This document proposes a queueing prioritization algorithm for patient arrivals at the Dana-Farber Cancer Institute's lab services. Currently, lab services experiences challenges with varying patient arrival patterns and volumes, leading to long wait times for on-time patients and missed appointments for late patients. The proposal recommends building an Arena simulation model using one week of historical patient arrival data to analyze different queueing strategies. It suggests prioritizing patients based on how late they arrive, with lower priorities for later patients. Designating some nurses and phlebotomists to treat late patients could minimize impact on on-time patients. The model will evaluate strategies based on key metrics like wait and treatment times to determine the best approach for handling late arrivals

1. Lab Services Queueing Prioritization
Dana-Farber Cancer Institute
WORCESTER POLYTECHNIC INSTITUTE
OIE 3460 B-TERM 2014
Victor Almeida de Oliveira
Carolina Baziewicz
Ashley Downer
Jiacong Xu

2. Abstract
This proposal, prepared for the Dana-Farber Cancer Institute (DFCI) and Worcester
Polytechnic Institute (WPI), will recommend an algorithm for patient queueing prioritization
at DFCI lab services. We will use both historical data collected by DFCI and Arena Simulation
to create a model representing the system. We will determine the best method for handling late
arrivals without impacting on-time patients.
Table of Contents
Abstract.......................................................................................................................................2
Problem Statement....................................................................................................................... 3
Assumptions ................................................................................................................................ 3
Logical Model............................................................................................................................... 3
Process Flow Model.................................................................................................................. 3
Arena Model ............................................................................................................................ 5
Results and Discussion.................................................................................................................. 7
Model Limitations and Future Suggestions..................................................................................... 7
Managerial Recommendations......................................................................................................7
Appendices ..................................................................................................................................9
1. Proposal ............................................................................................................................... 9
2. Transient Period(Output Analyzer) ............................................ Error! Bookmark not defined.
3. Run Length ......................................................................................................................... 12
4. Number of Replications (CI, Output Analyzer)....................................................................... 12
5. Arrivals............................................................................................................................... 12
6. Statistical Output ................................................................................................................ 13
7. Detailed Analysis (summary of statistical output).................................................................. 16
8. Experimentation for Improvement....................................................................................... 17
9. Nurse Staffing Schedule....................................................................................................... 17
10. Phlebotomist Staffing Schedule.......................................................................................... 18
12. PatientVolume Table ........................................................................................................ 18

3. Problem Statement
At DFCI, lab services is the first stop for every patient being treated, preceding all other
appointments the patients have scheduled for the day. The efficiency of lab services therefore
directly affects the procession of the rest of the services provided by DFCI. Currently, lab
services is experiencing difficulties in patient queueing prioritization. Daily volume has grown,
especially during peak hours in the morning and early afternoon, and the varying distributions
of patient arrival is presenting prioritization challenges. Specifically, patients arriving both on
time and in advance are experiencing long waits, and patients arriving late are subject to
missing proceeding appointments. The goal of this project is to determine the best method for
handling late arrivals at DFCI without impacting the on-time patients by establishing a patient
queueing algorithm.
Assumptions
Several assumptions were made during the creation of this model:
1. All processing times, including both nurse and patient blood-draw time, follow a
normal distribution. We made this assumption because we were given only two
parameters, average and standard deviation, regarding treatment time.
2. There are 19 nurse chairs and 11 phlebotomist chairs at DFCI.
3. The operating schedule is from 6:30am – 5:00pm. Nurse and phlebotomist
schedules are distributed throughout this time period, with the first shift beginning
at 6:30am, and the last ending at 5:00pm.
4. The last patient(s) arrive at 4:30pm.
5. All patients are treated on the day they arrive; no patients are leftover in the system
at the beginning of the next day.
6. All nurses and phlebotomists are full-time employees and work 8 hours per day.
7. Patient arrival data from the week of 6/23/14 – 6/27/14 best represents the average
daily and weekly patient volume.
8. Patients are not allowed to choose which nurse they would like to be seen by.
9. Patients take 10 seconds to walk from the check-in desk to the waiting room.
10. Entities both arrive and are treated independently.
11. More than one entity may arrive simultaneously.
12. There is no difference between immuno-compromised patients and normal patients.
This assumption was made because we were not given any data regarding how
many patients are immuno-compromised related to those who are not.
Logical Model
Process Flow Model
Patients arrive at DFCI. At the check-in counter, the patients are assigned a blood draw
provider: nurse or phlebotomist. This creates two separate patient queues. At the time of check-
in, the computer system calculates whether the patient is early, on time, or late. If the patient is
late, the system calculates how late the patient is.
If a patient is on time or early, he/she is automatically assigned a priority of 3 and proceeds to
the waiting room. If a patient is late, a priority is assigned to him/her. The priority number

