simul8 user group - geisinger case study - balancing patient satisfaction with staff efficiency

2013 East Coast

User Group Meeting

Balancing Patient Satisfaction and

Staff Efficiency: Logistics of Meal

Delivery

December 11, 2013

SIMUL8 Corporation | SIMUL8.com | [email protected]

1 800 547 6024 | +44 141 552 6888

Presenter

Seth Hostetler

Process Engineer, Care Support Services

Geisinger Health System

• Care Support Services includes Supply Chain and Enterprise

Pharmacy

• Been with Geisinger for 2+ years

• Educational background is in Industrial Engineering and Operations

Research

• Member of IIE, SHS, and INFORMS

Heal • Teach • Discover • Serve Copyright Geisinger Health System 2013

Not for reuse or distribution without permission

Geisinger Health System Confidential and Proprietary

Agenda

• Introduction and objectives

• Approach and the proposed system

• Phase 1 Modeling

• Experiment

• Results and insights

• Recommendations and future directions

• Phase 2 Modeling

• Model adjustments

• Operational strategy testing

• Analysis and recommendations

• Complete experimental results

| 3




Introduction

• Geisinger Medical Center’s (GMC) food service

department was planning a transition to on-demand,

room service style delivery of meals to inpatients in 2013

• 18 inpatient units, approx. average census = 360

• Excludes inpatient psych unit

• The new system will result in:

• Changes in resource management and requirements

• Changes in process and employee roles

• Changes in patient service

| 4




Objectives

1. Create a representation of the new food service

delivery process in a simulation environment

2. Use simulation to show how changing system

parameters will affect patient service levels and

resource requirements

3. Through model analysis, create operational

recommendations for the forthcoming system changes

| 5




The Model and the Team

• This work is based on a model that we have been using

building and working with since 2010

• Original model was developed by a consulting group,

Efficiency Engineers

• Geisinger did not have the in-house expertise to create

• Able to leverage consulting expertise to develop the complex

model

• Helped to gain buy-in from senior leadership to help develop the

process engineering team

• Model was handed off to the engineering to for further

development and use

• Phase 1 of this work was the thesis research of an

Industrial Engineering Masters student at Penn State | 6




The Internal Logistics Simulation Model

• Captures all support service functions within GMC

• 19 inpatient units

• 33 outpatient clinics

• 51 ancillary service departments

• Captures food delivery to:

• Med/Surg. Units

• Adult critical care units

• Women’s and Children’s

| 7




Internal

Logistics

Simulation

Model

2.1 Miles of Hallway

38 Elevators

11 Floors

199 Network Nodes

160 Destinations

35 Transport Groups




Modeling the New Food Services System

• Three delivery zones

• Dispatch timer on carts

• Once the first tray is placed on a cart a timer is set

• The cart will leave when it is full or when the timer

expires, whichever occurs first

• Nearest neighbor delivery route

• Cart and staff resource modeled as one entity

• Always a host to travel with a cart

• Three meals per day

• Approximately 1060 meals served between 5:30am

and 9:30pm

| 9

Zone A Zone B Zone JK

AGP4 GP2 WILL1

AICU4 SCU3 CH2

SCU4 SCU5 CH3

AGP5 BP5 HfAM7

ICS5 BP6 HfAM8

AICU5 BP7

BP8




The Delivery Process

| 10

Patient orders

meal

Is there a

cart

waiting for

that zone?

Initiate new

cart for the

zone

Add meal to

zone cart

Is cart at

capacity

?

Has the

time limit

been

met?

Wait for

another meal

or until time

limit met

Send trays for

delivery

N

Y

N

Y

N

Y




Input Data

• Distribution of meal orders

• Estimate of meal delivery count every 30 minutes

• Patient census snapshot

• Time to serve meal to patient

• Estimated with time studies

• ~59 seconds to serve meal

• ~49 additional seconds for isolation patient

• 10% of patients modeled as isolation status

• Travel speed

• 185 feet/min

| 11




Key Performance Measures

PATIENT SATISFACTION

• Service time

• Wait time on cart before

routing

• Percent delivered after

30*, 40, 45 and 50

minutes

OPERATIONAL EFFICIENCY

• Cart utilization

• Maximum number of

carts used*

• Number of delivery trips

made in a day

| 12

* Management chose these two metrics as the most important.




