Simulation Modeling and Design

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Simulation Modeling and Design
Designing how facilities will be used before
designing the facilities.
David Ferrin Principal Tanner Flynn Associate
Principal Brian Holley Healthcare Market
Director- California Region
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Agenda

• Lean Philosophy
• What is Simulation Who uses it?
• Comparing Simulation To Spreadsheet Planning
• Simulation In Action - Methodology
• Case Study Outcomes and Conclusions

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Lean Philosophy

• Lean means eliminating waste from any process or
  product.
• The first step is to identify the true Value
  Stream of a business process. A clearly defined
  and agreed upon Value Stream throughout the
  organization is the basis for any improvement
  action to achieve high process performance at a
  significantly reduced cost base.

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Lean Philosophy

• Conceptually, the business processes in Lean are
  viewed from the customers perspective.
• The value of an activity is solely defined by the
  customer.
• Activities that add value to the customer are
  those that make the product or service resemble
  more of what the customer actually wants and for
  which he is willing to pay.
• Non value-added activities, however, do not
  create any value for the customer, and therefore
  all nonessential, non-value-added activities are
  considered as waste.
• Waste is any activity in the workflow that adds
  time, effort or cost but does not create value.

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Six Sigma and Lean
Six Sigma Lean

• Eliminate defects as defined by the customer
• Recognizes that variations hinder our ability to
  reliably deliver high quality products
• Requires data-driven decisions
• Set tools for effective problem solving

• Focus on maximizing process velocity
• Tools for analyzing process flows and delays
• Centers on value added vs non-value added
• Means for quantifying and eliminating complexity

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Lean and Six Sigma

• Lean Improved process flow
• Six Sigma Reduced process variation

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Agenda

• Lean Philosophy
• What is Simulation Who uses it?
• Comparing Simulation To Spreadsheet Planning
• Simulation In Action - Methodology
• Case Study Outcomes and Conclusions

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How industry analysts regard simulation

• "Virtually all of the Fortune 50, a majority of
  the Fortune 1000 and military planning units of
  all technologically advanced countries, use
  simulation rather than subjective notions to make
  decisions about key manufacturing and logistics
  process decisions. There are no good reasons why
  simulation should not be used to aid decisions
  about key business processes. On the contrary,
  there are numerous good reasons why simulation
  should be used for BPR."
•  
• Robert Crosslin
• "Simulation, The Key to Designing and Justifying
  Business Reengineering Projects"
• The Electronic College of Process Innovation
• .
• Simulation and animation technology offers
  ... organizations the potential to more
  rigorously test, analyze, validate and
  communicate their business processes and systems
  before they invest in implementation.
• - The Gartner Group

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Organizations our team members have helped
realize the benefits of Simulation Modeling
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When should we use simulation?

• Simulation is the only tool that can provide the
  right answer when
• You cant afford to miss the design the first
  time.
• You need to evaluate complex system interactions
  - when operations have lots of steps with wide
  time variations and require multiple individuals
  and physical resources.
• You need to understand the combined financial,
  operational, and human experience of the design

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Agenda

• Lean Philosophy
• What is Simulation Who uses it?
• Comparing Simulation To Spreadsheet Planning
• Simulation In Action - Methodology
• Case Study Outcomes and Conclusions

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Most Planning Is Done Using Static Models Based
on Averages

• Example
• Let us attempt to model ED flow using averages
  (e.g. the spreadsheet model)
• Patients arrive every 10 minutes
• Each activity lasts 10 minutes
• Can you predict what the mean patient LOS will
  be?
• What will be the 95-ile range?

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The Results Are Predictable And Do Not Mirror
Real Processes!

• Results of the spreadsheet model are at the
  right
• Notice the process behavior of the model
• Physicians finish their activity just as the next
  patient arrives
• Patients never wait in queue
• The LOS never varies
• Patients move with drum-beat synchronicity
  through the ED
• Physician utilization is effectively 100

Do real processes behave like this?
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Simulation Accounts for Process Variability
Let us introduce variability into this example
(ED flow) and analyze the process

• Patient arrival is exponential with a mean of
  10mins

• Assessment is triangularly distributed, so that
  it always takes 8mins, usually takes 10mins, but
  never more than 12mins

• Note-average times for these activities are still
  10mins give or take a couple!