4. depends on how much time has passed since the patient’s actual appointment time. The later
the patient, the lower the assigned priority. All nurses may treat on-time or early patients. There
are certain nurses and phlebotomists designated to treat the late patients. The patient priority
numbers are used to decide which patient the late-patient providers treat first. Higher priority
is given to patients with higher priority numbers. This process creates two additional patient
queues.
The on-time and early patients are in a FIFO queue. The designated late-patient providers treat
the higher priority patients in order of decreasing priority number. Once the patients are treated,
they exit the system and proceed to their next appointment.
Patients Arrive
at DFCI
Check-in: designate
a provider
Nurse Patients
Is the patient late?
No Yes
Assign a queue
priority
Phlebotomist
Patients
Is the patient late?
No Yes
Assign a queue
priority
Waiting Room
Treatment
Proceed to next
appointment
Figure 1 Logical model

5. Arena Model
PATIENT ARRIVAL: Phlebotomist and nurse patients enter this model as two different types
of entities through two different CREATE modules. The CREATE modules each create one
entity which flows through the READWRITE module. This module reads the data file of actual
patient arrival times during the week of 6/23 – 6/27/14 (see Appendix 4 for reasoning). Once
the file is read, the entity is duplicated. One copy of the entity goes back into the READWRITE
module to read the next entity arrival time, and the other entity proceeds in the model. Once in
the system, each entity passes through an ASSIGN module where it is given a provider attribute
(either nurse or phlebotomist patient).
After being assigned a provider, the entities proceed to the “Check In Counter” PROCESS
modules where patients are queued.
PRIORITIZATION: Proceeding through the model, the patients flow through the “Patient
Arrives Late” DECIDE module, from which 27.58% are determined to be “late” (based on the
percentage of patients who arrived late for their appointments during the chosen week). The
72.42% of patients who are not late proceed to the waiting room, and the late patients proceed
to the late patient prioritization.
The late patients pass through a DECIDE module to determine how late they are. The
percentage of patients in each lateness interval is the average for the given data. There are six
Figure 2 Arena flowchart