The Phase 1 Experiment

• Carts modeled as an unlimited resource

• Three dispatch times

• 8, 10, or 12 minutes

• Three cart capacities

• 12, 14 or 18 trays

Which combination of dispatch time and cart

capacities would be most effective?

| 13




Results

| 14

Service Time Max Number of Carts Number of Delivery Trips

Parameters Minutes Cart Capacity Average 95% CI Average 95% CI Average 95% CI

8 12 17.22 (17.15, 17.29) 16.00 (15.83, 16.17) 242.60 (241.40, 243.80) 8 14 17.20 (17.13, 17.27) 15.93 (15.66, 16.21) 242.70 (241.39, 244.01) 8 18 17.23 (17.17, 17.29) 15.90 (15.72, 16.08) 243.23 (241.64, 244.82)

10 12 19.23 (19.16, 19.30) 14.63 (14.45, 14.82) 206.53 (205.36, 207.70) 10 14 19.31 (19.23, 19.38) 14.64 (14.46, 14.83) 205.00 (203.77, 206.23) 10 18 19.31 (19.23, 19.39) 14.37 (14.18, 14.55) 205.43 (204.39, 206.47) 12 12 20.97 (20.89, 21.04) 13.70 (13.53, 13.87) 179.60 (178.82, 180.38) 12 14 21.20 (21.11, 21.30) 13.27 (13.10, 13.43) 177.77 (176.79, 178.75)

Cart Utilization

Wait Time on Cart Before Routing

Percent delivered after 30 min Parameters

Minutes Cart Capacity Average 95% CI Average 95% CI Average 95% CI 8 12 0.3613 (0.3597, 0.3629) 4.78 (4.77, 4.80) 2.06 (1.83, 2.29) 8 14 0.3086 (0.3068, 0.3105) 4.80 (4.78, 4.83) 2.00 (1.78, 2.21) 8 18 0.2399 (0.2387, 0.2412) 4.80 (4.78, 4.81) 2.10 (1.92, 2.29)

10 12 0.4235 (0.4210, 0.4259) 5.80 (5.82, 5.77) 5.67 (6.02, 5.32) 10 14 0.3660 (0.3640, 0.3681) 5.83 (5.81, 5.85) 6.08 (5.78, 6.38) 10 18 0.2850 (0.2835, 0.2864) 5.85 (5.83, 5.87) 5.97 (5.59, 6.35) 12 12 0.4878 (0.4856, 0.4899) 6.72 (6.69, 6.76) 10.79 (10.41, 11.16) 12 14 0.4238 (0.4217, 0.4260) 6.81 (6.78, 6.84) 12.04 (11.58, 12.49)

Percent delivered after 40 min


Percent delivered after 50 min Parameters

Minutes Cart Capacity Average 95% CI Average 95% CI Average 95% CI 8 12 0 (0, 0) 0 (0, 0) 0 (0, 0) 8 14 0.003 (-0.003, 0.010) 0 (0, 0) 0 (0, 0) 8 18 0.003 (-0.003, 0.010) 0 (0, 0) 0 (0, 0)

10 12 0.061 (0.026, 0.095) 0 (0, 0) 0 (0, 0) 10 14 0.117 (0.073, 0.161) 0 (0, 0) 0 (0, 0) 10 18 0.199 (0.129, 0.270) 0.013 (-0.003, 0.028) 0 (0, 0) 12 12 0.231 (0.175, 0.288) 0 (0, 0) 0 (0, 0) 12 14 0.434 (0.310, 0.558) 0.010 (-0.001, 0.020) 0 (0, 0)

Dispatch Timer

Cart Utilization

Service Time

Max # Carts

# Delivery Trips

Service Level

Cart Capacity

Cart Utilization




Interpreting the Results

• These trends reveal a broad theme within the meal delivery system:

A trade-off exists between patient service and efficient use of

resources.

• The 8 minute dispatch timer provides the best performance in terms of

percent of meals delivered beyond each of the time intervals.