Can you predict the new patient LOS? What will
be the 95-ile range?
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Simulation Model Produces Drastically Different
Outcomes From Spreadsheet Model

• Results of the simulation model are at the right
  (50 iterations of 1 week are run)
• Notice the process behavior of this model
• Patient LOS is 3 5 hours
• Long queue waiting for Physician 1
• Physician utilization still 100
• Notice how small variations in the process
  increased patient LOS dramatically

Only about 10 of the total LOS is value-added
time the rest is time spent in queue how would
you account for this on a spreadsheet?
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Agenda

• Lean Philosophy
• What is Simulation Who uses it?
• Comparing Simulation To Spreadsheet Planning
• Simulation In Action - Methodology
• Case Study Outcomes and Conclusions

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A Process Map of an Emergency Department
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Number of ED Arrivals By Time of Day
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Impact of Inpatient Discharge Time of Day on ED
Length of Stay
13h 11m
11h 28m
10h 28m
10h 15m
10h 6m
9h 17m
Inpatient Discharge Time of Day
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Identifying a Facility Break Point
How many visits to the Emergency Department can
our facility handle as designed with 32 ED Beds
and 370 IP Beds?
ED LOS (hrs)
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Animations
Patient Care Throughput
ED 1
Operating Room
Patient Care Throughput 2
ED 2
Operating Room 2
Loading Dock
Clinics (3)
ED 3
Radiology
Construction
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Agenda

• Lean Philosophy
• What is Simulation Who uses it?
• Comparing Simulation To Spreadsheet Planning
• Simulation In Action - Methodology
• Case Study Outcomes and Conclusions
• LACUSC Medical Center
• UCSF Pharmacy
• Medical Center Operating Room
• California Department of Corrections and
  Rehabilitation

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Case Study - LACUSC Medical Center
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Case Study LACUSC Medical Center

• Simulations
• Emergency Department
• Operating Suites
• House-wide Patient Throughput
• Outpatient Clinic Operations/Space Allocation
• Loading Dock

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Case Study LACUSC Medical Center

• Project History
• Flagship Hospital of Los Angeles County Dept. of
  Health Services
• Originally 4 hospitals with 2,104 licensed beds
• 2 hospitals were destroyed in 1994 earthquake
• Complete replacement hospital approved designed
  for 946 beds - approved for construction of 600
  beds

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Case Study LACUSC Medical Center

• Facility Configuration
• Current Facility New Facility
• Inpatient Beds 671 600
• Outpatient Visits 520,000 350,000
• Admissions 38,000 -
• ED Visits 172,000 -
• Clinic Exam Rooms 340 217
• Inpatient Length of Stay 6.1 days 5.5 days

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The Effects of a Volume Increase on the Emergency
Department Design
3 Scenarios Volumes increase by 5, 10, and 15
(95 IP Occupancy)
5
10
15
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Adding Observation Beds Allows Faster Discharge
Patient Flow
Process Times Associated With Addition of 22
Observation Beds
125 7h
15 11h 13m
7h 30m
13h 11m
3h 6m
21h 57m
88
20 18h
38 55
39 4.5h
Wait Time For An IP Bed
Time to ED Bed
Time to ED Bed 95
ED LOS
Time ED General Bed Are Full
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Building a Duplicate ED To Test ED Capacity
Simulation Results if ED Had Twice the Number of
Beds and Staff
2X 12h 44m
7h 30m
21h 57m
13h 11m
3h 6m
88
28 9h 28m
43 9h 29m
81 1h 24m
69 27
Time to ED Bed
Time to ED Bed 95
ED LOS
Time ED General Bed Are Full
Wait Time For An IP Bed
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Inpatient Bed Availability Is The Major
Bottleneck In ED Operations
7h 30m
21h 57m
13h 11m
7 20h 25m
10 11h 52m
16 6h 20m
28 9h 30m
30 15h 22m
46 4h
52
6h 20m
76 5h 16m
83 1h 16m
30
10
20
Time to ED Bed
Time to ED Bed 95
ED LOS
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Case Study LACUSC Medical Center

• Emergency Department
• Sized for 946 Inpatient Beds v. 600
  OPERATIONAL/DESIGN MISMATCH causing a dramatic
  impact on the ED
• More than 85 of inpatient admissions enter via
  the ED
• 3X the square footage with the same number of ED
  Beds
• 6 free-standing EDs moving into one location
• 172,000 visits - 3rd busiest ED in the US

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Case Study LACUSC Medical Center

• Emergency Department - What did we learn?
• Current demand v. new demand
• Waiting Room Capacity Impact
• Geographic Staffing
• Impact of changes to Inpatient Occupancy Rate,
  LOS and DTOD
• Effect of various changes to internal ED
  processes radiology TAT Discharge process, etc.