6. ASSIGN modules used to assign late patient priorities. In the base case, all patients receive the
same prioritization. In final model, the patient queueing prioritization is determined by how
late the patients are. Considering the patients who are up to one hour late, the later the patient,
the lower the assigned queue priority. Patients who are more than one hour late are not
considered in the model.
Table 1 Prioritizations and percentages of late patients
Lateness interval
(minutes)
Priority
Number
Percentage of
Late Patients
On Time 3 N/A
0 – 10 9 45
10 – 20 8 28
20 – 30 7 10
30 – 40 6 6
40 – 50 5 3
50 – 60 4 8
As shown in the table above, the later the patient, the higher the queue priority. Higher priorities
designate a spot closer to the front of the queue.
QUEUES: There are four different queues for treatment: one nurse patient queue, one late-
nurse patient queue, one phlebotomist patients queue, and one late-phlebotomist patient queue.
On-time patients are routed to the “Nurse or Phlebotomist” DECIDE module. This separates
the on-time patients into two queues based on their provider (nurse or phlebotomist) attribute.
These patients have a priority of 3. Late patients are routed to the “Nurse or Phlebotomist Late”
DECIDE module, from which they are separated into two late queues based on their provider.
These patients have varying priority (as designated in the ASSIGN modules).
SETS: The SET module was used to create four resource pools: two nurse pools and two
phlebotomist pools. One nurse pool contains all nurses, and a second nurse pool contains only
10 nurses designated to treat late patients. Additionally, there is one phlebotomist pool
containing all phlebotomists, and a second phlebotomist pool containing only two designated
to treat late patients.
PROCESS MODULES: Entities are retrieved by their resource (either a nurse or a
phlebotomist), and then their blood draw time is incorporated into the nurse and phlebotomist
PROCESS modules. Additionally, entities who are treated by nurses must then pass through
the nurse documentation PROCESS module. Finally, the entities leave the system.
NURSING SCHEDULE: Some nurses arrive at the beginning of the day, and others arrive later
in the morning. There are seven different nursing schedules. The four nurses who arrive at 9am
work until the end of the day, treating the final patients. Lunch breaks are designated during
the less crowded hours, and they are scheduled between 12 – 2:30pm whenever possible.
Additionally, some breaks were scheduled at times when there were more nurses working than
the number of chairs in lab services (19). The number of nurses working at any given time is
enough to cover the provided data. (See Appendix 9 for detailed nurse schedule)
PHLEBOTOMIST SCHEDULE: There are three separate schedules: one phlebotomist works
from 6:30 – 2:30, the second works from 8 – 4, and the third work from 10 – 5. Breaks are

7. given to phlebotomists throughout the day. (See Appendix 10 for detailed phlebotomist
schedule)
Results and Discussion
The results from the new patient queueing prioritization algorithm were compared to those of
the “base case”. In the base case, the patients are not assigned queue priority numbers. Instead,
all queues are simply first-in-first-out (FIFO). A patient is called for his/her appointment when
a nurse is available and the patient is at the beginning of the queue. There were four key
performance indicators (KPIs) that were considered in comparing the two models:
1. Nurse patient dwell time
2. Nurse patient total time in system
3. Phlebotomist patient dwell time
4. Phlebotomist patient total time in system
After running 200 simulations of both the base case and the new model, the following results
were observed:
Table 2 Key performance indicator results for both the base case and the new model
KPI (minutes) Base Case New Model
Nurse patient dwell time 38.02 4.91
Nurse patient system time 56.00 22.86
Phleb. patient dwell time 1.83 2.70
Phleb. patient system time 7.15 8.03
Model Limitations and Future Suggestions
An important step to guarantee that the algorithm achieves good results in prioritization is
correctly selecting the nurses and phlebotomists which will take care of the late patients. There
should be at least one nurse or phlebotomist available to take care of these patients throughout
the day. We suggest that two out of three phlebotomists, six out of six part-time nurses, and
four out of twelve full-time nurses to be available to take care of the late. Part-time nurses play
a large role in decreasing the average total time of nurse patients in the system. Also, it is
important that the employees who are designated to treat the late patients also take care of on-
time and early patients when necessary. However, whenever a late patient arrives in the system,
he/she receives a higher priority to be treated by those specific nurses and phlebotomists.
Managerial Recommendations
Queueing algorithm:
1) Computer priority system: Upon check-in, the computer system must assign the patients two
attributes: (1) a provider (nurse or phlebotomist) and (2) a queue priority number. The first
attribute divides the patients into two initial groups. The patients are further divided into two
more groups (keeping different providers separate): (1) on-time or early patients and (2) late
patients. The computer calculates the time difference between the patient’s appointment time
and their actual arrival time. If a patient is on time or early, he/she receives a priority number

