheduled by aggregated Days of Week (DOW): SIC and NIC M on date byMon 9/259/189/119/48/288/218/148/77/317/247/177/107/36/266/196/126/5 9 8 7 6 5 4 3 2 1 0 T ue date byTue 9/269/199/129/58/298/228/158/88/17/257/187/117/46/276/206/136/6 8 7 6 5 4 3 2 1 0 4 1 4 0 4 2 3 2 5 3 5 2 3 2 6 5 8 22 3 1 33 5 4 22 4 2 0 1 2 7 4 SIC by Mondays SIC by Tuesdays The number of elective cases for the same DOWs for different weeks Baseline (from 8 to 0 cases): variation (std) = 1.97 cases Baseline (from 7 to 0 cases): variation (std) = 1.68 cases Take-away: There is a significant variation in scheduling elective cases from Mon to Mon, from Tue to Tue. This results in straining the system on busy days and under use the system on light days
  • 7. 7 Wed date byWed 9/279/209/139/68/308/238/168/98/27/267/197/127/56/286/216/146/7 7 6 5 4 3 2 1 0 T hu date byThu 9/289/219/149/78/318/248/178/108/37/277/207/137/66/296/226/156/86/1 7 6 5 4 3 2 1 0 2 3 2 1 6 3 2 3 1 5 0 3 6 0 1 2 6 22 1 4 5 222 0 2 1 5 3 2 4 0 4 6 SIC by Wednesdays SIC by Thursdays Baseline (from 6 to 0 cases): variation (std) = 1.99 cases Baseline (from 6 to 0 cases): variation (std) = 1.72 cases ICU Process Model Simulation Layout Patient move between the units: •If NO beds in CIC move to SIC •If NO beds in MIC move to CIC else SIC else NIC •If NO beds in SIC move CIC •If NO beds in NIC move to CIC else SIC The use of the Simulation to model Elective cases scenarios
  • 8. 8 The use of the Simulation to model Elective cases scenarios Example: wk2, Monday, adm_from=OpR, 1:05 pm Procedure_type=ELS (Elective Surgery) How adequate is the model ? Time period EMT data Process Model Simulation 1 month, June 1 to June 30, '06 6% 8% 2 months: June 1 to July 31,'06 12% 13.0% 3 months: June 1 to August 31,'06 10% 13.0% 4 months: June 1 to September 30,'06 8% 10.0% % ED diversion due to NO ICU beds Take-away: EMT and simulated % diversion for the various time periods are close enough (in the range of a few percentage points). Thus, the model captures characteristics of the ICU patients’ flow adequately enough to represent the system’s behavior
  • 9. 9 The use of the Process Model to play scenarios BASELINE - EXISTING NUMBER OF ELECTIVE CASES Take-away: When census exceeds the critical limit (2 beds short of ICU being full) ICU can be closed due to ‘no ICU beds’. ICU stays closed until its census drops below the critical limit ICU Census: Elective surgeries current pattern - No daily cap Closed due to No ICU beds: 10.5 % of time 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 0 168 336 504 672 840 1008 1176 1344 1512 1680 1848 2016 2184 2352 2520 2688 2856 3024 Hrs/ weeks cns Red zone: Critical census limit exceeded wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 wk12 wk13 wk14 wk15 wk16 wk17 wk18 The use of the Process Model to play scenarios Phase 1. SIC cap to 5 daily cases, NIC cap to 4 daily cases Mon date byMon 9/259/189/119/48/288/218/148/77/317/247/177/107/36/266/196/126/5 9 8 7 6 5 4 3 2 1 0 Tue date byTue 9/269/199/129/58/298/228/158/88/17/257/187/117/46/276/206/136/6 8 7 6 5 4 3 2 1 0 4 1 4 0 4 2 3 2 5 3 5 2 3 2 6 5 8 22 3 1 33 5 4 22 4 2 0 1 2 7 4 SIC by Mondays SIC by Tuesdays Cap = 5 Cap = 5 Example of shifting day to day elective cases: Cases that exceed daily cap moved to the nearest next day with minimum number of scheduled cases