• As the dispatch timer increases, the results show an improvement in the

efficient use of resources; however, the factors measuring patient

service perform worse as the timer length grows.

• A cart capacity of 12 meal trays provides the highest utilization for each

of the three dispatch timer settings.

– Cart utilization is the only measure significantly affected by a change in cart capacity.

– For this reason, the 12 meal tray carts were selected. | 15




Results

| 16

12-Tray Cart Capacity

Max Number of Carts Number of Delivery Trips Cart Utilization

Parameters

Minutes Average 95% CI Average 95% CI Average 95% CI

8 16.00 (15.83, 16.17) 242.60 (241.40, 243.80) 0.3613 (0.3597, 0.3629)

10 14.63 (14.45, 14.82) 206.53 (205.36, 207.70) 0.4235 (0.4210, 0.4259)

12 13.70 (13.53, 13.87) 179.60 (178.82, 180.38) 0.4878 (0.4856, 0.4899)



Parameters

Minutes Average 95% CI Average 95% CI

8 2.06 (1.83, 2.29) 0 (0, 0)

10 5.67 (6.02, 5.32) 0.061 (0.026, 0.095)

12 10.79 (10.41, 11.16) 0.231 (0.175, 0.288)

8 Minutes

10 Minutes

12 Minutes

0

2

4

6

8

10

12

13.5 14 14.5 15 15.5 16 16.5

% D

eliv

ere

d a

fter

30 m

inu

tes

Max Number of Carts




| 17

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Pe

rce

nt

of

Tim

e in

Us

e

Number of Carts in Use

Usage Profile for the 12-Tray Cart

8 Minutes

10 Minutes

12 Minutes

• Must consider more than just the max number of carts used.

• For example, the 8 minute dispatch timer experiences a count of 17 carts in

use for a period of time in a day, but it is for less than 0.01% of the time.

• A balance must be struck between the need to have a cart available to

deliver meals, and the acceptable limit of time that a meal must wait to be

delivered because it is waiting for a cart.




Phase 1 Recommendations

• 12 tray cart capacity will provide best cart utilization and

lowest equipment cost

• Depending on dispatch timer setting, trade-offs exist

between:

• Required number of carts (14-17)

• Number of delivery trips (178-244)

• Cart Utilization (35%-49%)

• Service Level (1.83%-11.16% delivered after 30 min)

• Shorter timer will provide better service level

• Longer timer will sacrifice service level for improved

resource usage | 18




Phase 2 Model

• The operational staff schedule provided did not provide

enough employee hours to satisfy work requirements

• Utilization is greater than 100%

• Combined delivery and pickup processes determined to

enhance service levels

• Estimated approx. 40% reduction in number of trips required

• Inclusion of return process

• Empty cart swapped with cart full of dirty trays on return route

• Return trip route based on a cyclical zone schedule

• Carts cleaned after each round trip

| 19




Additional Model Assumptions

• Food preparation and cart cleaning time included

• Zone routing for pickup and delivery

• Carts modeled as an unlimited resource

• Cart use limited by number of staff available

• No break times included in resource schedule • At request of management

• Adjustments made to original order distribution to create

a 7am-7pm order estimate | 20




Performance Measures

1. Service level

– Percent of carts delivered in 45 minutes or less

2. Resource utilization

– Percent of time resources are in use for

delivery/pickup services

3. Scheduled work hours in one day

| 21




Experimentation Objective

• Determine the optimal staffing schedule,

as well as the most efficient delivery and

pickup routing methods

• Test various daily employee schedules to

determine an hourly schedule which

allows for high service levels and worker

utilization

• Management asked that staff be

scheduled hourly, not by shift

• Ability to flex staff across various tasks

| 22

Example Schedule

Time Staff Count

7am-8am 9

8am-9am 12

9am-10am 12

10am-11am 8

11am-12pm 8

12pm-1pm 13

1pm-2pm 12

2pm-3pm 10

3pm-4pm 9

4-pm-5pm 7

5pm-6pm 9

6pm-7pm 11

7pm-8pm 9

8pm-7am 0




Delivery and Pickup Routing Scenarios

Testing performed on three scenarios:

A. Standard original operations (3 zones, fixed route

schedule)

B. Modified pickup schedule

• Rotational delivery schedule forces delivery route to begin

from different unit on each trip

• Pickup unit is last unit in delivery schedule

C. Six zone facility layout with original operations

Zone 1 2 3 4 5 6

Units

AGP4 AGP5 GP02 BP05 WLL1 HFAM6

AICU4 AICU5 SCU3 BP06 CHM2 HFAM7

SCU4 ICS5 SCU5 BP07 CHM3 HFAM8

BP08

| 23

Which of these scenarios do you think will be most effective?