8. of 3. If a patient is late, he/she is classified into a “lateness increment”. These increments are
10 minutes, and the maximum lateness increment considered is 50 to 60 minutes.
Each lateness increment is associated with a priority number, which is then designated to the
patient. The later a patient is, the lower his/her assigned priority number. The higher the priority
number, the greater the priority given to the patient in the queue. The following priority
numbers should be assigned to the patients:
Table 3 Prioritization of late patients based on their lateness increment
Lateness Increment
(minutes)
Priority
Number
On Time 3
0 – 10 9
10 – 20 8
20 – 30 7
30 – 40 6
40 – 50 5
50 – 60 4
2) Late-patient nurses: Several nurses should be designated to primarily treat late patients.
Though these nurses may also treat on-time patients, they will follow the priority numbers,
treating the patients with the highest priority numbers first. This will allow for the on-time
patients to be seen by the other pool of nurses, and they should therefore be minimally affected
by the late patients.
Further recommendations: We suggest the following for nurse staffing: 12 full-time nurses
and 6 part-time nurses. The part-time nurses should work daily between the hours of 7:30 –
11:30am. This will help to relieve the high volume of patients seen by the full-time nurses
during the morning appointments. We also suggest that there is a maximum of only 3
phlebotomists working at any given time, as our model resulted in under-used phlebotomists.
Finally, we suggest that patients who are more than 60 minutes late be asked to reschedule their
appointments for a different day.

9. Appendices
1. Proposal
Lab Services Queueing Prioritization
Dana-Farber Cancer Institute
WORCESTER POLYTECHNIC INSTITUTE
OIE 3460 B-TERM 2014
Victor Almeida de Oliveira
Carolina Baziewicz
Ashley Downer
Jiacong Xu
Abstract
This proposal, prepared for the Dana-Farber Cancer Institute (DFCI) and Worcester
Polytechnic Institute (WPI), will recommend an algorithm for patient queueing prioritization
at DFCI lab services. We will use both historical data collected by DFCI and Arena Simulation
to create a model representing the system. We will determine the best method for handling late
arrivals without impacting on-time patients.
Table of Contents
Abstract.......................................................................................................................................9
Introduction ............................................................................................................................... 10
Methodology.............................................................................................................................. 10
Tools...................................................................................................................................... 10
Data Collection....................................................................................................................... 10
Simulation.............................................................................................................................. 10

10. Key Performance Indicators..................................................................................................... 11
Potential Solutions .................................................................................................................. 11
Statement of Work...................................................................................................................... 12
Introduction
At DFCI, lab services is the first stop for every patient being treated, preceding all other
appointments the patients have scheduled for the day. The efficiency of lab services therefore
directly affects the procession of the rest of the services provided by DFCI. Currently, lab
services is experiencing difficulties in patient queueing prioritization. Daily volume has grown,
especially during peak hours in the morning and early afternoon, and the varying distributions
of patient arrival is presenting prioritization challenges. Specifically, patients arriving both on
time and in advance are experiencing long waits, and patients arriving late are subject to
missing proceeding appointments. The goal of this project is to determine the best method for
handling late arrivals at DFCI without impacting the on-time patients by establishing a patient
queueing algorithm.
Methodology
Tools
An software that WPI Students have access to is the Arena Academic License. Basically, the
most robust tool we are going to use is Arena itself, which additionally contains some
additional tools, such as the Input Analyzer. The Input Analyzer will help our team in
identifying patterns on patient arrival and processing distributions. Our team is also planning
to utilize Excel Spreadsheets for data organization and, whenever possible, we also want to use
them in order to automate the simulation.
Data Collection
Our project requires a specific data collection. In relation to each patient, for example, our team
is collecting over the period of one week: the appointment time, the actual arrival time (at
check-in), the scheduled provider (nurse or phlebotomist), the scheduled department (which in
this case will always be lab services), the medical record number (MRN) and the scheduled
appointment duration. The appointment duration depends on the patient’s type of treatment. If
the patient is going to see a phlebotomist, the appointment duration will always be 10 minutes,
and if the patient will see a nurse, it probably will take between 15 and 20 minutes. After, this
data will be entered into the Arena Input Analyzer. This sample data will be fit into a probability
distribution, therefore allowing for randomness in our model.
Other information of interest, and not of least importance, will be the nurse and phlebotomist
break schedules. Our team has seen many examples where just a simple modification of
schedule can positively impact the system efficiency. Therefore, our group is also concerned
in testing the various scheduling possibilities, and discovering which one is the best for DFCI.
Simulation
For entities entering the model, we will establish several different CREATE modules. The
differences in interarrival times of these entities will be based on the patient sickness levels.
We will also include ASSIGN modules to establish the difference between phlebotomy patients