  • 10. 10 The use of the Process Model to play scenarios Phase 1. New schedule: SIC cap 5 daily cases, NIC cap 4 daily cases Take-away: leveling to SIC 5 daily cases results in a smoother schedule. Variation (std) reduced by ~28% (Mon) and ~24% (Tue) respectively vs. baseline. This makes the system to easier handle the flow M on date byMon 9/259/189/119/48/288/218/148/77/317/247/177/107/36/266/196/126/5 4.8 3.6 2.4 1.2 0.0 T ue date byTue 9/269/199/129/58/298/228/158/88/17/257/187/117/46/276/206/136/6 4.8 3.6 2.4 1.2 0.0 4 1 4 0 4 333 5 3 5 333 555 22 3 2 33 5 4 22 4 2 0 22 5 4 SIC by Mon C A P = 5 SIC by Tuesdays C A P = 5 Daily Cap 5 (from 5 to 0). Variation (std)=1.42 cases Daily Cap 5 (from 5 to 0). Variation (std)=1.30 cases We d da te byWed 9/279/209/139/68/308/238/168/98/27/267/197/127/56/286/216/146/7 4.8 3.6 2.4 1.2 0.0 T hu da te byThu 9/289/219/149/78/318/248/178/108/37/277/207/137/66/296/226/156/86/1 4.8 3.6 2.4 1.2 0.0 2 3 22 5 3 2 3 2 5 0 3 5 0 22 5 22 1 4 5 222 0 2 1 5 33 4 0 4 5 SIC by Wednesdays C A P = 5 SIC by Thursdays C A P = 5 The use of the Process Model to play scenarios Phase 1. New Schedule: SIC cap to 5 daily cases, NIC cap to 4 daily cases Take-away: leveling to SIC 5 daily cases results in smoother schedule. Variation (std) reduced by ~21% (Wed) and ~6 % (Thu) respectively vs. baseline. This makes easier to handle the flow …. Daily Cap 5 (from 5 to 2). Variation (std)=1.57 cases Daily Cap 5 (from 5 to 2). Variation (std)=1.61 cases
  • 11. 11 ICU Census: Elective surgeries daily cap: SIC = 5, NIC = 4 ICU is closed due to No ICU beds: 3.5% of time 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 0 168 336 504 672 840 1008 1176 1344 1512 1680 1848 2016 2184 2352 2520 2688 2856 3024 Hrs, wks cns The use of the Process Model to play scenarios Phase 1. New schedule: SIC cap 5 daily cases, NIC cap 4 daily cases Take-away: leveling SIC and NIC daily cases (re-scheduling elective cases for another DOW with the same block-time) results in reduction of the overall time when census exceeds the critical limit. This, in turn, results in reduction ED diversion down to 3.5%. Total number of cases over the same time period stays the same !! Red zone: Critical census limit is exceeded fewer number of times for shorter period wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 wk12 wk13 wk14 wk15 wk16 wk17 wk18 The use of the Process Model to play scenarios Phase 1. New Schedule: SIC cap to 4 daily cases, NIC cap to 4 (or 3) daily cases Take-away: leveling to SIC 4 daily cases results in even smoother schedule. Variation (std) reduced by ~40% (Mon) and ~35 % (Tue) vs. baseline. This further helps the system to easier handle the flow…. M on date byMon 9/259/189/119/48/288/218/148/77/317/247/177/107/36/266/196/126/5 4 3 2 1 0 T ue date byTue 9/269/199/129/58/298/228/158/88/17/257/187/117/46/276/206/136/6 4 3 2 1 0 4 1 4 0 4 33 4444444444 22 3333 44 22 4 2 0 2 3 44 SIC by Mon C A P = 4 SIC by Tuesdays C A P = 4 Daily Cap 4 (from 4 to 2). Variation (std)= 1.18 cases Daily Cap 4 (from 4 to 2). Variation (std)= 1.09 cases
