Www.fdiplan.com Simulation Modeling and Design David Ferrin Principal Tanner Flynn Associate...

www.fdiplan.com

Simulation Modeling and DesignSimulation Modeling and Design

David FerrinPrincipal

Tanner FlynnAssociate Principal

Brian HolleyHealthcare Market Director-

California Region

Designing how facilities will be used before designing the facilities.

Confidential and Proprietary © 2008 FDI

Agenda

Lean PhilosophyWhat is Simulation – Who uses it?Comparing Simulation To “Spreadsheet” PlanningSimulation In Action - MethodologyCase Study Outcomes and Conclusions


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.


Lean Philosophy

Conceptually, the business processes in Lean are viewed from the customer’s 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.


Six Sigma and 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

Six Sigma Lean


Lean and Six Sigma

Lean = Improved process flow

Six Sigma = Reduced process variation


Agenda

Lean Philosophy

What is Simulation – Who uses it?

Comparing Simulation To “Spreadsheet” Planning

Simulation In Action - Methodology

Case Study Outcomes and Conclusions


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


Rush-Presbyterian-St. Luke’s Medical Center

Organizations our team members have helped realize the benefits of Simulation Modeling


“When should we use simulation?”

Simulation is the only tool that can provide the right answer when:

You can’t 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


Agenda



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?

Spreadsheet Model

Simulation Model

Mean

Lower Bound

Upper Bound

Run spreadsheet model:

Patients Arrive

10 minutes between arrivals

Triage Patient

Physician 1

1.1

10 m

Assess Patient

Physician 2

1.2

10 m

Diagnose Patient

Physician 3

1.3

10 m

Discharged /AdmittedPatients


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%

Spreadsheet Model

Simulation Model

Mean 30mins

Lower Bound 30mins

Upper Bound 30mins

Do real processes behave like this?


Simulation Accounts for Process Variability

Triage is normally distributed with a mean of 10mins, and a standard deviation of 2mins

Diagnosis is uniformly distributed, so that all times between 8 and 12 minutes are equally likely, mean is 10mins

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

Let us introduce variability into this example (ED flow) and analyze the process:

Can you predict the new patient LOS? What will be the 95%-ile range?

Run simulation model:

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


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

Triage Patient

Physician 1

1.1

Normal (10,2)

Patients Arrive

Time between arrivals: Exponential (10) minutes

Assess Patient

Physician 2

1.2

8 m 10 m 12 m

Diagnose Patient

Physician 3

1.3

Uniform (8,12)

Discharged /AdmittedPatients

Spreadsheet Model

Simulation Model

Mean 30mins 4.3 hrs

Lower Bound 30mins 3.5 hrs

Upper Bound 30mins 5.1 hrs

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?


Agenda



A Process Map of an Emergency Department


Number of ED Arrivals By Time of Day


Impact of Inpatient Discharge Time of Day on ED Length of Stay

6

7

8

9

10

11

12

13

14

Baseline 3pm 2pm 1pm noon 11am

ED A

vg L

OS

(Hours

) 13h 11m 11h

28m 10h 28m

10h 15m 10h

6m 9h 17m

Area of Opportunity

Inpatient Discharge Time of Day


Identifying a Facility “Break Point”

0

1

2

3

4

5

6

Current 40,000 41,000 42,000

ED LOS(hrs)

How many visits to the Emergency Department can our facility handle as designed with 32 ED Beds and 370 IP Beds?


AnimationsAnimations

ED 2

ED 1 Operating RoomPatient Care Throughput

Patient Care Throughput 2

Operating Room 2

Clinics (3) Loading Dock

Construction

ED 3

Radiology


Agenda Lean PhilosophyWhat is Simulation – Who uses it?Comparing Simulation To “Spreadsheet” PlanningSimulation In Action - MethodologyCase Study Outcomes and Conclusions LAC+USC Medical Center UCSF Pharmacy Medical Center Operating Room California Department of Corrections and Rehabilitation

www.fdiplan.com

Case Study - LAC+USC Medical Center


Simulations:

Emergency Department

Operating Suites

House-wide Patient Throughput

Outpatient Clinic Operations/Space Allocation

Loading Dock

Case Study – LAC+USC 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



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



The Effects of a Volume Increase on the Emergency Department Design

0

0.5

1

1.5

2

Time to ED Bed

97% 143%

7h30m

126%

ED LOS ED LOS95%

13h 11m

30h 27m

56%

81%73%

29%36%34%

3 Scenarios: Volumes increase by 5%, 10%, and 15% (95% IP Occupancy):

+5%10%15%


Adding Observation Beds Allows Faster Discharge Patient Flow

0

0.5

1

1.5

2

Time to ED Bed

7h30m

20%18h

Time to ED Bed

95%

ED LOS

21h 57m

13h 11m

15%11h

13m

38%55%

% Time EDGeneral Bed

Are Full

88%

39% 4.5h

Wait Time For An IP Bed

125% 7h

3h 6m

Process Times Associated With Addition of 22 Observation Beds:


“Building a Duplicate ED” To Test ED Capacity

2X%12h 44m

0

0.5

1

1.5

2

Time to ED Bed

81%1h

24m

7h30m

43%9h

29m

Time to ED Bed

95%

ED LOS

21h 57m

13h 11m 28%

9h 28m

69%27%

% Time EDGeneral Bed

Are Full

88%

Wait Time For An IP Bed

3h6m

Simulation Results if ED Had Twice the Number of Beds and Staff:


Inpatient Bed Availability Is The Major Bottleneck In ED Operations

0

0.5

1

1.5

2

Baseline +10 Beds +20 Beds +30 Beds

Time to ED Bed

16%6h 20m

83%1h 16m

7h30m

46%4h

Time to ED Bed 95% ED LOS

21h 57m

13h 11m7%

20h 25m

76%5h 16m

30%15h 22m

10%11h 52m

52%

28%9h 30m

6h 20m

+10 +20 +30


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



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.




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


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



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



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


www.fdiplan.com

Case Study - Pharmacy Operations and Construction Phase Model Final Report


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)


CurrentNarc

BS

IJS

IV

MP

UD

TI

Visual Representation of the pharmacy as it exists today.

The departure point for the 4 phased construction project.


New

MP

NARC

BS

UD

IVIJS

SL

All work stations are now in place

Swiss Log & Carousals are installed for operation


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


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.


Phase IUD

Narc

BS

IJS

IV

MP

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


Phase 2

IV

UD

NARC

BS

MP

IJS

IVAS is moved to new home

Inject able storage is moved to the previous transplant/Investigational


Phase 3

MP

BS

NARC

IV

UD

IJS

Bulk Storage is moved to new home

Inject able storage is moved to new home

Unit Dose is moved to new home


Phase 4

MP

BS

UD

NARC

IV

IJS

Narc is moved to new home

Manual Pick is moved to new home


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


Metrics

The project analysis was focused on two components:

“Storage Capacity” - defined by the physical storage space necessary to accomplish the workload


Reducing IVAS Techs/Space

0

0.5

1

1.5

2

2.5

3

0%

82%

22m 0% 66m22m

0%

IVAS Unit Dose

4.6% 69m

2hr 28m

39m

124%

- 1 - 2 - 1 - 2 Baseline Baseline

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.

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.


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

www.fdiplan.com

Case Study – Operating Room


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.

Case Study


Simulations:

Operating Suite with Demographic Changes for 2010, 2015 & 2018

Case Study


Average Minutes PACU Full in an Hour

Key Points: Yr – 2010 Number of OR’s – 6 PACU = 3 beds

0

5

10

15

20

25

30

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 of Day

Min

utes

Ful

l




0

5

10

15

20

25

30

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 of Day

Min

utes

Ful

l Recommendation: PACU = 4 beds 2010




0

5

10

15

20

25

30

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 of Day

Min

utes

Ful

l




0

5

10

15

20

25

30

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 of Day

Minu

tes F

ull




0

5

10

15

20

25

30

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 of Day

Minu

tes F

ull Recommendation:

PACU = 4 beds 2015




0

5

10

15

20

25

30

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 of Day

Minu

tes F

ull




0

5

10

15

20

25

30

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 of Day

Min

utes

Ful

l




Recommendation: PACU = 5 beds

0

5

10

15

20

25

30

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 of Day

Min

utes

Ful

l




0

5

10

15

20

25

30

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 of Day

Min

utes

Ful

l


Average Minutes Pre Op Full in an Hour

Key Points: Yr – 2010 Number of OR’s – 6 Pre Op = 7 beds

0

10

20

30

40

50

60

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 of Day

Min

utes

Ful

l



0

10

20

30

40

50

60

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 of Day

Min

utes

Ful

l





0

10

20

30

40

50

60

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 of Day

Min

utes

Ful

l Recommendation: Pre Op = 11 beds 2010




0

10

20

30

40

50

60

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 of Day

Min

utes

Ful

l




0

10

20

30

40

50

60

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 of Day

Min

utes

Ful





0

10

20

30

40

50

60

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 of Day

Min

utes

Ful

l




0

10

20

30

40

50

60

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 of Day

Min

utes

Ful





0

10

20

30

40

50

60

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 of Day

Min

utes

Ful

l


Capacity Summary/Recommendations

Operating Rooms=62010 OR=5

2015 OR=6

2018 OR=6

PACU Beds=5 Pre/Post Op Beds= 112010 PACU=4 Pre Op=11

2015 PACU=4 Pre Op=12

2018 PACU=5 Pre Op=12


Proposed OR LayoutAnimation

QVMC OR animationV3.exe

www.fdiplan.com

CDCR Receiving and Release Simulation CDCR Receiving and Release Simulation Model Model

David FerrinPrincipal

Tanner FlynnAssociate Principal

Steve FallerSr. Project Engineer

September 24, 2008


Simulations:

The Process of Receiving and Releasing Inmates

Case Study – CDCR Receive and Release


Case Study – CDCR Receive and Release

The reception centers process all inmates coming in to the system. The reception center assesses each inmate’s 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.


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


Scenario:Reduce Inventory Property Times (To-Be)

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

NK SQ Wasco

5%38%

37%

% Complete in 6 hours

Time Waiting to Begin Process

Time in Parallel Process

NK SQ Wasco NK SQ Wasco

85% 36%7 h

27 m 57 m7 h

59 m2 h

24 m2 h

28 m2 h

18 m

6%38%

8%6 h

53 m

2%56 m

5%7 h

36 m1%

86%

3%2 h

19 m

3%2 h

24 m

6%2 h

11 m

Methodology:

Green % is GOODRed % is BAD


Scenario:Hours Per Week Required for Inventory Property Screening (To-Be)

Key Points: Hours saved per week by reducing the inventory property:

NK – 24 hoursSQ – 20 hoursW – 48 hours

NK Wasco

47 h

Hours per Week

36 h

56%16 h

Methodology:


51%23 h

75 h

64%27 h

SQ


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


Analysis to increase the quantity of resources by Double

Methodology:


Key Points: Resource analysis

Each bar is a different scenario with that sole resource being doubled

The bottleneck resource will give the highest % improvement

% Complete in 6 hours

37%1%

36%4%

38%

17%30%

5%39%

42%52%

9%40%

4%38%

3%38%

Baseline Counter Live Scan

Photo Room

Psych Room

PanorexLab Table

Nurse Office

LVN Station

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


FDI Simulation is the largest Healthcare Simulation Modeling firm in the country with more than 20 years experience, providing custom models incorporating best-practices and

hospital’s own data routinely yielding multi-million dollar financial impacts. FDI’s models simulate and analyze hundreds of scenarios projecting multi-annual impacts in minutes.

FDI uniquely provides prioritized implementation strategies for patient throughput and capacity issues. Our prioritization is based on the greatest and quickest financial and

patient experience benefits with the least amount of change required.

www.fdiplan.com

David Ferrin 602-212-3565 (office)630-258-0141 (cell)[email protected]

Tanner [email protected]

Www.fdiplan.com Simulation Modeling and Design David Ferrin Principal Tanner Flynn Associate...

Documents

Transcript of Www.fdiplan.com Simulation Modeling and Design David Ferrin Principal Tanner Flynn Associate...