11. and nurse patients. In addition to these ASSIGN modules, a DECIDE module will physically
divide the simulated patients into three groups: phlebotomist patients, patients in nurse pool
one and patients in nurse pool two.
Following the DECIDE module, we will have separate PROCESS modules with different
processing times, decided by the blood draw time for phlebotomists and of nurses (the nurse
process time will also include the time for the required documentation). After the PROCESS
modules, we will need two separate DISPOSE modules for nurse patients and phlebotomist
patients, respectively.
In our simulation, we include several variables for entities to calculate the dwell time, which is
important for our model to minimize waiting time for patients. Since one of our potential
solutions is the addition of an assistant for the phlebotomists and nurses to reduce the time
spent between patients, we will need to include a break schedule for that assistant as well.
Additionally, we are considering the need to create the model in a way so that the patients
scheduled to see a phlebotomist may stray from their scheduled provider and see a nurse, if
both the phlebotomist chairs and queue are full and there are vacant nurse chairs at the time.
This would only be possible if the nurse chair utilization is low.
Key Performance Indicators
In order to analyze the performance of the current model and the impact of the changes, the
following performance measures will be relevant in the development of our project. The nurse
and phlebotomist chair utilization is essential to measure the idle and busy time of DFCI’s
resources. A higher utilization of the resources generally indicate a lower wasted cost to the
company, but could also imply in higher queue length. Therefore, it will also be important to
collect and analyze the average and maximum queue times and the average total number of the
patients in the system to balance this trade-off with utilization and queue length and times.
Additionally, the total number of patients seen by lab services, and the total number of patients
per resource (nurses and phlebotomists) are fundamental performance measures to identify
bottlenecks and suggest improvements backed by these indicators. Finally, the average and
maximum dwell time will be important indicators in analyzing the efficiency and the behavior
of the patient arrival process.
Potential Solutions
Our team has come up with several potential solutions of queueing algorithms at DFCI. To
reduce, or possibly eliminate, the impact of late arrivals on on-time patients, we think it could
be beneficial to designate certain nurses to take the late-arrival patients. This way, the late-
arrivals are not interrupting the scheduled appointments. We would also like to simulate
multiple operational changes to potentially reduce the total wait time of patients. The first
solution is hiring an office assistant to bring the patients to the nurse’s chair while he or she is
documenting. This could reduce the nurse process time. A second potential solution
is converting some of the phlebotomy chairs into nurse chairs (or vice versa) if one of those
resources is underutilized and the other has a typically long average queue time. A third
possible solution is eliminating the immuno-compromised waiting room and using the space
for additional blood draw chairs (most likely nurse chairs, depending on the current utilization
of both the nurse and the phlebotomist chairs).