  • 12. 12 We d date byWed 9/279/209/139/68/308/238/168/98/27/267/197/127/56/286/216/146/7 4 3 2 1 0 T hu date byThu 9/289/219/149/78/318/248/178/108/37/277/207/137/66/296/226/156/86/1 4 3 2 1 0 2 3 2 3 4 3333 4 0 3 4 0 2 3 4 222 44 222 0 22 4 3 44 0 44 SIC by Wednesday s C A P = 4 SIC by Thursdays C A P = 4 The use of the Process Model to play scenarios Phase 1. SIC cap to 4 daily cases, NIC cap to 4 (or 3) daily cases Take-away: leveling to SIC 4 daily cases results in even smoother schedule. Variation (std) reduced by ~39% (Wed) and ~23 % (Thu) vs. baseline. This further helps the system to easier handle the flow. Daily Cap 4 (from 4 to 2). Variation (std)= 1.21 cases Daily Cap 4 (from 4 to 2). Variation (std)= 1.33 cases ICUs census: Elective surgerydailycap:SIC =4, NIC = 4 ICU closed due to No ICU beds:1.5% of time 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 0 168 336 504 672 840 1008 1176 1344 1512 1680 1848 2016 2184 2352 2520 2688 2856 3024 hrs/wks cns The use of the Process Model to play scenarios Phase 1. SIC cap 4 daily cases, NIC cap 4 (or 3) daily cases Take-away: Additional leveling SIC and NIC daily cases (re-scheduling elective cases for another DOW with the same block-time) results in further reduction of the overall time when census exceeds the critical limit. This, in turn, results in further reduction of the closure time down to 1.5%. Total number of cases over the same time period stays the same !! Red zone: Critical census limit exceeded even fewer number of times and for shorter period wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 wk12 wk13 wk14 wk15 wk16 wk17 wk18
  • 13. 13 Did we find the best solution to implement by daily capping at four cases per day ? NOT YET….. • Not all patients can wait too long for their surgery, sometimes up to 2 months !! • Surgeons and physicians requested modeling scenarios in which ‘bumping’ was not further than 2 weeks apart (2 weeks waiting) •Therefore Phase 2 scenarios have been considered: • ‘Bump’ extra cases to the next available day with the same block-time for a different week which is not further than 2 weeks apart. • Keep daily load leveling at a higher level (five) for easier practical implementation M o n d a t e byMon 9 /2 59 /1 89 /1 19 /48 /2 88 /2 18 /1 48 /77 /3 17 /2 47 /1 77 /1 07 /36 /2 66 /1 96 /1 26 /5 8 7 6 5 4 3 2 1 0 M o n d a t e byMon 9 /2 59 /1 89 /1 19 /48 /2 88 /2 18 /1 48 /77 /3 17 /2 47 /1 77 /1 07 /36 /2 66 /1 96 /1 26 /5 8 7 6 5 4 3 2 1 0 4 1 4 0 4 2 3 2 5 3 5 2 3 2 6 5 8 4 1 4 0 4 2 3 2 5 3 5 2 3 2 6 5 8 S I C b y M o n d a y s S I C b y M o n d a y s B u m p e d e x t r a c a s e s t o m i n i m i z e o v e r a l l v a r i a b i l i t y B u m p e d e x t r a c a s e s w i t h i n 2 w e e k s a p a r t 4 5 Two ways of ‘bumping’ extra cases for load-leveling (Monday to Monday example) Take-away: • Load leveling 5 cases/day with bumped extra cases within 2 weeks apart resulted in a relatively small reduction of diversion: from 10.5% (no load leveling-baseline) to only about 8%. • This low effectiveness was because bumping within only 2 weeks apart resulted in a higher flow variability (std=1.59 cases) than the previous bumping aimed at minimizing the overall flow variability (std= 1.42 cases). Note, that the no load-leveling (baseline) flow variability was the highest, std =1.97 cases. • Thus, load leveling to 5 elective cases/day with bumped extra cases within 2 weeks apart was not effective enough alone.
