Discrete Event Simulation of

Outpatient Flow in a Phlebotomy Clinic

Ajaay Chandrasekaran

May 22, 2016

Agenda

• The Team

• UMHS Comprehensive Cancer Center

• Phlebotomy

– Overview

– Simulation Design

– Process Flow Scenarios

• Future Steps

2

The Team

3

Hassan Abbas Nursing Student

Jeremy Castaing, PhD Candidate Industrial and Operations Engineering

Chhavi Chaudhry Industrial and Operations Engineering

Amy Cohn, PhD Associate Director, CHEPS

Diane Drago Patient & Family Advisory Board (PFAB)

Marian Grace Boxer, MD Professor, Internal Med., Hematology/Oncology

Corinne Hardecki, RN Clinical Care Coordinator, Infusion

Madalina Jiga Nursing Student

John Li Industrial and Operations Engineering

Brittany Lopez Industrial and Operations Engineering

Pamela Martinez Villarreal, MSE Industrial and Operations Engineering

Jennifer Mathie Supervisor, Department of Pathology

Jonathon McCormick Industrial and Operations Engineering

Carol McMahon, RN Nurse Supervisor, Infusion

Harry Neusius Manager, Department of Pathology

Elizabeth Olin Industrial and Operations Engineering

Donald Richardson, PhD Pre-Candidate Industrial and Operations Engineering

Matthew Rouhana, MSE Industrial and Operations Engineering

Stephanie See, RN Nursing Graduate

Renee Stoklosa Associate Supervisor, Department of Pathology

Brooke Szymanski, RN Nursing Graduate

Irene Turkewycz, RN Nurse Manager, Infusion

Carolina Typaldos, MHSA Operations Manager, Infusion

Alon Zadok Weizer, MD, MS Medical Director, UMCCC

Jonathan Zhou Pre-Medical Student

The Team (Cont.)

4

– Home to 17 multidisciplinary and 10 specialty clinics, which are organized by cancer type

– In 2015, over 50% of outpatient visits in the UMCCC

resulted in chemotherapy infusion treatments: • 97,147 outpatient visits • 58,419 infusion treatments

– High outpatient demand for infusion causes:

• increased patient waiting times • overworked staff

Source:

U of M Comprehensive Cancer Center (2016)

http://www.mcancer.org

5

UMHS Comprehensive Cancer Center

Infusion Overview

6

Patient Flow

Information Flow

Material Flow

Phlebotomy Patient

Arrives Clinic Infusion

Patient

Discharged

Infusion Overview

Lab

Processing

Phlebotomy Patient

Arrives Clinic

Pharmacy

Infusion Patient

Discharged

Patient Flow

Information Flow

Material Flow 7

Potential Delays at Infusion

8

• Patient is late; no infusion chairs are available

• Infusion drugs are unavailable

Infusion

DELAY

DELAY

Pharmacy

DELAY

Patient Flow

Information Flow

Material Flow

Potential Delays at Clinic

9

Clinic DELAY

DELAY

DELAY

Patient Flow

Information Flow

Material Flow

• Patient is late; clinicians are meeting other patients

• Patient blood draw results are not ready for clinician

• Drug can’t be mixed until approval of patient’s infusion

Enter: Phlebotomy

10

Patient Flow

Information Flow

Material Flow

Phlebotomy DELAY

DELAY

DELAY

Causes for delays:

• Patient is late by their own volition

• The phlebotomy process itself

DELAY

Problem

Lab

Processing

Phlebotomy Patient

Arrives Clinic

Pharmacy

Infusion Patient

Discharged

DELAY

DELAY

DELAY

DELAY

DELAY

DELAY

DELAY

DELAY

11

Delays in phlebotomy can ripple through the system

Patient Flow

Information Flow

Material Flow

Our Goal

12

Reduce patient waiting times at

phlebotomy to alleviate overarching

system delays

Overview: The Phlebotomy Clinic

Waiting Room

Chair 1 Chair 2

Batch AreaP

neu

mat

ic

Tub

e Sy

stem

Chair 3

Chair 4

Chair 5

Chair 6

Chair 7

Chair 8

Patient Bathroom

StaffRoom Chair 9

Office

Ch

eck-

In 1

Che

ck-I

n 2

Che

ck-I

n 3

Gre

eter

Sink

Sink

Patient Bathroom

13

Waiting Room

Chair 1 Chair 2

Batch AreaP

neu

mat

ic

Tub

e Sy

stem

Chair 3

Chair 4

Chair 5

Chair 6

Chair 7

Chair 8

Patient Bathroom

StaffRoom Chair 9

Office

Ch

eck-

In 1

Che

ck-I

n 2

Che

ck-I

n 3

Gre

eter

Sink

Sink

Patient Bathroom

Overview: The Phlebotomy Clinic

14

Waiting Room

Chair 1 Chair 2

Batch AreaP

neu

mat

ic

Tub

e Sy

stem

Chair 3

Chair 4

Chair 5

Chair 6

Chair 7

Chair 8

Patient Bathroom

StaffRoom Chair 9

Office

Ch

eck-

In 1

Che

ck-I

n 2

Che

ck-I

n 3

Gre

eter

Sink

Sink

Patient Bathroom

Overview: The Phlebotomy Clinic

15

Waiting Room

Chair 1 Chair 2

Batch AreaP

neu

mat

ic

Tub

e Sy

stem

Chair 3

Chair 4

Chair 5

Chair 6

Chair 7

Chair 8

Patient Bathroom

StaffRoom Chair 9

Office

Ch

eck-

In 1

Che

ck-I

n 2

Che

ck-I

n 3

Gre

eter

Sink

Sink

Patient Bathroom

Overview: The Phlebotomy Clinic

16

Waiting Room

Chair 1 Chair 2

Batch AreaP

neu

mat

ic

Tub

e Sy

stem

Chair 3

Chair 4

Chair 5

Chair 6

Chair 7

Chair 8

Patient Bathroom

StaffRoom Chair 9

Office

Ch

eck-

In 1

Che

ck-I

n 2

Che

ck-I

n 3

Gre

eter

Sink

Sink

Patient Bathroom

Overview: The Phlebotomy Clinic

17

Waiting Room

Chair 1 Chair 2

Batch AreaP

neu

mat

ic

Tub

e Sy

stem

Chair 3

Chair 4

Chair 5

Chair 6

Chair 7

Chair 8

Patient Bathroom

StaffRoom Chair 9

Office

Ch

eck-

In 1

Che

ck-I

n 2

Che

ck-I

n 3

Gre

eter

Sink

Sink

Patient Bathroom

Overview: The Phlebotomy Clinic

18

Waiting Room

Chair 1 Chair 2

Batch AreaP

neu

mat

ic

Tub

e Sy

stem

Chair 3

Chair 4

Chair 5

Chair 6

Chair 7

Chair 8

Patient Bathroom

StaffRoom Chair 9

Office

Ch

eck-

In 1

Che

ck-I

n 2

Che

ck-I

n 3

Gre

eter

Sink

Sink

Patient Bathroom

Overview: The Phlebotomy Clinic

19

Waiting Room

Chair 1 Chair 2

Batch AreaP

neu

mat

ic

Tub

e Sy

stem

Chair 3

Chair 4

Chair 5

Chair 6

Chair 7

Chair 8

Patient Bathroom

StaffRoom Chair 9

Office

Ch

eck-

In 1

Che

ck-I

n 2

Che

ck-I

n 3

Gre

eter

Sink

Sink

Patient Bathroom

Overview: The Phlebotomy Clinic

20

Overview: The Phlebotomy Clinic

Lab

Processing

Phlebotomy Patient

Arrives Clinic

Pharmacy

Infusion Patient

Discharged

Patient Flow

Information Flow

Material Flow 21

Patient Flow at Phlebotomy

22

Phlebotomy Patient

Arrives Clinic

Check-In Blood-Draw

Wait

Lab

Processing

Normal Patient:

~2 min, 48 sec

Extended Patient:

~7 min, 42 sec

Vein Draw:

~5 min, 7 sec

Port Draw:

~13 min, 17 sec

Patient Flow

Information Flow

Material Flow

Approach: Simulation

– Successfully utilized to expedite patient flow in clinics

throughout the country

– Users can: • manipulate input parameters and observe effect on

various metrics

• assess the impact of policy changes before actual

implementation

23

Implementation

– Developed in C++ • Directly usable by the Cancer Center management

• Adaptable for implementation of new scenario

24

Discrete Event Simulation Model

– Maintain a growing queue of events that occur

throughout the day, sorted by time of occurrence

– Three (3) main event types, each corresponding to

an availability queue:

• Patient Available for Check-In

• Patient Available for Blood Draw

• Phlebotomist Available

25

Simulation Design

26

Event Type Participant ID Time

PatientAvailCI 3948 7:03:42

PatientAvailCI 2084 7:06:12

PhlebAvail 0962 7:15:00

PatientAvailCI 5541 7:16:09

PatientAvailCI 8737 7:20:33

Event Queue

Participant ID Time

PatientAvailCI

Queue Participant ID Time

PhlebAvail

Queue

Clock

Simulation Design

27

Event Type Participant ID Time

PatientAvailCI 3948 7:03:42

PatientAvailCI 2084 7:06:12

PhlebAvail 0962 7:15:00

PatientAvailCI 5541 7:16:09

PatientAvailCI 8737 7:20:33

Event Queue

Participant ID Time

PatientAvailCI

Queue Participant ID Time

PhlebAvail

Queue

Clock

Simulation Design

28

Event Type Participant ID Time

PatientAvailCI 2084 7:06:12

PhlebAvail 0962 7:15:00

PatientAvailCI 5541 7:16:09

PatientAvailCI 8737 7:20:33

Event Queue

Participant ID Time

3948 7:03:42

PatientAvailCI

Queue Participant ID Time

PhlebAvail

Queue

Clock

Simulation Design

29

Event Type Participant ID Time

PatientAvailCI 2084 7:06:12

PhlebAvail 0962 7:15:00

PatientAvailCI 5541 7:16:09

PatientAvailCI 8737 7:20:33

Event Queue

Participant ID Time

3948 7:03:42

PatientAvailCI

Queue Participant ID Time

PhlebAvail

Queue

Clock

Simulation Design

30

Event Type Participant ID Time

PhlebAvail 0962 7:15:00

PatientAvailCI 5541 7:16:09

PatientAvailCI 8737 7:20:33

Event Queue

Participant ID Time

3948 7:03:42

2084 7:06:12

PatientAvailCI

Queue Participant ID Time

PhlebAvail

Queue

Clock

Simulation Design

31

Event Type Participant ID Time

PhlebAvail 0962 7:15:00

PatientAvailCI 5541 7:16:09

PatientAvailCI 8737 7:20:33

Event Queue

Participant ID Time

3948 7:03:42

2084 7:06:12

PatientAvailCI

Queue Participant ID Time

PhlebAvail

Queue

Clock

Simulation Design

32

Event Type Participant ID Time

PhlebAvail 0962 7:15:00

PatientAvailCI 5541 7:16:09

PatientAvailCI 8737 7:20:33

Event Queue

Participant ID Time

3948 7:03:42

2084 7:06:12

PatientAvailCI

Queue Participant ID Time

PhlebAvail

Queue

Generate Service Time:

2 minutes 51 seconds

Clock

Simulation Design

33

Event Type Participant ID Time

PatientAvailCI 5541 7:16:09

PatientAvailCI 8737 7:20:33

PatientAvailBD 3948 7:17:51

PhlebAvail 0962 7:17:51

Event Queue

Participant ID Time

2084 7:06:12

PatientAvailCI

Queue Participant ID Time

PhlebAvail

Queue

Clock

Initialize Event Queue

Simulate Day

Store Metrics

Initialize Patient Arrival Rates

Initialize Phlebotomist

Schedules

Initialize Additional Parameters

Report Patient Metrics

Generate Summary Statistics

Generate Graphs

Simulation Procedure Run Engine Read Simulation

Parameters Report Metrics X Iterations

34

Base Case

35

0

5

10

15

20

6:30 8:30 10:30 12:30 14:30 16:30

Pat

ien

ts/h

ou

r

Time of Day

AVERAGE PATIENT ARRIVAL RATE

Arrival Rate Data

36

Staffing Model

1200 1230 1300 1330 1400 1430 1500 1530 1600 1630 1700 1730

2 2 2 2 2 2 2 2 2 1 1 1

2 2 2 2 1 1 2 1

7 7 7 7 7 7 7 2 1 1 2 2

645 700 730 800 830 900 930 1000 1030 1100 1130

Check-In 1 2 3 3 3 3 2 2 2 2 2

Break/Lunch 1 2 2 2 2 2 2

Available to Draw

0 5 6 6 7 6 7 7 7 7 7

37

Service Time Data

Mean Std. Dev Proportion of

Patients

Check-In Duration (Minutes)

Extended* 7.71 4.12 9%

Regular 2.81 0.94 91%

Mix 3.25 1.53

Blood Draw Duration (Minutes)

Port 13.28 4.64 28%

Vein 5.11 3.75 72%

Mix 7.40 4.02

*Extended defined as check-in that takes > 5 minutes. 38

Base Case Results

39

0.03

0.13

0.37

0.46

0.01 0.00 0.00 0.00 0%

10%

20%

30%

40%

50%

15 20 25 30 35 40 45 More

Pe

rce

nta

ge

Minutes

Maximum Total Wait Times N=300 iterations

15

4

1 2

4

2 3

1 0 0

1 0

0

5

10

15

20

6:00 AM 8:00 AM 10:00 AM 12:00 PM 2:00 PM 4:00 PM

Wai

t Ti

me

(M

inu

tes)

Time of Day

Mean Total Wait Times

Base Case Results (Cont.)

40

Preliminary Validation

• Simulation run under current configuration

of clinic

• Results used for preliminary model validation with the hospital management

Check In Wait Times Draw Wait Times Total Wait Times mean median sd mean median sd mean median sd

0:02:32 0:00:22 0:04:08 0:00:16 0:00:00 0:01:18 0:02:49 0:02:49 0:04:37

Preliminary Validation

• Simulation run under current configuration

of clinic

• Results used for preliminary model validation with the hospital management

Check In Wait Times Draw Wait Times Total Wait Times mean median sd mean median sd mean median sd

0:02:32 0:00:22 0:04:08 0:00:16 0:00:00 0:01:18 0:02:49 0:02:49 0:04:37

What if…?

43

Possible Process Flow Scenarios

• Staffing Model Change

• Arrival Rate Increase

• Patient Population Change

44

What if…? One Draw to CI!

1200 1230 1300 1330 1400 1430 1500 1530 1600 1630 1700 1730

3 3 2 2 3 3 3 2 2 1 1 1

2 2 2 2 1 1 2 1

6 6 6 7 6 6 6 2 1 1 2 2

645 700 730 800 830 900 930 1000 1030 1100 1130

Check-In 1 3 4 4 4 4 2 2 3 3 3

Break/Lunch 1 2 2 2 2 2 2

Available to Draw

0 4 5 5 6 5 6 6 6 6 6

45

0

5

10

15

20

6:00 AM 8:00 AM 10:00 AM 12:00 PM 2:00 PM 4:00 PM

Wai

t Ti

me

(M

inu

tes)

Time of Day

Mean Total Wait Times n=300 iterations

Base Case

New Case

Comparison (Cont.)

46

Total Wait Times mean sd

New Case 0:02:12 0:04:25 Base Case 0:02:49 0:04:37

Comparison (Cont.)