12. We also came up with several solutions to convenience patients while they are waiting to get
their blood drawn. These solutions involve changing the purpose of the immuno-compromised
room. We believe DFCI could either 1) reduce the room size to increase in order to add more
chairs to the waiting room 2) completely eliminate the immuno-compromised room and use
the space as an addition to the current waiting room or 3) better educate immuno-compromised
patients upon check-in so that the room is better utilized, freeing up some space in the normal
waiting room. We believe that we may be able to combine several of our solutions to further
improve the patient queueing of lab services at DFCI.
We are aware of the growing demand for services by DFCI. Therefore we realize that our
solutions going forward may not always be effective for this organization, as the needs will
change.
Statement of Work
1. Receive data from DFCI.
2. Organize and gain an understanding of the data.
3. Establish concrete solutions, and draw out schematics of the possible flowcharts to
simulate these ideas.
4. Create a flowchart model in Arena for each of the potential solutions (including any
combinations of solutions).
5. For patient arrival, interarrival and processing times, enter data into the Arena Input
Analyzer to fit it to a reasonable distribution.
6. Use these distributions as expressions for entity arrival, interarrival and processing
times in the model.
7. Establish a basis for comparing the results potential solutions.
8. Run each model and collect the necessary key performance indicators.
9. Compare the different solutions and decide which is the best algorithm for DFCI lab
services patient queueing.
10. Validate the model, and rework as necessary.
2. Run Length
The designed model represents Monday – Friday of one week at DFCI. Considering each day
to be 11 hours (from 6:30am – 5:00pm), the total run length of the simulation is 55 hours.
3. Number of Replications (CI, Output Analyzer)
Initially, 200 replications of the simulation were run. The key parameters used to determine the
necessary number of replications were the average nurse and phlebotomist patient dwell times
as well as the average system times. From the initial number of replications, and considering
all four KPIs, the widest confidence interval was 0.06 minutes. Because this confidence interval
was so low, it was determined that 200 replications were sufficient. Additionally, it was
confirmed in Arena Output Analyzer that differences between all of the KPIs between the base
case and the new model are statistically significant.
4. Arrivals
Patient arrival data was inserted directly into the model using the READWRITE module.
Phlebotomist and nurse patients enter this model as two different types of entities through two

13. different CREATE modules. The CREATE modules each create one entity which flows
through the READWRITE module. This module reads the data file of actual patient arrival
times during the week of 6/23/14 – 6/27/14. Once the file is read, the entity is duplicated. One
copy of the entity goes back into the READWRITE module to read the next entity arrival time,
and the other entity proceeds in the model. Once in the system, each entity passes through an
ASSIGN module where it is given a provider attribute (either nurse or phlebotomist patient).
The total number of nurse and phlebotomist patients for each day of the week was calculated
for each week in the given data file. From the total amounts and the total number of weeks in
the data, the average number of nurse and phlebotomist patients arriving on each day of the
week was calculated. The week of 6/23/14 to 6/27/14 was chosen because the average number
of patients for each day most closely matched the average of all data. (See Appendix 12 for
detailed table)
5. Statistical Output
All tests in Arena Output Analyzer were considered by using Base Case – New Model.
Figure 3 Arena Output Analyzer results for nurse patient dwell time

14. Figure 4 Arena Output Analyzer for phlebotomist patient dwell time

15. Figure 5 Arena Output Analyzer for nurse patient system time

16. Figure 6 Arena Output Analyzer for phlebotomist patient system time
6. Detailed Analysis (summary of statistical output)
Arena Output Analyzer was used to determine if the differences in the KPIs between the base
case and the new model were statistically significant. All four KPIs were determined to have
statistically significant differences.

17. 7. Experimentation for Improvement
8. Nurse Staffing Schedule
Table 4 Nurse staffing schedule
Table 5 Nurse patients arrival schedule
As we could see from the arrival schedule of the nurse patients, between 6:30am and 11:30am,
the number of patients is higher than during the lunch period (the middle of day). From 11:30
am – 2:00 pm, the average number of patients iss around 8. From 2pm until the end of the day,
the average number of incoming patients is 2. Based on this patient arrival data schedule, and
assuming each nurse works 8 hours a day, we created three types of working schedules to cover
the whole operation time (6am – 5pm). Two 30-min breaks are offered for each nurse during
his/her shift. These breaks were scheduled to interfere minimally with peak hours.
When observing the results of our updated schedule, we discovered that during peak hours,
some patients were waiting extensively in the nursing queue. To lower this wait time, several
part-time nurses were added to treat patients during the morning.

18. 9. Phlebotomist Staffing Schedule
Table 6 Phlebotomist staffing schedule
Three types of phlebotomist schedules were created to cover the operation time of DFCI.
During experimentation, we found that increasing the number of phlebotomists did not have a
large effect on the phlebotomist patient wait times. We therefore reduced the initial number of
phlebotomists to three. Each phlebotomist covers a different time block. (This slightly
increased the total time in system for the patients, but it was determined that this is worth the
reduction of total employee cost.)
10. Patient Volume Table
Table 7 Patient volume for the given data