  • 14. 14 Because load leveling to 5 elective cases/day with bumped extra cases within 2 weeks apart was not effective enough alone, Phase 3 has been considered: Phase 3: Exclude ICU admissions for patients who stayed less than 24 hrs. Combine with Phase 2. PlasticGynTrauma-GenOrthoTransplantAnesthesiaGUVascularGeneral 6 5 4 3 2 1 0 service Count 11 22 33 5 66 Services that meet ICUs admitting exclusion criteria Exclusion criteria: patients stayed less than 24 hrs General and Vascular patients most frequently stayed less than 24 hrs Phase 3 Take-Away: • Dramatic reduction in ICU closure time due to 'No ICU beds': down to about ~ 1 %. Summary of simulated ‘what-if’ scenarios SIC & NIC cap 5 SIC & NIC cap 5 SIC & NIC cap 4 SIC cap 5, NIC cap 4 SIC cap 6, no NIC cap current pattern-baseline 12 10 8 6 4 2 0 %ICUDiversion 1.0 8.0 1.5 3.5 8.9 10.5 ICU adm ission criteria applied Take-away: Smoothing the number of elective cases over time (daily load leveling) is a very significant factor which strongly affects ICU time closure due to ‘no ICU beds’. Move ‘extra’ cases within 2 weeks Predicted % of time ICU was closed
  • 15. 15 SUMMARY •There is a significant variation of the number of scheduled elective cases between the same days of the different weeks (Monday to Monday, Tuesday to Tuesday, and so on..) • Smoothing the number of elective cases over time (daily load leveling) is a very significant factor which strongly affects ICU closure time due to ‘no ICU beds’. • Using Process Model Simulation, it has been demonstrated (Phase 1) that daily load leveling of elective cases to not more than 4 cases per day will result in a very significant reduction of closure time due to ‘no ICU beds’ (from ~10% down to ~1%). However this solution (based on max reduction of the overall variability) requires ‘bumping’ extra cases up to 8 weeks ahead. Not all patients could wait that long for their elective surgeries. SUMMARY (cont) An alternative solution (Phase 2) has been simulated: ‘bumping’ extra cases to NOT more than 2 weeks apart and keeping daily leveling at 5 cases/day. However this solution was not effective enough alone: ICU closure time was down from~10% to only ~8%. • Therefore another alternative solution (Phase 3) has been simulated: combining Phase 2 and exclusion ICU admissions for patients stayed less than 24 hrs in ICU (ICU admission criteria applied). This solution was effective enough (closure time was less than 1%). It was considered for the practical implementation as a pilot project. • There is a trade-off between these two solutions: from the practical standpoint the higher level-loading (5 cases/day) would be easier to implement than the lower level-loading (4 cases/day). However the former assumes that ICUs admission criteria are strictly followed while the later does not require exclusion from the current ICU admitting pattern
  • 16. 16 • All three components affect the flow of patients that the system can handle. • A lack of the proper balance between these components results in the system’s over-flow and closure/diversion • Process Model Simulation methodology provides the only means of analyzing and managing the proper balance in the complex systems OVERALL METHODOLOGY SUMMARY • Patient Throughput flow is an example of the general dynamic Supply & Demand problem. This is not a one-time snapshot. System’s behavior depends on time. • There are three basic components that should be accounted for in this type of problems: • The number of patients (or, generally, any items) entering the system at any point of time • The number of patients (any items) leaving the system at any point of time after spending some time in the system • Limited Capacity of the system which limits the flow of patients through the system APPENDIX
  • 17. 17 WHAT IS THE PROCESS MODEL ? •It is a computer model that mimics the dynamic behavior of a real process over the time in order to visualize and quantitatively analyze its performance in terms of: •Cycle times •Throughput capacity •Resources utilization •Activities utilization •It is a tool to perform ‘WHAT-IF’ analysis and play different scenarios of the model behavior as conditions and process parameters change. This allows to make experiments on the computer model, and test different solutions (changes) for their effectiveness before going to the floor for the actual implementation. WHAT ARE THE BASIC ELEMENTS OF THE PROCESS MODEL? •Flow chart of the process: Diagram that depicts logical flow of a process from its inception to its completion •Entities: Items to be processed: patients, documents, customers, etc. •Activities: Tasks performed on entities: medical procedures, document approval, customer check out, etc •Resources: Agents used to perform activities and move entities: service personnel, operators, equipment, nurses, physicians. •Connections: •Entity arrivals: Define process entry points, time, and quantities of the entities that enter the system to begin processing •Entity routings: Define directions and logical conditions flow for entities
  • 18. 18 WHAT INFORMATION (DATA) IS REQUIRED TO FEED THE MODEL ? •Entities quantities and arrival time: periodic, random, scheduled, daily pattern, etc •The time that the entities spend in the activities. This is usually not a fixed time but a statistical distribution. The wider the time distribution the higher the variability of the system behavior. •The capacity of each activity, i.e. the max number of entities that can be processed concurrently in the activity. •The size of input and output queues for the activities •The routing type or the logical conditions to take a specified routing. •Resource Assignments: their number and availability, and/or resources shift schedule