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Case Study LACUSC Medical Center

• Outpatient Clinic Design Challenges
• Excluded Primary Care (Internal Medicine Clinic)
  - Primary Care is on Campus today, with highest
  volume of all Clinic Groups
• Designed to accommodate up to 350,000 Annual
  Visits - Must maintain current visits of over
  500,000 annual visits. (42 increase)
• Designed to accommodate Infusion Therapy Services
  for 22 patients in one location (Adult and Peds
  combined) - Current Infusion Therapy Services
  accommodates over 54 Infusion patients in one
  location (trending upwards)
• Exam Rooms - 50 reduction
• Existing Buildings 340
• New Building 171

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Case Study LACUSC Medical Center

• Outpatient Clinic - Project Goals
• Accommodate current service volumes
• Minimize the move of services to the Community
  Health Centers
• No addition of evening/off-hours sessions due to
  staffing limitations
• Minimize session/schedule change to the extent
  possible
• Minimize session over-time
• Optimize space utilization of the new facility

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Case Study LACUSC Medical Center

• Simulation gave us a way to
• Accommodate 500 individual Clinics in 104
  Service Groups
• Minimize clinic schedule changes to just 23
  Services
• Identify the 64 Services that needed to adjust
  their time per patient to sustain current volumes
  (benchmarked for feasibility)
• Address numerous factors in the clinic
  operations to assess impact of clinic designation
  such as patient arrival patterns, appointment
  slots, physician ratios, etc.
• Redesign, refine and standardized clinic
  processes
• Experiment with over 120 scenarios to determine
  best fit for each clinic service

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Case Study LACUSC Medical Center

• Without Simulation
• We would have had to try multiple scenarios using
  post-it notes and averages!
• Move or eliminate services that we would have
  otherwise assumed would fit and function
• Alternatives would have been tried in the
  facility at the expense of the patients

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Case Study - Pharmacy Operations and Construction
Phase Model Final Report
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Situation Challenge

• Pharmacy is transforming from centralized manual
  pick operation to a decentralized automated
  operation
• Must stay operational with very limited space
  available for temporary setup.
• Typical healthcare renovation challenges exist
  (ILS, ICRA, OSHPD, etc)

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Narc
Current
BS
Visual Representation of the pharmacy as it
exists today. The departure point for the 4
phased construction project.
IJS
TI
MP
IV
UD
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New
MP
SL
NARC
All work stations are now in place Swiss Log
Carousals are installed for operation
BS
IJS
IV
UD
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Objectives

• Establish realistic expectations for the
  operational impacts and necessary planning during
  the design phase.
• Communicate the phasing plan to the department
  users.
• Define metrics for evaluating/ managing the plan.
• Simulate results and define monitoring points

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Approach

• Phasing Overview
• Design Team BFHL Architects- San Francisco, C
• Current versus New
• Phases 1 4
• Metrics
• Simulation Result- example of findings from data
  evaluation.

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Narc
UD
Phase I
BS
MP
IJS
Unit Dose is moved to location in front of
Narc. Manual Pick is moved to previous order
entry location Transplant/Investigational
Order Entry is displaced elsewhere in the
facility
IV
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NARC
Phase 2
UD
BS
MP
IVAS is moved to new home Inject able storage is
moved to the previous transplant/Investigational
IJS
IV
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NARC
Phase 3
MP
Bulk Storage is moved to new home Inject able
storage is moved to new home Unit Dose is moved
to new home
BS
IJS
IV
UD
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Phase 4
MP
NARC
Narc is moved to new home Manual Pick is moved
to new home
BS
IJS
IV
UD
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Metrics
The project analysis was focused on two
components
Workload - defined by the number of Orders
processed
Orders may contain several sub-orders (referred
to as labels) 85.8 of all orders had 1 label and
were processed the same day
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Metrics
The project analysis was focused on two
components
Storage Capacity - defined by the physical
storage space necessary to accomplish the workload
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Reducing IVAS Techs/Space
Approach This graph represents the effect of
reducing an IVAS tech/space vs. the time it takes
to have an order ready for delivery once the
order has been electronically forwarded to the
IVAS pharmacist.
124
2hr 28m
82
39m
Key Points Reducing 1 tech/space will have no
effect for the IVAS area. A slight increase,
however, was noted in the time it takes to have
an order ready for delivery in the Unit dose
area. This is due to the fact that some Unit dose
orders require use of an IVAS work area. Reducing
2 tech/space results in significant increases in
both IVAS and Unit dose times.
0
22m
0 66m
22m
0
4.6 69m
- 1
- 2
- 1
- 2
Baseline
Baseline
IVAS
Unit Dose
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Summary

• In addition to confirming the operational impact
  of a design..
• Simulation can be an invaluable design /
  construction planning tool for implementing
  complex hospital projects.
• Communicate phase planning in operational terms.
• Test the results before the OSHPD permit /
  construction barricades
• Define key monitoring points for managing the plan

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Case Study Operating Room
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Case Study

• Project History
• The hospital needed to redesign their work
  processes, Identify capacity constraints and
  operational bottlenecks, and determine mitigation
  approaches for their new Operating Room. The
  facility is planning for the same number of
  operating rooms, but needs to handle more volume.