Check In Wait Times Draw Wait Times

mean sd mean sd

New Case 0:01:16 0:03:07 0:00:56 0:02:34

Base Case 0:02:32 0:04:08 0:00:16 0:01:18

47

Summary

48

– Simulation may be adjusted to account

for vary intricacies of the clinic

–Hospital management can find optimal

input parameters for improving patient

wait times

Future Steps

49

–Continued improvement towards

representing current state, which

involves: • Accurate Service Time Distributions

• Non-instantaneous Service Transitions

• Additional Roles

–Obtain better arrival rate data for

analysis and formal validation

Acknowledgements

50

–U of M Center for Healthcare

Engineering and Patient Safety

–The Seth Bonder Foundation

–The Doctors Company Foundation

–UMHS Comprehensive Cancer Center

–U of M College of Engineering SURE

Program

QUESTIONS?

Thank you!

51

CONTACT INFORMATION:

Ajaay Chandrasekaran – ajaay@umich.edu

Amy Cohn – amycohn@umich.edu

Phlebotomy Staff Schedule

52

Appendix

0

5

10

15

20

25

Average Weekly Arrivals

Monday Tuesday Wednesday Thursday Friday Average

53

Simulation Overview

Main

1 Read Simulation Parameters

2 Run Engine

3 Report Metrics

Initialize internal simulation parameters with inputs from user-provided text files

Simulate day for multiple iterations

Output user-friendly metrics

54

Read Simulation Parameters

Read Simulation Parameters

1.1 Initialize Patient Arrival Rates

1.2 Initialize Phlebotomist Schedules

1.3 Initialize Additional Parameters

55

Run Engine

Run Engine

Simulate Iteration

• 2.1 Initialize Event Queue

• 2.2 Simulate

• 2.3 Store Metrics

X Iterations

56

Patient Arrival Rates

57

Phlebotomist Schedules

58

Additional Parameters

59

Report Metrics

60

Appendix

• Phlebotomy – 253 patients per day

• Clinic (7 Total) – 311 patients per day

• Infusion:

– Total of 51 infusion chairs

– 123 patients per day

– 20% of infusion appointments are coupled

61 61

Alternative Workflow A

62

Workflow A

Description Split current check-in process in two

Pros More patient interaction at check-in and no interruptions at order consolidation

Cons Additional space, change in layout, more hand-offs

Patient

Arrives Check-In

Lab Orders

Review

Blood

Draw

62

Alternative Workflow B

63

Workflow B

Description Order review and blood draw in the same area

Pros 2x verification and interaction with patient, fewer hand-offs

Cons Additional space and equipment/computers

Patient

Arrives Check-In

Lab Order and

Blood draw

63

Appendix

64

Infusion 13%

Lab Before Clinic 38% Lab

After Clinic 11%

Clinic & Infusion

20%

None 16%

Lab Before and After

Clinic 2%

Cancer Center Lab Patient Population Data Source: May & June 2014 Appointment Data (10,850 patients)

64

Appendix

65

• Phlebotomy Staff Schedule

Arrival Rate Data

66

0.00

5.00

10.00

15.00

20.00

25.00

6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30

Pat

ien

ts/h

ou

r

Time of Day

AVERAGE WEEKLY ARRIVALS

Monday

Arrival Rate Data

67

0.00

5.00

10.00

15.00

20.00

25.00

6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30

Pat

ien

ts/h

ou

r

Time of Day

AVERAGE WEEKLY ARRIVALS

Monday Tuesday

Arrival Rate Data

68

0.00

5.00

10.00

15.00

20.00

25.00

6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30

Pat

ien

ts/h

ou

r

Time of Day

AVERAGE WEEKLY ARRIVALS

Monday Tuesday Wednesday

Arrival Rate Data

69

0.00

5.00

10.00

15.00

20.00

25.00

6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30

Pat

ien

ts/h

ou

r

Time of Day

AVERAGE WEEKLY ARRIVALS

Monday Tuesday Wednesday Thursday

Arrival Rate Data

70

0.00

5.00

10.00

15.00

20.00

25.00

6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30

Pat

ien

ts/h

ou

r

Time of Day

AVERAGE WEEKLY ARRIVALS

Monday Tuesday Wednesday Thursday Friday