Scenario Testing

• All scenarios tested in a 24-hour simulation period

• Includes routing and staffing variations

• Seven highest performing scenarios tested in 30-day

simulation period

• Reduction of variability

• Testing to maximize service level and utilization

• Minimization of total employee hours used to break “ties”

| 24




85

80.8 79.75

74.78

94 95

96 97

80

75.86 74.83

69.7

95 96

97 98

90.52

88.4 87.5

83.7

96

98 98 99

60

65

70

75

80

85

90

95

100

105

3 7 6 5

Schedule

Standard Util

Standard Service

Rotational Util

Rotational Service

6 Zone Util

6 Zone Service

2

5

120 129 131 142 | 25

Comparison of Service Level (squares) and Resource Utilization (triangles) for

the 4 best performing staff schedules. Option A: Shown in green, is the standard routing, 3-zone option.

Option B: Shown in red, is the modified, rotational, 3-zone option.

Option C: Shown in blue, is the standard routing, 6-zone option.




90.52 88.4

83.7

96 98 99

60

65

70

75

80

85

90

95

100

105

110

3 7 5

Schedule

6 Zone Operation Utilization-Service Trade-Off

Utilization

Service

Six Zone Delivery Layout

98% Service Level ~20/meals per day delivered after 45 minutes

88.4 % Utilization 114 hours/day busy (129 total) Schedule 7

Time Staff Count

7am-8am 8

8am-9am 13

9am-10am 12

10am-11am 7

11am-12pm 8

12pm-1pm 13

1pm-2pm 11

2pm-3pm 10

3pm-4pm 8

4-pm-5pm 7

5pm-6pm 10

6pm-7pm 12

7pm-8pm 10

8pm-7am 0 * Schedule 6 was eliminated from comparison because it performed

comparable to Schedule 7, however the utilization was slightly lower

and it used additional scheduled work hours | 26




Conclusions and Recommendations

• Six zone facility layout allows for best service with least

compromise in employee utilization

• Best found schedule requires 129 hours scheduled for

delivery/pickup services per day • Break times not included in schedule

Zone 1 2 3 4 5 6

Units

AGP4 AGP5 GP02 BP05 WLL1 HFAM6

AICU4 AICU5 SCU3 BP06 CHM2 HFAM7

SCU4 ICS5 SCU5 BP07 CHM3 HFAM8

BP08

| 27




Final Thoughts

• We chose to use Simul8 due to the ease of use, system

performance, and low-cost

• The simulation model has been great at helping us gain

stakeholder buy-in from various groups

• Allows us to model test of change before implementing in actual

system

• We have used a variety of resources to help us with our

model

• Consultants helped with initial build and continue to help with

difficult problems on an ad-hoc basis

• Students have developed pieces of the model

• Process engineering team provides in-house support

| 28




Final Thoughts

Keys to Success

• Involve the process stakeholders

• Good communication with the management and staff of the

processes you are modeling is essential.

• Take time to fully understand the system

• Do the job, or shadow the job, to truly understand the processes.

• Gather accurate data

• Don’t always just trust system to provide good data, you may

need to manually collect the data yourself.

• Build and test iteratively

• Build the model in small pieces and constantly test it to make

sure your model is performing how you desire.

| 29




THANK YOU FOR ATTENDING!

WHO HAS QUESTIONS?

Seth Hostetler

Geisinger Health System

[email protected]

570-214-7029 30 |

simul8 user group - geisinger case study - balancing patient satisfaction with staff efficiency

Business

new system

proposed system phase

new food services system

forthcoming system changes5

changing system parameters

efficiency engineers

food delivery

logistics of meal delivery