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Case Study

• Simulations
• Operating Suite with Demographic Changes for
  2010, 2015 2018

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• PACU 3 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• PACU 3 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• PACU 4 beds

• Recommendation
• PACU 4 beds 2010

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• PACU 5 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• PACU 6 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• PACU 3 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• PACU 4 beds

• Recommendation
• PACU 4 beds 2015

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• PACU 5 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• PACU 6 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• PACU 3 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• PACU 4 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• PACU 5 beds

• Recommendation
• PACU 5 beds

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Average Minutes PACU Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• PACU 6 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• Pre Op 7 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• Pre Op 8 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• Pre Op 9 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• Pre Op 10 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• Pre Op 11 beds

• Recommendation
• Pre Op 11 beds 2010

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2010
• Number of ORs 6
• Pre Op 12 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• Pre Op 7 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• Pre Op 8 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• Pre Op 9 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• Pre Op 10 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• Pre Op 11 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• Pre Op 12 beds

• Recommendation
• Pre Op 12 beds 2015

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2015
• Number of ORs 6
• Pre Op 13 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• Pre Op 7 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• Pre Op 8 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• Pre Op 9 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• Pre Op 10 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• Pre Op 11 beds

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• Pre Op 12 beds

• Recommendation
• Pre Op 12 beds 2018

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Average Minutes Pre Op Full in an Hour

• Key Points
• Yr 2018
• Number of ORs 6
• Pre Op 13 beds

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Capacity Summary/Recommendations

• Operating Rooms6
• 2010 OR5
• 2015 OR6
• 2018 OR6
• PACU Beds5 Pre/Post Op Beds 11
• 2010 PACU4 Pre Op11
• 2015 PACU4 Pre Op12
• 2018 PACU5 Pre Op12

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Proposed OR LayoutAnimation
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CDCR Receiving and Release Simulation Model 
David Ferrin Principal Tanner Flynn Associate
Principal Steve Faller Sr. Project
Engineer September 24, 2008
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Case Study CDCR Receive and Release

• Simulations
• The Process of Receiving and Releasing Inmates

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Case Study CDCR Receive and Release

• The reception centers process all inmates coming
  in to the system. The reception center assesses
  each inmates health and routes them to the
  correct prison. The inmates then wait at the
  reception center until the selected prison has an
  available bed for them. The challenge for the
  reception center was to reduce the typical
  three-day period assessment to six hours per
  inmate on the day of arrival.

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CDCR Reception Release Process

• Standardizing the inmate property received to the
  lowest acceptable level from the counties
• This was supposed to improve the value stream
  (processed inmates) by removing a bottle neck and
  increasing the percent of inmates completed in
  six hours and decreasing the inmates time to be
  seen
• Although this did not have the desired effect it
  was found that the staffing hours per week could
  be dramatically reduced using this standard
• The following two slides show how the numbers
  played out

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ScenarioReduce Inventory Property Times (To-Be)
Methodology Green is GOOD Red is BAD

• Key Points
• Does not have a major impact on the complete
  in 6 hours
• The other 8 activities in the parallel process
  dilute the time that is saved in this one activity

8 6 h 53 m
2 56 m
5 7 h 36 m
3 2 h 19 m
3 2 h 24 m
6 2 h 11 m
5 38
6 38
1 86
7 h 59 m
2 h 18 m
7 h 27 m
2 h 24 m
2 h 28 m
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85
36
57 m
NK
SQ
Wasco
NK
SQ
Wasco
NK
SQ
Wasco
Complete in 6 hours
Time Waiting to Begin Process
Time in Parallel Process
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ScenarioHours Per Week Required for Inventory
Property Screening (To-Be)
Methodology Green is GOOD Red is BAD
75 h

• Key Points
• Hours saved per week by reducing the inventory
  property
• NK 24 hours
• SQ 20 hours
• W 48 hours

47 h
36 h
51 23 h
64 27 h
56 16 h
NK
Wasco
SQ
Hours per Week
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CDCR Reception and Release Process

• Doubling the quantity of resources revealed that
  the only additional space necessary would be for
  additional RN offices.
• Doubling the RN offices increased the velocity of
  inmates on the reception end of the process

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Analysis to increase the quantity of resources by
Double
Methodology Green is GOOD Red is BAD
42 52

• Key Points
• Resource analysis
• Each bar is a different scenario with that sole
  resource being doubled
• The bottleneck resource will give the highest
  improvement

9 40
1 36
4 38
5 39
4 38
3 38
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17 30

• Resources Doubled to
• Counter 30
• Live Scan 10
• Photo Room 2
• LVN Station 4
• Nurse Office 4
• Lab Table 4
• Panorex 4
• Psych Room - 10

Baseline
Counter
Live Scan
Photo Room
Psych Room
Panorex
Lab Table
Nurse Office
LVN Station
Complete in 6 hours
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David Ferrin 602-212-3565 (office)630-258-0141
(cell)dferrin_at_fdiplan.comTanner
Flynn602-212-3579602-538-2640tflynn_at_fdiplan.com

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